20 Acta Electrotechnica et Informatica Vol 13 No 4 2013 20ndash25 DOI 1015546aeei-2013-0044

A MOBILE BROWSER PROTOTYPE FOR SEMANTIC INFORMATION SYSTEMS

Gergo GOMBOSlowast Tamas MATUSZKAlowastlowast Balazs PINCZELlowast Gabor RACZlowast Attila KISSlowast Tamas GAIZERlowastlowastlowastlowastDepartment of Information Systems Faculty of Informatics Eotvos Lorand University Pazmany Peter setany 1C 1117 Budapest

Hungary e-mail ggombos tomintt vic gabee33 kissinfeltehulowastlowastInter-University Centre for Telecommunications and Informatics Debrecen Hungary

lowastlowastlowastRegens Zrt Budapest Hungary e-mail gaizertamasregenshu

ABSTRACTNowadays companies have to handle large amounts of data every day (eg employee data reports statements) These data are

usually unstructured or semi-structured they do not have a consistent format since they come from various sources As a solution theSemantic Web provides a common data format which helps integrate the heterogeneous data In the industry in order to be efficientand to remain competitive it is crucial to have quick easy and reliable access to your data from anywhere In this paper we introducea client-server system which enables browsing of semantic data with smartphones and tablets running Android operating system Theusers can access their data using predefined views These views determine which parts of the data a user can see and how to presentthem in the client application Each user can have different access privileges to different views so each employee can have a level ofaccess suitable to his status

Keywords Semantic Web Ontology Mobile Application ERP System

1 INTRODUCTION

During their operation the companies have to manage alarge number of documents which are usually plain textsCVs project reports websites e-mails feeds blog postsgeographic data etc In addition we can access more andmore public knowledge bases on the Internet for exam-ple encyclopedias books dictionaries databases in field ofgovernment touristic multimedia content and social net-works Furthermore there are numerous databases whichcontain theoretical and experimental results of various sci-entific experiments in the field of computer science biol-ogy chemistry etc There is a quite complex collectionof these kinds of data maintained by the Linked Data Com-munity [3] This collection contains datasets and ontologieswhich are at least 1000 lines in length and which containlinks to each other We could get relevant answers fromsome databases with the help of search engines using well-defined questions However from the perspective of com-panies it means much more potential advantages if they canintegrate their structured and unstructured data and theyare capable to extract the information from textual contentsThe Semantic Web [2] provides a standardized data modeland a corresponding query language for this purpose Withthese technologies it is possible to integrate and query datawhich come from various sources in a standard way Be-cause of these features the industrial interest is increasingmore and more

In this paper we present a system we designed and im-plemented which is able to access filter and display se-mantic datasets The system uses a client-server architec-ture where the clients can be smartphones tablets and otherdevices which are running Android operating system Thisis an important feature for the companies because it is cru-cial to have quick easy and reliable access to their datafrom anywhere in order to be efficient and to remain com-petitive Furthermore the system is capable of managingmultiple users and controlling which parts of the data areaccessible for a given user and how to format them

The structure of the paper is as follows In Section

2 we outline the preliminary definitions Thereafter wepresent the architecture of the system in Section 3 In Sec-tion 4 we describe the main functions of the system indetails Then we compare our solution with some sim-ilar applications in Section 5 After that we give twoindustrial-like examples in Section 6 to demonstrate theusability of our application Finally we summarize our ex-periences with the system in Section 7

2 PRELIMINARIES

As we mentioned in the introduction the Semantic Web[2] provides various techniques to manage the data avail-able on the Internet This section gives insight into the ba-sic concepts of Semantic Web that are necessary for under-standing what our system is capable of and how it worksThe main technologies that are used in our system are thefollowing Resource Description Framework (RDF) RDFSchema (RDFS) SPARQL query language Web OntologyLanguage (OWL) In the formal discussion we follow theconcepts and notations introduced in [11]

The Resource Description Framework is a descriptionlanguage where the information is represented by RDFtriples Informally an RDF triple consists of a subject apredicate and an object or alternatively it consists of anentity a property and the value of that property of the de-scribed entity This representation form is similar to natu-ral language sentences For example the sentence rsquoEotvosLorand University is located in Budapestrsquo can be trans-lated into the triple (Eotvos Lorand University locationBudapest) Three kinds of terms are distinguished IRIsrepresent entities (eg httpdbpediaorgresourceELTE)or relations (eg httpdbpediaorgontologylocation) lit-erals can only occur as value of a property blank nodesare the terms that do not represent real world entities theyjust help to construct complex values for example mail ad-dresses which consist of multiple parts such as postal codecity street and number Below is the formal definition ofRDF triples (Definition 21)

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

Acta Electrotechnica et Informatica Vol 13 No 4 2013 21

Definition 21 Let I B and L (IRIs Blank Nodes Lit-erals) be pairwise disjoint sets An RDF triple is a(v1v2v3) isin (I cupB)times Itimes (I cupBcupL) where v1 is the sub-ject v2 is the predicate and v3 is the object A finite set ofRDF triples is called an RDF graph or RDF dataset

The RDF Schema is a data-modeling vocabulary builton the top of RDF for defining concepts properties andconstraints which are essential for organizing the knowl-edge represented by triples The Web Ontology Languagealso enables us to define concept and property hierarchieshowever it is a computational logic-based language There-fore logical constraints and rules can be expressed in orderto verify the consistency of that knowledge or to make im-plicit knowledge explicit The formal definition of an on-tology is presented in Definition 22 based on [17]

Definition 22 A structure O = (CleCPσ) is an ontol-ogy where C and P are two disjoint sets The elements of Cand P are called classes and properties respectively A par-tial order leC on C is called class hierarchy and a functionσ PrarrCtimesC is a signature of a property For a propertyp isin P its domain and its range can be defined in the fol-lowing dom(p) = π1(σ(p)) and range(p) = π2(σ(p))where π is the projection operation Let c1c2 isin C be twoclasses if c1 leC c2 then c1 is a subclass of c2 and c2 is asuperclass of c1

SPARQL is a query language for retrieving and manipu-lating RDF data It is an SQL-like declarative language thequeries are based on pattern matching where the patternsare in the form of triples though they can contain variablesas well Most of the keywords and their meanings are thesame such as SELECT WHERE LIMIT However thereare some new keywords in SPARQL for example OP-TIONAL means optional pattern matching or FILTER thatdefines constraints for the variables Definition 23 givesthe abstract syntax of the filter conditions and Definition 24presents the abstract syntax of the SPARQL expressions

Definition 23 Let V be the set of distinct variables over(I cup B cup L) The variables are distinguished by a ques-tion mark Let X Y isin V be variables and cd isin (Lcup I)be a literal and an IRI constant respectively We definethe filter conditions recursively as follows The X = cX =Y c = d bound(X) isIRI(X) isLiteral(X) andisBlank(X) are atomic filter conditions Thereafter ifR1R2 are filter conditions then notR1 R1 andR2 and R1 orR2are filter conditions as well

Definition 24 A SPARQL expression is built up recur-sively in the following way

1 the triple t isin (I cupV )times (I cupV )times (L cup I cupV ) is aSPARQL expression

2 if Q1Q2 are SPARQL expressions and R is a filterconditionthen Q1 FILTER R Q1 UNION Q2 Q1 OPT Q2 andQ1 AND Q2 are SPARQL expressions as well

The discussion of formal semantics of SPARQL is outof the scope of this paper Set and multiset semantics aredescribed in [11]

3 SYSTEM ARCHITECTURE

As it can be seen on Figure 1 the application uses client-server architecture All resource-intensive operations arecarried out on the server thus clients of any quality canconnect to it even ones with limited CPU or memory re-sources such as smartphones or tablets The clients onlyrequest answers to queries and display the results A clientcan be any devices which are running Android operationsystem Such devices are always on hand and they usuallyhave internet connection The server consists of a middle-ware system and a local database

Fig 1 The architecture of our system

On server-side there is a REST web service whichcommunicates with clients handles the configuration filestransforms the queries according to the requests and for-wards them to local or remote endpoints Furthermorethe server uses an Oracle database with Jena Adapter APIto store the local models It is capable the handle largedatasets The results of queries are sent in form XML backto clients Query results are stored in the local database andsent to the clients in small parts which they can handle

Fig 2 Screens in the client application and transitions betweenthem

A typical scenario is the following The first step is au-thentication which is done with a username and a pass-word Next the middleware loads the userrsquos configurationfile which contains the views which are accessible by himso that he can decide which table view he would like to seeThe middleware sends the table viewrsquos query to either a lo-cal or a remote endpoint depending on where the data forthe view is located The query results are forwarded to theclient dynamically That means only the first few rows aresent and whenever the user gets close to the bottom of thetable while browsing it more rows are sent to the client

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

22 A Mobile Browser Prototype for Semantic Information Systems

This way we can prevent the undesired situation where theclient application would hang until a large table loads Ifthe user filter the table then he can open the filter dialogand construct the filter conditions using the dynamicallybuilt filter form The desired filter conditions are incorpo-rated to the table viewrsquos query and the middleware resendsthis modified query to the endpoint and the results are pro-cessed in a similar way Note that filtering does not requireknowledge of the SPARQL query language [14] the filteroptions can be selected using a graphical interface and theyare translated to SPARQL by the middleware If the userselects an object from the table then the appropriate de-tailed view (which is also defined in the configuration file)appears To do this the middleware needs to send anothermodified query but this time the result is small enough tobe sent to the client in one step

4 SYSTEM FUNCTIONALITY

In this section we describe the main functions of the sys-tem both the client and the server-side components Thedetails of semantic data querying user account handlingviews filters as well as query rewriting are presented

41 Querying semantic data

One of the many advantages of semantic technology isthat we can access data from diverse sources using a uni-fied data model (RDF triples [5]) Furthermore it providesthe SPARQL language which can be used to query seman-tic data These techniques make it possible to create anapplication which can integrate various data sources undera common user interface Data sources can be local [9]or one of the numerous publicly available sources such asDBpedia [4] Geonames [16] etc

Because the clients of the system can be mobile deviceswith limited resources (such as memory or battery life) it isimportant to preprocess the results of the queries It meansthat if the query results are too large they must be split intosmaller fragments and we have to store these fragments ona server and send them to the clients only if it is necessary

42 Handling user accounts

In industrial environment it is essential to handle mul-tiple users and to be able to control which parts of the datacan be seen by the individual users Our system authenti-cates each client with a username and a password Afterconfirming the identity of a user the appropriate configu-ration file will be loaded A configuration file defines theviews that describe the way how a user can see the datasets(see below) Moreover if a query result has been split intopieces because of its size as we mentioned above then thefragments are stored separately for each query of each user

43 Views

One of the main features of our system besides data in-tegration is the management of views Each user has a con-figuration file that is located on the server-side These files

created based on some general schemes then an adminis-trator can be modifies them The configuration file definesthe data accessible for the user and the way it is to be pre-sented There are two types of views the table and thedetailed view

The table view is suitable to define a table of objectsof a given category or class along with their most impor-tant attributes The filter window is available from this typeof view (for further details see the next section) If an ob-ject is selected the appropriate detailed view will be loadedThe detailed view screen is dynamically built up as the filterscreen based on the attribute types In this view additionalattributes are displayed Moreover if one of the attributesdescribe the location of an image or PDF document thatdocument could be displayed as well The semantic repre-sentation allows multiple values to an attribute in the de-tailed view these values will be represented using lists Fig-ure 3 illustrates a table view with the corresponding detailedview

Fig 3 View selection screen with examples for table anddetailed views

44 Filters

From table view screens a special tab is availablewhere you can define filter conditions for the main at-tributes To do this the data type of each attribute mustbe specified in the configuration file These data types canbe one of the followings

bull Text in this case there will be a text area in the filterwindow The value of the attribute must contain thegiven text

bull Enum possible values will be converted to a drop-down list The possible values are collected automat-ically by an auxiliary query

bull Decimal you can define an interval with its lowerand upper bounds If one end of the interval is notspecified that means no bounds

bull Date these are filtered using intervals as well In thiscase bounds can be entered using a date selection di-alog

bull Boolean determines whether the object has a partic-ular property (eg a picture attached to it)

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

Acta Electrotechnica et Informatica Vol 13 No 4 2013 23

After filtering the table only those items remain whichmatch the criteria In Figure 4 you can see two examplesfor filter windows The first one illustrate a date selectionscreen while the second one shows a complex use case withsome attributes related to a person such as name age orexperience level

Fig 4 Filter examples

45 Query rewriting

For each table view its declaration consists of an ini-tial SPARQL query and additional attribute definitionsThat query selects the displayable entities and additional at-tributes which determine the columns of the table on clientside Every attribute has a URI tag a name and a type Thesystem can build the final query from these parameters inthe following way

The names of additional attributes are added to thequery as variables after the SELECT keyword TheWHERE clause will contain the corresponding triplesbased on the attribute declarations in the form (entityltURIgtltname of attributegt) in OPTIONAL groups Incase of filtering the appropriate groups will contain addi-tional triplets based on the filter conditions The Figure 5illustrates the process On left-hand side there is a frag-ment from the configuration file with a table view definitionnamed rsquoCandidatesrsquo The definition contains the SPARQLquery and an attribute description belongs to column namedrsquonamersquo When the client application has to build up the ta-ble view that initial query will be transformed as the right-hand side box shows it

Fig 5 Building a SPARQL query

Detailed views use the initial query of the correspond-ing table view but they define some additional attributesto display in the same way than we do it in case of tableviews with an ltattributegttag In case of detailed viewsthe SPARQL query will be contain an extra condition tofilter data related to the previously chosen entity

5 RELATED WORK

There are some application which have already used theadvantages of semantic technologies In this section wecompare these applications with our system DBpedia Mo-bile [1] is a location-sensitive semantic application whichenables its users to browse linked data on their mobile de-vices by navigating a map The objects on the map are dis-played using icons based on the YAGO categories [10] Byclicking on these icons we can access DBpedia descrip-tions and Flickr [8] photos for the selected object Thisapplication shares many common features with our solu-tion the user can use filters for a search and the resultscan be browsed using a detailed view which displays infor-mation about the object using multiple data sources How-ever there are differences as well which stem from the factthat DBpedia Mobile is an application designed exclusivelyfor one particular data set while our browser is general-purpose and supports arbitrary data sets The most signifi-cant difference is that our main browsing interface is table-based not map-based Moreover in our application the fil-ter conditions cover every attribute of the data set for eachview a filter form is generated automatically where specificfilter conditions can be set for each column In DBPediaMobile however only a limited set of filter conditions areavailable on the form to achieve more complex filteringthe users should use the SPARQL query language

OntoWiki Mobile [7] is an application based on the se-mantic collaboration platform called OntoWiki It allowsits users to browse navigate create and edit semantic dataon-the-fly It focuses on offline editing users can alter theknowledge base without a data connection and their modi-fications will take effect later when a connection is present

The idea of displaying RDF data with views comes fromFresnel [13] It defines a vocabulary which allows the usersto describe the display properties of their data The vocab-ulary uses two basic concepts lenses and formats Lensesdefine the parts of the data which the users would like todisplay and formats define how the selected data shouldappear Selectors for the lenses can be defined using simplerestrictions (eg filtering objects by their type) but morecomplex filters can be expressed with the Fresnel Selec-tor Language (FSL) or the SPARQL query language Avisual browser which implements Fresnel is (for example)IsaViz [12] In our solution we have a different approachto define views the user only needs to specify the parts ofthe data to be displayed as a SPARQL query Formatting isdone automatically based on the data types of the selectedattributes

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

24 A Mobile Browser Prototype for Semantic Information Systems

6 EXPERIMENTS

As we mentioned previously the industryrsquos interesttowards semantic technologies is growing continuouslythanks to the capability to integrate and query data origi-nating from different sources In this section we presenttwo examples where we can use our application The firstexample helps the managers in a company while the sec-ond example illustrates the perspective of truck drivers in atransport company Since our browser was made for gen-eral purposes the range of use is not limited to these twotasks but with these two different uses we would like todemonstrate the diversity of our application

61 HR

At large companies interviewing is typically made bymanagers Those who also deal with many other tasks donot sit permanently in the office so they cannot be informedabout the candidates in time The application we madecan be used for a possible industrial use of a companyrsquosHR and project management support Our application al-lows managers to query the necessary information providedby HR assistants anytime anywhere To help this taskvarious ontologies have been developed for semantic stor-age of the data of the employees trainees candidates orprojects [6] [15]

The views presented above allow for a variety of au-thorized users to view data at different levels Returningto the previous example we can store more data about anapplicant than it is absolutely necessary for the managerThus the data needed for the interview (such as experienceand knowledge) can be filtered beforehand with the helpof the configuration file We can see the process in Figure2 At a larger company there are several managers work-ing each with its own area of responsibility For examplein the software development department such areas are de-signing or testing It is clear that different areas requiredifferent competencies so we can specify initial filters inthe configuration file for these as well Figure 3 illustratesthe selection of a candidate for the tester position

62 Transportation

Another example shows where our application can beused in transportation The drivers receive their waybill thecargo on their truck then they set off to the destination Theperson in office decides what and where to take In order forthe drivers to know where they are to travel they must askit from the office worker It can generate a lot of calls whichcan be very expensive and make a 24-hour dispatch service

necessary Our system can also be a solution to this prob-lem because if the person in the office inserted transporta-tion data in a dataset the drivers would be able to check itanytime on their phones They could see delivered and theupcoming transports too Obviously the current transportcan be found with him so these are not relevant Beforethey arrive to a certain destination they could check whereto go next so they could organize their next day The ap-plication exploits the available public datasets as well Bigcompanies have subsidiaries all over the world The geo-graphical information of the premises of these subsidiariescan be obtained from DBpedia or GeoNames datasets

7 CONCLUSION

In our paper we presented an application which pro-vides opportunity to make use of semantic technologies inthe industry Our main result is a system that makes it pos-sible to browse semantic data sources in personalized wayThe system is simple to configure yet it is general enoughto handle arbitrary semantic datasets Thanks to its client-server architecture the resource-intensive operations can becarried out by the server thus mobile clients with limited re-sources can use the application as well Moreover the clientapplication hides the underlying technological details fromthe user they do not have to formulate SPARQL queriesbecause filtering can be done with the help of easy-to-useforms

We demonstrated the usability of the system with twoenterprise use cases In the first case the application helpsmanagers in finding the right applicant for a job The HRdepartment can record the applicantrsquos data and only thecorresponding manager can access it using a view In thesecond use case truck drivers of a transport company cansee their next freight using their mobile phone or tabletThe application can provide solutions for similar caseswith a suitable configuration file

In the future we would like to extend our system to beable to not just only display the data but edit them as wellWe also aim that data types of attributes could be extractedfrom a corresponding ontology It could simplify the cre-ation and maintenance of configuration files

ACKNOWLEDGEMENT

This work was partially supported by the EuropeanUnion and the European Social Fund through project Fu-turICThu (grant no TAMOP-422C-111KONV-2012-0013) and TAMOP-422C-111KONV-2012-0001 sup-ported by the European Union co-financed by the EuropeanSocial Fund

REFERENCES

[1] BECKER C ndash BIZER C DBpedia Mobile ALocation-Enabled Linked Data Browser World 369No 1 (pp 6-7) (2008)

[2] BERNERS-LEE T ndash HENDLER J ndash LASSILA O

The semantic web Scientific American 284 No 5(pp 28-37) (2001)

[3] BIZER C ndash JENTZSCH A ndash CYGANIAK RState of the LOD Cloud (2011) httpwww4

wiwissfu-berlindelodcloudstate

[4] BIZER C ndash LEHMANN J ndash KOBILAROV G ndashAUER S ndash BECKER C ndash CYGANIAK R ndash HELL-MANN S DBpedia-A crystallization point for the

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

Acta Electrotechnica et Informatica Vol 13 No 4 2013 21

Definition 21 Let I B and L (IRIs Blank Nodes Lit-erals) be pairwise disjoint sets An RDF triple is a(v1v2v3) isin (I cupB)times Itimes (I cupBcupL) where v1 is the sub-ject v2 is the predicate and v3 is the object A finite set ofRDF triples is called an RDF graph or RDF dataset

The RDF Schema is a data-modeling vocabulary builton the top of RDF for defining concepts properties andconstraints which are essential for organizing the knowl-edge represented by triples The Web Ontology Languagealso enables us to define concept and property hierarchieshowever it is a computational logic-based language There-fore logical constraints and rules can be expressed in orderto verify the consistency of that knowledge or to make im-plicit knowledge explicit The formal definition of an on-tology is presented in Definition 22 based on [17]

Definition 22 A structure O = (CleCPσ) is an ontol-ogy where C and P are two disjoint sets The elements of Cand P are called classes and properties respectively A par-tial order leC on C is called class hierarchy and a functionσ PrarrCtimesC is a signature of a property For a propertyp isin P its domain and its range can be defined in the fol-lowing dom(p) = π1(σ(p)) and range(p) = π2(σ(p))where π is the projection operation Let c1c2 isin C be twoclasses if c1 leC c2 then c1 is a subclass of c2 and c2 is asuperclass of c1

SPARQL is a query language for retrieving and manipu-lating RDF data It is an SQL-like declarative language thequeries are based on pattern matching where the patternsare in the form of triples though they can contain variablesas well Most of the keywords and their meanings are thesame such as SELECT WHERE LIMIT However thereare some new keywords in SPARQL for example OP-TIONAL means optional pattern matching or FILTER thatdefines constraints for the variables Definition 23 givesthe abstract syntax of the filter conditions and Definition 24presents the abstract syntax of the SPARQL expressions

Definition 23 Let V be the set of distinct variables over(I cup B cup L) The variables are distinguished by a ques-tion mark Let X Y isin V be variables and cd isin (Lcup I)be a literal and an IRI constant respectively We definethe filter conditions recursively as follows The X = cX =Y c = d bound(X) isIRI(X) isLiteral(X) andisBlank(X) are atomic filter conditions Thereafter ifR1R2 are filter conditions then notR1 R1 andR2 and R1 orR2are filter conditions as well

Definition 24 A SPARQL expression is built up recur-sively in the following way

1 the triple t isin (I cupV )times (I cupV )times (L cup I cupV ) is aSPARQL expression

2 if Q1Q2 are SPARQL expressions and R is a filterconditionthen Q1 FILTER R Q1 UNION Q2 Q1 OPT Q2 andQ1 AND Q2 are SPARQL expressions as well

The discussion of formal semantics of SPARQL is outof the scope of this paper Set and multiset semantics aredescribed in [11]

3 SYSTEM ARCHITECTURE

As it can be seen on Figure 1 the application uses client-server architecture All resource-intensive operations arecarried out on the server thus clients of any quality canconnect to it even ones with limited CPU or memory re-sources such as smartphones or tablets The clients onlyrequest answers to queries and display the results A clientcan be any devices which are running Android operationsystem Such devices are always on hand and they usuallyhave internet connection The server consists of a middle-ware system and a local database

Fig 1 The architecture of our system

On server-side there is a REST web service whichcommunicates with clients handles the configuration filestransforms the queries according to the requests and for-wards them to local or remote endpoints Furthermorethe server uses an Oracle database with Jena Adapter APIto store the local models It is capable the handle largedatasets The results of queries are sent in form XML backto clients Query results are stored in the local database andsent to the clients in small parts which they can handle

Fig 2 Screens in the client application and transitions betweenthem

A typical scenario is the following The first step is au-thentication which is done with a username and a pass-word Next the middleware loads the userrsquos configurationfile which contains the views which are accessible by himso that he can decide which table view he would like to seeThe middleware sends the table viewrsquos query to either a lo-cal or a remote endpoint depending on where the data forthe view is located The query results are forwarded to theclient dynamically That means only the first few rows aresent and whenever the user gets close to the bottom of thetable while browsing it more rows are sent to the client

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

22 A Mobile Browser Prototype for Semantic Information Systems

This way we can prevent the undesired situation where theclient application would hang until a large table loads Ifthe user filter the table then he can open the filter dialogand construct the filter conditions using the dynamicallybuilt filter form The desired filter conditions are incorpo-rated to the table viewrsquos query and the middleware resendsthis modified query to the endpoint and the results are pro-cessed in a similar way Note that filtering does not requireknowledge of the SPARQL query language [14] the filteroptions can be selected using a graphical interface and theyare translated to SPARQL by the middleware If the userselects an object from the table then the appropriate de-tailed view (which is also defined in the configuration file)appears To do this the middleware needs to send anothermodified query but this time the result is small enough tobe sent to the client in one step

4 SYSTEM FUNCTIONALITY

In this section we describe the main functions of the sys-tem both the client and the server-side components Thedetails of semantic data querying user account handlingviews filters as well as query rewriting are presented

41 Querying semantic data

One of the many advantages of semantic technology isthat we can access data from diverse sources using a uni-fied data model (RDF triples [5]) Furthermore it providesthe SPARQL language which can be used to query seman-tic data These techniques make it possible to create anapplication which can integrate various data sources undera common user interface Data sources can be local [9]or one of the numerous publicly available sources such asDBpedia [4] Geonames [16] etc

Because the clients of the system can be mobile deviceswith limited resources (such as memory or battery life) it isimportant to preprocess the results of the queries It meansthat if the query results are too large they must be split intosmaller fragments and we have to store these fragments ona server and send them to the clients only if it is necessary

42 Handling user accounts

In industrial environment it is essential to handle mul-tiple users and to be able to control which parts of the datacan be seen by the individual users Our system authenti-cates each client with a username and a password Afterconfirming the identity of a user the appropriate configu-ration file will be loaded A configuration file defines theviews that describe the way how a user can see the datasets(see below) Moreover if a query result has been split intopieces because of its size as we mentioned above then thefragments are stored separately for each query of each user

43 Views

One of the main features of our system besides data in-tegration is the management of views Each user has a con-figuration file that is located on the server-side These files

created based on some general schemes then an adminis-trator can be modifies them The configuration file definesthe data accessible for the user and the way it is to be pre-sented There are two types of views the table and thedetailed view

The table view is suitable to define a table of objectsof a given category or class along with their most impor-tant attributes The filter window is available from this typeof view (for further details see the next section) If an ob-ject is selected the appropriate detailed view will be loadedThe detailed view screen is dynamically built up as the filterscreen based on the attribute types In this view additionalattributes are displayed Moreover if one of the attributesdescribe the location of an image or PDF document thatdocument could be displayed as well The semantic repre-sentation allows multiple values to an attribute in the de-tailed view these values will be represented using lists Fig-ure 3 illustrates a table view with the corresponding detailedview

Fig 3 View selection screen with examples for table anddetailed views

44 Filters

From table view screens a special tab is availablewhere you can define filter conditions for the main at-tributes To do this the data type of each attribute mustbe specified in the configuration file These data types canbe one of the followings

bull Text in this case there will be a text area in the filterwindow The value of the attribute must contain thegiven text

bull Enum possible values will be converted to a drop-down list The possible values are collected automat-ically by an auxiliary query

bull Decimal you can define an interval with its lowerand upper bounds If one end of the interval is notspecified that means no bounds

bull Date these are filtered using intervals as well In thiscase bounds can be entered using a date selection di-alog

bull Boolean determines whether the object has a partic-ular property (eg a picture attached to it)

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

Acta Electrotechnica et Informatica Vol 13 No 4 2013 23

After filtering the table only those items remain whichmatch the criteria In Figure 4 you can see two examplesfor filter windows The first one illustrate a date selectionscreen while the second one shows a complex use case withsome attributes related to a person such as name age orexperience level

Fig 4 Filter examples

45 Query rewriting

For each table view its declaration consists of an ini-tial SPARQL query and additional attribute definitionsThat query selects the displayable entities and additional at-tributes which determine the columns of the table on clientside Every attribute has a URI tag a name and a type Thesystem can build the final query from these parameters inthe following way

The names of additional attributes are added to thequery as variables after the SELECT keyword TheWHERE clause will contain the corresponding triplesbased on the attribute declarations in the form (entityltURIgtltname of attributegt) in OPTIONAL groups Incase of filtering the appropriate groups will contain addi-tional triplets based on the filter conditions The Figure 5illustrates the process On left-hand side there is a frag-ment from the configuration file with a table view definitionnamed rsquoCandidatesrsquo The definition contains the SPARQLquery and an attribute description belongs to column namedrsquonamersquo When the client application has to build up the ta-ble view that initial query will be transformed as the right-hand side box shows it

Fig 5 Building a SPARQL query

Detailed views use the initial query of the correspond-ing table view but they define some additional attributesto display in the same way than we do it in case of tableviews with an ltattributegttag In case of detailed viewsthe SPARQL query will be contain an extra condition tofilter data related to the previously chosen entity

5 RELATED WORK

There are some application which have already used theadvantages of semantic technologies In this section wecompare these applications with our system DBpedia Mo-bile [1] is a location-sensitive semantic application whichenables its users to browse linked data on their mobile de-vices by navigating a map The objects on the map are dis-played using icons based on the YAGO categories [10] Byclicking on these icons we can access DBpedia descrip-tions and Flickr [8] photos for the selected object Thisapplication shares many common features with our solu-tion the user can use filters for a search and the resultscan be browsed using a detailed view which displays infor-mation about the object using multiple data sources How-ever there are differences as well which stem from the factthat DBpedia Mobile is an application designed exclusivelyfor one particular data set while our browser is general-purpose and supports arbitrary data sets The most signifi-cant difference is that our main browsing interface is table-based not map-based Moreover in our application the fil-ter conditions cover every attribute of the data set for eachview a filter form is generated automatically where specificfilter conditions can be set for each column In DBPediaMobile however only a limited set of filter conditions areavailable on the form to achieve more complex filteringthe users should use the SPARQL query language

OntoWiki Mobile [7] is an application based on the se-mantic collaboration platform called OntoWiki It allowsits users to browse navigate create and edit semantic dataon-the-fly It focuses on offline editing users can alter theknowledge base without a data connection and their modi-fications will take effect later when a connection is present

The idea of displaying RDF data with views comes fromFresnel [13] It defines a vocabulary which allows the usersto describe the display properties of their data The vocab-ulary uses two basic concepts lenses and formats Lensesdefine the parts of the data which the users would like todisplay and formats define how the selected data shouldappear Selectors for the lenses can be defined using simplerestrictions (eg filtering objects by their type) but morecomplex filters can be expressed with the Fresnel Selec-tor Language (FSL) or the SPARQL query language Avisual browser which implements Fresnel is (for example)IsaViz [12] In our solution we have a different approachto define views the user only needs to specify the parts ofthe data to be displayed as a SPARQL query Formatting isdone automatically based on the data types of the selectedattributes

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

24 A Mobile Browser Prototype for Semantic Information Systems

6 EXPERIMENTS

As we mentioned previously the industryrsquos interesttowards semantic technologies is growing continuouslythanks to the capability to integrate and query data origi-nating from different sources In this section we presenttwo examples where we can use our application The firstexample helps the managers in a company while the sec-ond example illustrates the perspective of truck drivers in atransport company Since our browser was made for gen-eral purposes the range of use is not limited to these twotasks but with these two different uses we would like todemonstrate the diversity of our application

61 HR

At large companies interviewing is typically made bymanagers Those who also deal with many other tasks donot sit permanently in the office so they cannot be informedabout the candidates in time The application we madecan be used for a possible industrial use of a companyrsquosHR and project management support Our application al-lows managers to query the necessary information providedby HR assistants anytime anywhere To help this taskvarious ontologies have been developed for semantic stor-age of the data of the employees trainees candidates orprojects [6] [15]

The views presented above allow for a variety of au-thorized users to view data at different levels Returningto the previous example we can store more data about anapplicant than it is absolutely necessary for the managerThus the data needed for the interview (such as experienceand knowledge) can be filtered beforehand with the helpof the configuration file We can see the process in Figure2 At a larger company there are several managers work-ing each with its own area of responsibility For examplein the software development department such areas are de-signing or testing It is clear that different areas requiredifferent competencies so we can specify initial filters inthe configuration file for these as well Figure 3 illustratesthe selection of a candidate for the tester position

62 Transportation

Another example shows where our application can beused in transportation The drivers receive their waybill thecargo on their truck then they set off to the destination Theperson in office decides what and where to take In order forthe drivers to know where they are to travel they must askit from the office worker It can generate a lot of calls whichcan be very expensive and make a 24-hour dispatch service

necessary Our system can also be a solution to this prob-lem because if the person in the office inserted transporta-tion data in a dataset the drivers would be able to check itanytime on their phones They could see delivered and theupcoming transports too Obviously the current transportcan be found with him so these are not relevant Beforethey arrive to a certain destination they could check whereto go next so they could organize their next day The ap-plication exploits the available public datasets as well Bigcompanies have subsidiaries all over the world The geo-graphical information of the premises of these subsidiariescan be obtained from DBpedia or GeoNames datasets

7 CONCLUSION

In our paper we presented an application which pro-vides opportunity to make use of semantic technologies inthe industry Our main result is a system that makes it pos-sible to browse semantic data sources in personalized wayThe system is simple to configure yet it is general enoughto handle arbitrary semantic datasets Thanks to its client-server architecture the resource-intensive operations can becarried out by the server thus mobile clients with limited re-sources can use the application as well Moreover the clientapplication hides the underlying technological details fromthe user they do not have to formulate SPARQL queriesbecause filtering can be done with the help of easy-to-useforms

We demonstrated the usability of the system with twoenterprise use cases In the first case the application helpsmanagers in finding the right applicant for a job The HRdepartment can record the applicantrsquos data and only thecorresponding manager can access it using a view In thesecond use case truck drivers of a transport company cansee their next freight using their mobile phone or tabletThe application can provide solutions for similar caseswith a suitable configuration file

In the future we would like to extend our system to beable to not just only display the data but edit them as wellWe also aim that data types of attributes could be extractedfrom a corresponding ontology It could simplify the cre-ation and maintenance of configuration files

ACKNOWLEDGEMENT

This work was partially supported by the EuropeanUnion and the European Social Fund through project Fu-turICThu (grant no TAMOP-422C-111KONV-2012-0013) and TAMOP-422C-111KONV-2012-0001 sup-ported by the European Union co-financed by the EuropeanSocial Fund

REFERENCES

[1] BECKER C ndash BIZER C DBpedia Mobile ALocation-Enabled Linked Data Browser World 369No 1 (pp 6-7) (2008)

[2] BERNERS-LEE T ndash HENDLER J ndash LASSILA O

The semantic web Scientific American 284 No 5(pp 28-37) (2001)

[3] BIZER C ndash JENTZSCH A ndash CYGANIAK RState of the LOD Cloud (2011) httpwww4

wiwissfu-berlindelodcloudstate

[4] BIZER C ndash LEHMANN J ndash KOBILAROV G ndashAUER S ndash BECKER C ndash CYGANIAK R ndash HELL-MANN S DBpedia-A crystallization point for the

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

22 A Mobile Browser Prototype for Semantic Information Systems

This way we can prevent the undesired situation where theclient application would hang until a large table loads Ifthe user filter the table then he can open the filter dialogand construct the filter conditions using the dynamicallybuilt filter form The desired filter conditions are incorpo-rated to the table viewrsquos query and the middleware resendsthis modified query to the endpoint and the results are pro-cessed in a similar way Note that filtering does not requireknowledge of the SPARQL query language [14] the filteroptions can be selected using a graphical interface and theyare translated to SPARQL by the middleware If the userselects an object from the table then the appropriate de-tailed view (which is also defined in the configuration file)appears To do this the middleware needs to send anothermodified query but this time the result is small enough tobe sent to the client in one step

4 SYSTEM FUNCTIONALITY

In this section we describe the main functions of the sys-tem both the client and the server-side components Thedetails of semantic data querying user account handlingviews filters as well as query rewriting are presented

41 Querying semantic data

One of the many advantages of semantic technology isthat we can access data from diverse sources using a uni-fied data model (RDF triples [5]) Furthermore it providesthe SPARQL language which can be used to query seman-tic data These techniques make it possible to create anapplication which can integrate various data sources undera common user interface Data sources can be local [9]or one of the numerous publicly available sources such asDBpedia [4] Geonames [16] etc

Because the clients of the system can be mobile deviceswith limited resources (such as memory or battery life) it isimportant to preprocess the results of the queries It meansthat if the query results are too large they must be split intosmaller fragments and we have to store these fragments ona server and send them to the clients only if it is necessary

42 Handling user accounts

In industrial environment it is essential to handle mul-tiple users and to be able to control which parts of the datacan be seen by the individual users Our system authenti-cates each client with a username and a password Afterconfirming the identity of a user the appropriate configu-ration file will be loaded A configuration file defines theviews that describe the way how a user can see the datasets(see below) Moreover if a query result has been split intopieces because of its size as we mentioned above then thefragments are stored separately for each query of each user

43 Views

One of the main features of our system besides data in-tegration is the management of views Each user has a con-figuration file that is located on the server-side These files

created based on some general schemes then an adminis-trator can be modifies them The configuration file definesthe data accessible for the user and the way it is to be pre-sented There are two types of views the table and thedetailed view

The table view is suitable to define a table of objectsof a given category or class along with their most impor-tant attributes The filter window is available from this typeof view (for further details see the next section) If an ob-ject is selected the appropriate detailed view will be loadedThe detailed view screen is dynamically built up as the filterscreen based on the attribute types In this view additionalattributes are displayed Moreover if one of the attributesdescribe the location of an image or PDF document thatdocument could be displayed as well The semantic repre-sentation allows multiple values to an attribute in the de-tailed view these values will be represented using lists Fig-ure 3 illustrates a table view with the corresponding detailedview

Fig 3 View selection screen with examples for table anddetailed views

44 Filters

From table view screens a special tab is availablewhere you can define filter conditions for the main at-tributes To do this the data type of each attribute mustbe specified in the configuration file These data types canbe one of the followings

bull Text in this case there will be a text area in the filterwindow The value of the attribute must contain thegiven text

bull Enum possible values will be converted to a drop-down list The possible values are collected automat-ically by an auxiliary query

bull Decimal you can define an interval with its lowerand upper bounds If one end of the interval is notspecified that means no bounds

bull Date these are filtered using intervals as well In thiscase bounds can be entered using a date selection di-alog

bull Boolean determines whether the object has a partic-ular property (eg a picture attached to it)

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

Acta Electrotechnica et Informatica Vol 13 No 4 2013 23

After filtering the table only those items remain whichmatch the criteria In Figure 4 you can see two examplesfor filter windows The first one illustrate a date selectionscreen while the second one shows a complex use case withsome attributes related to a person such as name age orexperience level

Fig 4 Filter examples

45 Query rewriting

For each table view its declaration consists of an ini-tial SPARQL query and additional attribute definitionsThat query selects the displayable entities and additional at-tributes which determine the columns of the table on clientside Every attribute has a URI tag a name and a type Thesystem can build the final query from these parameters inthe following way

The names of additional attributes are added to thequery as variables after the SELECT keyword TheWHERE clause will contain the corresponding triplesbased on the attribute declarations in the form (entityltURIgtltname of attributegt) in OPTIONAL groups Incase of filtering the appropriate groups will contain addi-tional triplets based on the filter conditions The Figure 5illustrates the process On left-hand side there is a frag-ment from the configuration file with a table view definitionnamed rsquoCandidatesrsquo The definition contains the SPARQLquery and an attribute description belongs to column namedrsquonamersquo When the client application has to build up the ta-ble view that initial query will be transformed as the right-hand side box shows it

Fig 5 Building a SPARQL query

Detailed views use the initial query of the correspond-ing table view but they define some additional attributesto display in the same way than we do it in case of tableviews with an ltattributegttag In case of detailed viewsthe SPARQL query will be contain an extra condition tofilter data related to the previously chosen entity

5 RELATED WORK

There are some application which have already used theadvantages of semantic technologies In this section wecompare these applications with our system DBpedia Mo-bile [1] is a location-sensitive semantic application whichenables its users to browse linked data on their mobile de-vices by navigating a map The objects on the map are dis-played using icons based on the YAGO categories [10] Byclicking on these icons we can access DBpedia descrip-tions and Flickr [8] photos for the selected object Thisapplication shares many common features with our solu-tion the user can use filters for a search and the resultscan be browsed using a detailed view which displays infor-mation about the object using multiple data sources How-ever there are differences as well which stem from the factthat DBpedia Mobile is an application designed exclusivelyfor one particular data set while our browser is general-purpose and supports arbitrary data sets The most signifi-cant difference is that our main browsing interface is table-based not map-based Moreover in our application the fil-ter conditions cover every attribute of the data set for eachview a filter form is generated automatically where specificfilter conditions can be set for each column In DBPediaMobile however only a limited set of filter conditions areavailable on the form to achieve more complex filteringthe users should use the SPARQL query language

OntoWiki Mobile [7] is an application based on the se-mantic collaboration platform called OntoWiki It allowsits users to browse navigate create and edit semantic dataon-the-fly It focuses on offline editing users can alter theknowledge base without a data connection and their modi-fications will take effect later when a connection is present

The idea of displaying RDF data with views comes fromFresnel [13] It defines a vocabulary which allows the usersto describe the display properties of their data The vocab-ulary uses two basic concepts lenses and formats Lensesdefine the parts of the data which the users would like todisplay and formats define how the selected data shouldappear Selectors for the lenses can be defined using simplerestrictions (eg filtering objects by their type) but morecomplex filters can be expressed with the Fresnel Selec-tor Language (FSL) or the SPARQL query language Avisual browser which implements Fresnel is (for example)IsaViz [12] In our solution we have a different approachto define views the user only needs to specify the parts ofthe data to be displayed as a SPARQL query Formatting isdone automatically based on the data types of the selectedattributes

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

24 A Mobile Browser Prototype for Semantic Information Systems

6 EXPERIMENTS

As we mentioned previously the industryrsquos interesttowards semantic technologies is growing continuouslythanks to the capability to integrate and query data origi-nating from different sources In this section we presenttwo examples where we can use our application The firstexample helps the managers in a company while the sec-ond example illustrates the perspective of truck drivers in atransport company Since our browser was made for gen-eral purposes the range of use is not limited to these twotasks but with these two different uses we would like todemonstrate the diversity of our application

61 HR

At large companies interviewing is typically made bymanagers Those who also deal with many other tasks donot sit permanently in the office so they cannot be informedabout the candidates in time The application we madecan be used for a possible industrial use of a companyrsquosHR and project management support Our application al-lows managers to query the necessary information providedby HR assistants anytime anywhere To help this taskvarious ontologies have been developed for semantic stor-age of the data of the employees trainees candidates orprojects [6] [15]

The views presented above allow for a variety of au-thorized users to view data at different levels Returningto the previous example we can store more data about anapplicant than it is absolutely necessary for the managerThus the data needed for the interview (such as experienceand knowledge) can be filtered beforehand with the helpof the configuration file We can see the process in Figure2 At a larger company there are several managers work-ing each with its own area of responsibility For examplein the software development department such areas are de-signing or testing It is clear that different areas requiredifferent competencies so we can specify initial filters inthe configuration file for these as well Figure 3 illustratesthe selection of a candidate for the tester position

62 Transportation

Another example shows where our application can beused in transportation The drivers receive their waybill thecargo on their truck then they set off to the destination Theperson in office decides what and where to take In order forthe drivers to know where they are to travel they must askit from the office worker It can generate a lot of calls whichcan be very expensive and make a 24-hour dispatch service

necessary Our system can also be a solution to this prob-lem because if the person in the office inserted transporta-tion data in a dataset the drivers would be able to check itanytime on their phones They could see delivered and theupcoming transports too Obviously the current transportcan be found with him so these are not relevant Beforethey arrive to a certain destination they could check whereto go next so they could organize their next day The ap-plication exploits the available public datasets as well Bigcompanies have subsidiaries all over the world The geo-graphical information of the premises of these subsidiariescan be obtained from DBpedia or GeoNames datasets

7 CONCLUSION

In our paper we presented an application which pro-vides opportunity to make use of semantic technologies inthe industry Our main result is a system that makes it pos-sible to browse semantic data sources in personalized wayThe system is simple to configure yet it is general enoughto handle arbitrary semantic datasets Thanks to its client-server architecture the resource-intensive operations can becarried out by the server thus mobile clients with limited re-sources can use the application as well Moreover the clientapplication hides the underlying technological details fromthe user they do not have to formulate SPARQL queriesbecause filtering can be done with the help of easy-to-useforms

We demonstrated the usability of the system with twoenterprise use cases In the first case the application helpsmanagers in finding the right applicant for a job The HRdepartment can record the applicantrsquos data and only thecorresponding manager can access it using a view In thesecond use case truck drivers of a transport company cansee their next freight using their mobile phone or tabletThe application can provide solutions for similar caseswith a suitable configuration file

In the future we would like to extend our system to beable to not just only display the data but edit them as wellWe also aim that data types of attributes could be extractedfrom a corresponding ontology It could simplify the cre-ation and maintenance of configuration files

ACKNOWLEDGEMENT

This work was partially supported by the EuropeanUnion and the European Social Fund through project Fu-turICThu (grant no TAMOP-422C-111KONV-2012-0013) and TAMOP-422C-111KONV-2012-0001 sup-ported by the European Union co-financed by the EuropeanSocial Fund

REFERENCES

[1] BECKER C ndash BIZER C DBpedia Mobile ALocation-Enabled Linked Data Browser World 369No 1 (pp 6-7) (2008)

[2] BERNERS-LEE T ndash HENDLER J ndash LASSILA O

The semantic web Scientific American 284 No 5(pp 28-37) (2001)

[3] BIZER C ndash JENTZSCH A ndash CYGANIAK RState of the LOD Cloud (2011) httpwww4

wiwissfu-berlindelodcloudstate

[4] BIZER C ndash LEHMANN J ndash KOBILAROV G ndashAUER S ndash BECKER C ndash CYGANIAK R ndash HELL-MANN S DBpedia-A crystallization point for the

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

Acta Electrotechnica et Informatica Vol 13 No 4 2013 23

After filtering the table only those items remain whichmatch the criteria In Figure 4 you can see two examplesfor filter windows The first one illustrate a date selectionscreen while the second one shows a complex use case withsome attributes related to a person such as name age orexperience level

Fig 4 Filter examples

45 Query rewriting

For each table view its declaration consists of an ini-tial SPARQL query and additional attribute definitionsThat query selects the displayable entities and additional at-tributes which determine the columns of the table on clientside Every attribute has a URI tag a name and a type Thesystem can build the final query from these parameters inthe following way

The names of additional attributes are added to thequery as variables after the SELECT keyword TheWHERE clause will contain the corresponding triplesbased on the attribute declarations in the form (entityltURIgtltname of attributegt) in OPTIONAL groups Incase of filtering the appropriate groups will contain addi-tional triplets based on the filter conditions The Figure 5illustrates the process On left-hand side there is a frag-ment from the configuration file with a table view definitionnamed rsquoCandidatesrsquo The definition contains the SPARQLquery and an attribute description belongs to column namedrsquonamersquo When the client application has to build up the ta-ble view that initial query will be transformed as the right-hand side box shows it

Fig 5 Building a SPARQL query

Detailed views use the initial query of the correspond-ing table view but they define some additional attributesto display in the same way than we do it in case of tableviews with an ltattributegttag In case of detailed viewsthe SPARQL query will be contain an extra condition tofilter data related to the previously chosen entity

5 RELATED WORK

There are some application which have already used theadvantages of semantic technologies In this section wecompare these applications with our system DBpedia Mo-bile [1] is a location-sensitive semantic application whichenables its users to browse linked data on their mobile de-vices by navigating a map The objects on the map are dis-played using icons based on the YAGO categories [10] Byclicking on these icons we can access DBpedia descrip-tions and Flickr [8] photos for the selected object Thisapplication shares many common features with our solu-tion the user can use filters for a search and the resultscan be browsed using a detailed view which displays infor-mation about the object using multiple data sources How-ever there are differences as well which stem from the factthat DBpedia Mobile is an application designed exclusivelyfor one particular data set while our browser is general-purpose and supports arbitrary data sets The most signifi-cant difference is that our main browsing interface is table-based not map-based Moreover in our application the fil-ter conditions cover every attribute of the data set for eachview a filter form is generated automatically where specificfilter conditions can be set for each column In DBPediaMobile however only a limited set of filter conditions areavailable on the form to achieve more complex filteringthe users should use the SPARQL query language

OntoWiki Mobile [7] is an application based on the se-mantic collaboration platform called OntoWiki It allowsits users to browse navigate create and edit semantic dataon-the-fly It focuses on offline editing users can alter theknowledge base without a data connection and their modi-fications will take effect later when a connection is present

The idea of displaying RDF data with views comes fromFresnel [13] It defines a vocabulary which allows the usersto describe the display properties of their data The vocab-ulary uses two basic concepts lenses and formats Lensesdefine the parts of the data which the users would like todisplay and formats define how the selected data shouldappear Selectors for the lenses can be defined using simplerestrictions (eg filtering objects by their type) but morecomplex filters can be expressed with the Fresnel Selec-tor Language (FSL) or the SPARQL query language Avisual browser which implements Fresnel is (for example)IsaViz [12] In our solution we have a different approachto define views the user only needs to specify the parts ofthe data to be displayed as a SPARQL query Formatting isdone automatically based on the data types of the selectedattributes

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

24 A Mobile Browser Prototype for Semantic Information Systems

6 EXPERIMENTS

As we mentioned previously the industryrsquos interesttowards semantic technologies is growing continuouslythanks to the capability to integrate and query data origi-nating from different sources In this section we presenttwo examples where we can use our application The firstexample helps the managers in a company while the sec-ond example illustrates the perspective of truck drivers in atransport company Since our browser was made for gen-eral purposes the range of use is not limited to these twotasks but with these two different uses we would like todemonstrate the diversity of our application

61 HR

At large companies interviewing is typically made bymanagers Those who also deal with many other tasks donot sit permanently in the office so they cannot be informedabout the candidates in time The application we madecan be used for a possible industrial use of a companyrsquosHR and project management support Our application al-lows managers to query the necessary information providedby HR assistants anytime anywhere To help this taskvarious ontologies have been developed for semantic stor-age of the data of the employees trainees candidates orprojects [6] [15]

The views presented above allow for a variety of au-thorized users to view data at different levels Returningto the previous example we can store more data about anapplicant than it is absolutely necessary for the managerThus the data needed for the interview (such as experienceand knowledge) can be filtered beforehand with the helpof the configuration file We can see the process in Figure2 At a larger company there are several managers work-ing each with its own area of responsibility For examplein the software development department such areas are de-signing or testing It is clear that different areas requiredifferent competencies so we can specify initial filters inthe configuration file for these as well Figure 3 illustratesthe selection of a candidate for the tester position

62 Transportation

Another example shows where our application can beused in transportation The drivers receive their waybill thecargo on their truck then they set off to the destination Theperson in office decides what and where to take In order forthe drivers to know where they are to travel they must askit from the office worker It can generate a lot of calls whichcan be very expensive and make a 24-hour dispatch service

necessary Our system can also be a solution to this prob-lem because if the person in the office inserted transporta-tion data in a dataset the drivers would be able to check itanytime on their phones They could see delivered and theupcoming transports too Obviously the current transportcan be found with him so these are not relevant Beforethey arrive to a certain destination they could check whereto go next so they could organize their next day The ap-plication exploits the available public datasets as well Bigcompanies have subsidiaries all over the world The geo-graphical information of the premises of these subsidiariescan be obtained from DBpedia or GeoNames datasets

7 CONCLUSION

In our paper we presented an application which pro-vides opportunity to make use of semantic technologies inthe industry Our main result is a system that makes it pos-sible to browse semantic data sources in personalized wayThe system is simple to configure yet it is general enoughto handle arbitrary semantic datasets Thanks to its client-server architecture the resource-intensive operations can becarried out by the server thus mobile clients with limited re-sources can use the application as well Moreover the clientapplication hides the underlying technological details fromthe user they do not have to formulate SPARQL queriesbecause filtering can be done with the help of easy-to-useforms

We demonstrated the usability of the system with twoenterprise use cases In the first case the application helpsmanagers in finding the right applicant for a job The HRdepartment can record the applicantrsquos data and only thecorresponding manager can access it using a view In thesecond use case truck drivers of a transport company cansee their next freight using their mobile phone or tabletThe application can provide solutions for similar caseswith a suitable configuration file

In the future we would like to extend our system to beable to not just only display the data but edit them as wellWe also aim that data types of attributes could be extractedfrom a corresponding ontology It could simplify the cre-ation and maintenance of configuration files

ACKNOWLEDGEMENT

This work was partially supported by the EuropeanUnion and the European Social Fund through project Fu-turICThu (grant no TAMOP-422C-111KONV-2012-0013) and TAMOP-422C-111KONV-2012-0001 sup-ported by the European Union co-financed by the EuropeanSocial Fund

REFERENCES

[1] BECKER C ndash BIZER C DBpedia Mobile ALocation-Enabled Linked Data Browser World 369No 1 (pp 6-7) (2008)

[2] BERNERS-LEE T ndash HENDLER J ndash LASSILA O

The semantic web Scientific American 284 No 5(pp 28-37) (2001)

[3] BIZER C ndash JENTZSCH A ndash CYGANIAK RState of the LOD Cloud (2011) httpwww4

wiwissfu-berlindelodcloudstate

[4] BIZER C ndash LEHMANN J ndash KOBILAROV G ndashAUER S ndash BECKER C ndash CYGANIAK R ndash HELL-MANN S DBpedia-A crystallization point for the

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

24 A Mobile Browser Prototype for Semantic Information Systems

6 EXPERIMENTS

As we mentioned previously the industryrsquos interesttowards semantic technologies is growing continuouslythanks to the capability to integrate and query data origi-nating from different sources In this section we presenttwo examples where we can use our application The firstexample helps the managers in a company while the sec-ond example illustrates the perspective of truck drivers in atransport company Since our browser was made for gen-eral purposes the range of use is not limited to these twotasks but with these two different uses we would like todemonstrate the diversity of our application

61 HR

At large companies interviewing is typically made bymanagers Those who also deal with many other tasks donot sit permanently in the office so they cannot be informedabout the candidates in time The application we madecan be used for a possible industrial use of a companyrsquosHR and project management support Our application al-lows managers to query the necessary information providedby HR assistants anytime anywhere To help this taskvarious ontologies have been developed for semantic stor-age of the data of the employees trainees candidates orprojects [6] [15]

The views presented above allow for a variety of au-thorized users to view data at different levels Returningto the previous example we can store more data about anapplicant than it is absolutely necessary for the managerThus the data needed for the interview (such as experienceand knowledge) can be filtered beforehand with the helpof the configuration file We can see the process in Figure2 At a larger company there are several managers work-ing each with its own area of responsibility For examplein the software development department such areas are de-signing or testing It is clear that different areas requiredifferent competencies so we can specify initial filters inthe configuration file for these as well Figure 3 illustratesthe selection of a candidate for the tester position

62 Transportation

Another example shows where our application can beused in transportation The drivers receive their waybill thecargo on their truck then they set off to the destination Theperson in office decides what and where to take In order forthe drivers to know where they are to travel they must askit from the office worker It can generate a lot of calls whichcan be very expensive and make a 24-hour dispatch service

necessary Our system can also be a solution to this prob-lem because if the person in the office inserted transporta-tion data in a dataset the drivers would be able to check itanytime on their phones They could see delivered and theupcoming transports too Obviously the current transportcan be found with him so these are not relevant Beforethey arrive to a certain destination they could check whereto go next so they could organize their next day The ap-plication exploits the available public datasets as well Bigcompanies have subsidiaries all over the world The geo-graphical information of the premises of these subsidiariescan be obtained from DBpedia or GeoNames datasets

7 CONCLUSION

In our paper we presented an application which pro-vides opportunity to make use of semantic technologies inthe industry Our main result is a system that makes it pos-sible to browse semantic data sources in personalized wayThe system is simple to configure yet it is general enoughto handle arbitrary semantic datasets Thanks to its client-server architecture the resource-intensive operations can becarried out by the server thus mobile clients with limited re-sources can use the application as well Moreover the clientapplication hides the underlying technological details fromthe user they do not have to formulate SPARQL queriesbecause filtering can be done with the help of easy-to-useforms

We demonstrated the usability of the system with twoenterprise use cases In the first case the application helpsmanagers in finding the right applicant for a job The HRdepartment can record the applicantrsquos data and only thecorresponding manager can access it using a view In thesecond use case truck drivers of a transport company cansee their next freight using their mobile phone or tabletThe application can provide solutions for similar caseswith a suitable configuration file

In the future we would like to extend our system to beable to not just only display the data but edit them as wellWe also aim that data types of attributes could be extractedfrom a corresponding ontology It could simplify the cre-ation and maintenance of configuration files

ACKNOWLEDGEMENT

This work was partially supported by the EuropeanUnion and the European Social Fund through project Fu-turICThu (grant no TAMOP-422C-111KONV-2012-0013) and TAMOP-422C-111KONV-2012-0001 sup-ported by the European Union co-financed by the EuropeanSocial Fund

REFERENCES

[1] BECKER C ndash BIZER C DBpedia Mobile ALocation-Enabled Linked Data Browser World 369No 1 (pp 6-7) (2008)

[2] BERNERS-LEE T ndash HENDLER J ndash LASSILA O

The semantic web Scientific American 284 No 5(pp 28-37) (2001)

[3] BIZER C ndash JENTZSCH A ndash CYGANIAK RState of the LOD Cloud (2011) httpwww4

wiwissfu-berlindelodcloudstate

[4] BIZER C ndash LEHMANN J ndash KOBILAROV G ndashAUER S ndash BECKER C ndash CYGANIAK R ndash HELL-MANN S DBpedia-A crystallization point for the

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk

Acta Electrotechnica et Informatica Vol 13 No 4 2013 25

Web of Data Web Semantics Science Services andAgents on the World Wide Web 7 No 3 (pp154-165)(2009)

[5] BRICKLEY D ndash GUHA R V RDF vocabulary de-scription language 10 RDF schema (2004)

[6] DORN J ndash NAZ T ndash PICHLMAIR M Ontologydevelopment for human resource management In 4thInternational Conference on Knowledge Management(pp 109-120) (2007)

[7] ERMILOV T ndash HEINO N ndash TRAMP S ndash AUERS Ontowiki mobile ndash knowledge management inyour pocket In The Semantic Web Research andApplications (pp 185-199) Springer Berlin Heidel-berg (2011)

[8] FLICKR httpwwwflickrcom

[9] GOMBOS G ndash MATUSZKA T ndash PINCZEL B ndashRACZ G ndash KISS A VOSD A General-Purpose Vir-tual Observatory over Semantic Databases In 13thSymposium on Programming Languages and Soft-ware Tools (SPLST 2013) (pp 90-99) (2013)

[10] HOFFART J ndash SUCHANEK F M ndash BERBERICHK ndash LEWIS-KELHAM E ndash DE MELO G ndashWEIKUM G Yago2 exploring and querying worldknowledge in time space context and many lan-guages In Proceedings of the 20th international con-ference companion on World wide web (pp 229-232)ACM (2011)

[11] PEREZ J ndash ARENAS M ndash GUTIERREZ C Se-mantics and Complexity of SPARQL In The Seman-tic Web-ISWC 2006 (pp 30-43) Springer Berlin Hei-delberg (2006)

[12] PIETRIGA E Isaviz a visual environment forbrowsing and authoring rdf models In Eleventh In-ternational World Wide Web Conference DevelopersDay (2002)

[13] PIETRIGA E ndash BIZER C ndash KARGER D ndash LEER Fresnel A browser-independent presentation vo-cabulary for RDF In The semantic web-ISWC 2006(pp 158-171) Springer Berlin Heidelberg (2006)

[14] PRUDHOMMEAUX E ndash SEABORNE ASPARQL query language for RDF W3C recommen-dation 15 (2008)

[15] SZEKELY A An Approach to Ontology Develop-ment in Human Resources Management In Proceed-ings of the 5th International Conference on VirtualLearning (pp 153-159) (2010)

[16] VATANT B ndash WICK M Geonames ontology(2012)

[17] VOLZ R ndash KLEB J ndash MUELLER W To-wards Ontology-based Disambiguation of Geograph-ical Identifiers In Proceedings of WWW2007 (2007)

Received December 2 2013 accepted December 22 2013

BIOGRAPHIES

Gergo Gombos was born on 14121986 In 2012 he grad-uated from the Department of Information Systems of theFaculty of Informatics at Eotvos Lorand University in Bu-dapest His master thesis was about the webservice formobile application that use the semantic web Since 2012he is a PhD student at the same Department His scientificresearch is focusing on semantic web cloud and distibutedcomputing webservices and federated queries He is alsointerested in collecting the social networks and big data

Tamas Matuszka was born on 25111987 In 2012 hegraduated (MSc) with distinction at the department of In-formation Systems of the Faculty of Eotvos Lorand Uni-versity in Budapest Since 2012 he is PhD student at theDepartment of Information Systems His scientific researchis focusing on Augmented Reality supported by SemanticWeb In addition he also investigates questions related withthe mobile Semantic Web and Augmented Reality

Balazs Pinczel was born on 23111988 In 2012 he grad-uated (MSc) with distinction at the department of Informa-tion Systems of the Faculty of Eotvos Lorand Universityin Budapest Since 2012 he is a PhD student at the De-partment of Information Systems His scientific research isprimarily focusing on improving the efficiency of SemanticWeb technologies (eg SPARQL query evaluation) In ad-dition he is also interested in the area of NoSQL databasesand Big Data

Gabor Racz was born on 27021989 In 2012 he grad-uated from the Department of Information Systems of theFaculty of Informatics at Eotvos Lorand University in Bu-dapest His master thesis was about the visual query lan-guages and the SPARQL query language Since 2012 heis a PhD student at the same Department His scientificresearch is focusing on semantic web and data mining Inaddition he is also interested in natural language process-ing and social networks

Attila Kiss was born in 1960 In 1985 he graduated (MSc)as mathematician at Eotvos Lorand University in BudapestHe defended his PhD in the field of database theory in 1991his thesis title was Dependencies of Relational DatabasesSince 2010 he is working as the head of Information Sys-tems Department at Etvs Lornd University His scientificresearch is focusing on database theory and practice se-mantic web big data graph databases data mining Inaddition he also investigates questions related with socialnetwork analysis

Tamas Gaizer was born on 1964 In 1986 he graduated(MSc) with distinction at the University of Szeged Since1995 he is working as a senior IT consultant at Regens ZrtHis scientific research is focusing on analysis design andimplementation of systems In addition he also investi-gates questions related with business process analysis andmodeling

ISSN 1335-8243 (print) ccopy 2013 FEI TUKE ISSN 1338-3957 (online) wwwaeitukesk