Tractability Method for Ontology Development of Semantic Web

Abstract—Ontology is an explicit representation of a particular domain of interest. This paper focuses on the inherent problem of intractability (data complexity) due to data capturing for heterogeneous sources. We proposed to follow the tradition of knowledge representation and reasoning processes to develop aSemantic Web. Building query system is the ultimate goal for this study. Conjunctive queries were adopted over ontology methods to address the data complexity. The Conjunctive queries are the form of queries in first-order logic that deal with conjunction (⋀)and existential quantification (∃) in database atomic formulae. State of the art meteorological and oceanographic domain provides the motivation for this work. We found that the querying method facilitates the tractability in reasoning and logical systems.

Keywords- Ontology, Semantic Web, Conjunctive Queries, tractability

I. INTRODUCTION

Due to the increase of numerous data from different sources over the Internet, the nature of sharing and keeping data in a database has changed to new knowledge base transition [5, 6,16]. This is supported by efficient reasoning tools like Web Ontology Language (OWL) and Resource Description Framework (RDF). However, the incompatibility with software across various layers in Semantic Web architecture brings about computational intractability. The most intractable problem is difficulty in understanding new or unfamiliar extensions of meaning given to computer systems [16].

Many Artificial Intelligence (AI) protagonists have been attempting to solve the problems of intractability in structural complexity and diversity of data [16]. Intuitively, for instance, in meteorological and oceanographic domain, the challenge of sharing information from different sources of oil and gas data sectors makes knowledge and sharing approaches complex.

Therefore, integration of data using machine-understandable metadata is the solution of reducing such problems. This can be done by building conceptual layer or ontology model by intelligent systems and producing interoperability between heterogeneous systems [3, 5, 12]. Italso makes the domain to be reused and allows knowledge sharing.

Theoretically, Semantic Web is a vision initiated by Tim Berners-Lee with the World-Wide Web Consortium (W3C) standards, as well as programs and technologies. In practice, we consider the openness and accessibility of the Web that makes machine-understandable and allows sharing data as Semantic Web. The technologies/architecture of Semantic Webincludes: trust, logic and proof, eXtensible Markup Language (XML), OWL, RDF and RDF Schema (RDFS) and Uniform Resource Identifier (URI) [6, 13] (see Figure 1.1). Logically, such technologies allow people to leverage and share data across the globe using machine-readable or intelligent machine of Web pages [7-9, 17]. Furthermore, these technologies enhance understanding and transfer of knowledge over deductive reasoning processes.

Figure 1.1: Semantic Web Architecture [13]

Thus, in developing such processes, methodologies [12], frameworks [13, 19, 24] and/or models [14] are needed based on ontology engineering and reasoning aspects. In particular, the research area is the intersection between Ontology, Description Logics and RDF with the code technologies as the theoretical framework for the knowledge representation.

This paper focuses more on logic part of the Semantic Web.The main objective and purpose of this study is to determine

Kamaluddeen Usman Danyaro1, Jafreezal Jaafar2, Mohd Shahir Liew3

1, 2 Department of Computer & Information SciencesUniversiti Teknologi PETRONAS, Seri Iskandar, 31750 Tronoh, Perak, Malaysia

3 Department of Civil EngineeringUniversiti Teknologi PETRONAS, Seri Iskandar, 31750 Tronoh, Perak, Malaysia

kamaluddeen_g01325@utp.edu.my, jafreez@petronas.com.my, shahir_liew@petronas.com.my

978-1-61284-353-7/11/$26.00 © 2011 IEEE

the behavior of different knowledge bases particularly in oceanographic environment. Hence, we use conjunctive query methods to determine the tractability of inferences. Moreover,according to [16, 29, 30] computability, decidability and tractability are the major computational techniques in AI.

The rest of this paper is organized as follows. Section 2 consists of review of some works related to ontology, RDF, RDFS, description logics and conjunctive queries. Section 3 presents a case study. Section 4 discusses the experimental approach for data retrieval. Finally, in section 5 we derive the conclusions and give remarks for the resulting method for future work.

II. RELATED WORK

There have been numerous research and developments of distributed knowledge bases (ontologies) in different domains, for instance, in medical or biological environments [4, 15]. But, there has not been much in the area of oil and gas or meteorological and oceanographic environment. Ontologiesare based on utilization, optimization, or extraction of resources and then address some level annotations as well as query answering. Therefore, the challenge is building knowledge-intensive task where all the resources may be made available. In this part, we present the related works that are important to understand the problems.

A. Ontologies and the Semantic Web

Ontology is an explicit representation of a particular domain of interest. Indeed, ontologies allow understanding a domain based on specific concepts. The traditional concepts and vocabularies describe the properties and instances of a particular domain. According to Yeung and Leung [14], ontology means the specification of relations, properties and objects that one meets across a particular domain. Also, as from the most cited [4, 14, 17] definition of ontology given by Gruber:

An ontology is a formal explicit specification of a shared conceptualization of a domain of interest.

This shows that the specification of a certain domain isformally and explicitly shared. However, the formality here means the expression of ontology as a formal Semantics that must be expressed in terms of knowledge representation language [17]. According to [12], conceptualization makes the model of knowledge to be suitable for reasoning at the knowledge level. This provides the ontology to be machine-processable and allow knowledge representation that can be interpreted precisely. Generally, the representations are been understood by humans. If the knowledge representation is done through the concepts and relates it to humans using symbols on a particular group, it is then considered as conceptualization. Nevertheless, the knowledge representation has major three flavors; concepts, relations and instances (C, Rand I) [17, 14].

Semantically, ontologies enable the domain-specific vocabulary for data annotation with metadata. This is underthe conceptual domains that facilitate machine-interoperability for the knowledge representation. Semantic logic-based

notations reduce the complexity by encoding axiomatic information. According to [17] axioms refers to the set of ontological statements that can be expressed in form of vocabularies. Examples; "the Weather is hot" or "only those who are registered with MetOcean can know the current wave direction of Mediterranean Sea". Both of these consist of instances and concepts on a particular domain.

B. Knowledge Representations in Ontologies and the Semantic Web

The resources of Semantic Web and Semantic annotation language are standardized by W3C. The standard allowsencoding metadata that constitutes the description of RDF and RDF(S) languages [17, 18]. On top of RDF(S) (see Figure 1.1), the W3C built the standard for OWL family with intention for providing ontologies in the Semantic Web. Evidently, it increases expressiveness and formalism of DLs[14, 17]. For instance in practice, ALC (Attributive, Language with Complement) gives a constructive language expression for the formalism of DLs. Indeed, the universal concept restrictions and/or existential concept restrictions are considered to as primitive concepts and the disjunction of conjunctive element expression is to be in ALC normal form [16]. Table 1.1 shows the class/concept constructors of ALCthat support related markup such us OWL.

Table 1.1: Class Constructors

1) RDFResource Description Framework (RDF) use URI to model

the entities in ontology uniquely. This uniqueness identified and organized through the decentralization of knowledge on the Web. Since the resources are naming entities, therefore they can also be referred as URIs [17]. The triple in RDF is astatement that consists of subject, predicate and object. RDF captures the metadata and knowledge from the Web page [17]. It is also a Web language where many triples in RDF graph are being shared from different sites. More precisely, we can say RDF is a language for specifying languages. It has nodes and arc which represent resources and properties respectively. On other hand, the concept of a class in RDF identifies the RDFS resources.

2) RDFSRDF Schema (RDFS) are classes arrange in hierarchical

order of RDF resources [17]. This provides another future for describing the ontologies. Therefore, the class membership is indicated by reserving the predicate rdf: type (rdfs: class). Also, the same as property, rdfs: property. Logically, the rules

of RDF are intrinsically determined by the Semantic class membership. As from [17] for example;

IF F contains (C, rdfs: subClassOf, D) and (R, rdf: type, C) THEN drive (R, rdf: type, D)

IF F contains (C, rdfs: SubClassOf, D) and CD, rdfs: subClassOf, E) THEN drive (C, rdfs: SubClassOf, E)

These can be instantiated in RDF resources or URI together with the variables (C, D, E, F).

3) DLsThe idea of description logics (DLs) in Web environment

comes through many ideas of set theories. For example, OWL-DL and OWL-Lite were axiomatically OWL standards after the work from description logics [7, 17]. They can also be described as the subsets of First-order logic (FOL) due to their formalisms in description logics. In particular, looking at RDF(S) and OWL vocabularies, they were known as “T-Box” and “A-Box” respectively [14]. Also, [19] pointed that DLs are the representation of knowledge languages and mainly for particularization of ontologies that bring the knowledge bases and reasoning together. However, since DLs are essentially decidable, meaning that it has effective method for determining membership of the sets. Therefore, the decidability in DL gives high impacts in knowledge representation paradigm. These can be considered as the theoretical foundation of logical reasoning in Web services.Consequently, in this research, we validate this using case study as discussed in section III.

Broadly speaking, reasoning over comprehensive data is important. However, there are difficulties in data expression and vague knowledge use in reasoning DLs [20]. Also, [21]noted that without the help of Semantic, it is difficult to understand, analyze or optimize the behavior of reasoning systems. Therefore, the ultimate goal of reasoning is to enable "global knowledge-base" through intelligent machine over queried data. This can be achieved through capturing behavior and creating algorithms. But, some authors [14, 20, 22, 23] noted that, the capturing behavior in linguistic way is simple and has to be comprehended based on the knowledge base agent, which is near to human thinking. Significantly, to enhance the strength of Semantic Web over the real world knowledge, the knowledge base structures that answer queries from the resources must be incorporated. For this purpose, wedescribe the methods of capturing and well as reasoning processes with the aid of a reasoning tool (Protégé 4.1 [25]).

Thus, the knowledge could be published explicitly on the Web and then implicitly inferable by machine. This is a critical challenge for reasoning, querying and answering the data from huge amount of database.

C. Conjunctive Queries

Query answering method is a good idea used to show how different knowledge representations behave. In this paper, we use the conjunctive queries so that we can determine the tractability of inferences. It is noted that the emphasis on

tractability of inference is the major aspects of DLs [30]. Therefore, disjunctions and negations have less presence in classic AI and totally absence in DLs. Now, the queries which are expressive in computation which enumerate all necessary things on a particular knowledge base statement can be referred as conjunctive queries [30]. More specifically, consider the following complex conjunctive query definitions.They include simple use of conjunctions and existential quantification.

Definition 1

A conjunctive query q is of the form

q(X) ← ∃x1, x2, . . . , xk(R1,(U1) ⋀ R2,(U2) … ⋀ Rn(Un)),

where q(X) is the head while the other part after ← is the body. The variables x1, x2, . . . , xk are all occurring in the bodynot in the head. The symbol ⋀ denotes the conjunctions and ∃represent the existential quantification.

Definition 2(Terminological Queries [1])

Suppose K is a set of variables disjoint from IN; then the terminological query Q over a knowledge base T is an expression of the form:

qli, . . . qmi

where qi are the query terms of the form x : c or (x, y) : r such that x, y ϵ K ∪ IN, C ∈ CN and R ϵ RN.

D. MetOcean

For the domain model, ontology is used for many purposes but it depends on the scenario of application. Meteorological and oceanographic knowledge is complex and require clear processes. One of the challenging factors of MetOcean knowledge is its ineludibly sharing nature in terms of data or metadata access by major oil and gas companies worldwide [10]. Therefore, its database suffers from the diversity across data or metadata and lacks the power to search the huge amount of data precisely and meaningfully. Therefore, we focus database management systems in oceanographic data that need such integration. This is however due to the high demand of data exchange (dumping / retrieving) by research institutes and other industrial agencies [29]. Consequently, the ontologyapplication will solve the problem of diversity across the data or metadata using annotation. Not to mention, Semantic Webtechnologies, OWL for instance has been used for solving the problem of interoperability [2]. On this occasion, MetOcean in situ is a well known example of oceanographic organization that deals with data and metadata [26-28]. Therefore, this necessitates the integration of MetOcean data or metadata into reasoning aspects.

III. CASE STUDY

As we mentioned the need for integrating MetOcean data in section II above, it is necessary to integrate the knowledge base that could facilitate the way people access data from MetOcean, since the information is stored as “metadata” [11]. In this regard, meteorological and oceanographic ontologies provide and describe the simple expressions basically on the

domain of discourse for a distinct community. Ontology comprises the extension of a Semantic repository with related concepts in regional ocean and marine systems. The repository solves the problems of interoperability in which it allowsmany database to be integrated explicitly.

On this occasion, an ontology is developed using protégé 4.1 [25] and is based on the concepts related to MetOceaninformation systems (Figure 1.2). This guides and stimulatesthe knowledge discovery for the development of Semantic Web.

Figure 1.2: MetOcean Relation Ontology Concepts

There are presently three ontologies for the development of MetOcean (Meteorological and Oceanographic) Semantic Web. Each represents an explicit domain in the meteorological and oceanographic area.

The first one is the time series data which has the entity representing the sequence of data related to weather forecast domain. This includes the abstract concepts, like wave time, real time of sea level, current time and their instances.

The second is the oceanographic parameter domain. This deals with the actual and forecasting interval parametric knowledge.

The third one is the MetOcean sea place domain. The classes here represent the knowledge contained in sea places

where oil and gas companies provide or receive information. The subclasses are multi-inherited from such places.

Thus, this ontology model is the heart of this specific case study. It provides and identifies the relationships among oil and gas companies, sea places, sea parameters, time series data, and so on. Nevertheless, it covers the property of the domain of interest that describes schema level and instance level information governing the knowledge-based system. However, to query the data we follow the experiment as below.

IV. RESULT AND DISCUSSIONS

This approach provides a MetOcean query experiment for checking complexity of querying as well as future capability of information integration.

The query or method is designed in order to aid the particular problem of complexity evaluation. It is from oil and gas information systems’ viewpoint. However, for practical approach, possible features with regard to data integration receive through getting benefit of the reasoning and Semantic abilities associated with OWL ontologies. Within DLs, concepts are divided in to two segments: (i) TBox which usually consists of terminological (intentional) information by means of terms which identify common properties of the concepts; and (ii) ABox of which includes assertional (extensional) knowledge particularly to the individuals associated with a domain. This is essential in order to identify which concept of information complexity. As recommended by Horrock [1], inside that the particular conclusion will be that ABox. Similarly, the idea that the query method developed via knowledge base implies that the reasoning issue of querying a particular knowledge is examined.

To decide whether the KB is entailed for a particular sentence, we check the logical inference for every concept in KB and enumerate the true [30]. However, on the way to achieve this goal, this work tests three aspects namely: equivalence, satisfiability and validity.

In order to compare queries in ontologies, it is very important to manage predicates (unary and binary) within the query expressions that match classes and relations in the ontology. This provides the tractability of the KB of a particular domain.

Now, applying the conjunctive queries over the ontology (definitions 1 and 2 above) and the ontology concepts (Figure 1.2), we have the following:

Q(X) ← MetOceanSea(X) ⋀hasMetoceanSeaPlaces(X,A) ⋀ hasSeaPlaces(X,B) ⋀ hasTimeSeriesData(X,C) ⋀hasOceanographicParameter (X,D) ⋀ A = Carigali ⋀ B = SeaLevel ⋀ C = WindParameter ⋀ D = Petroleum

Figure 1.3: Conjunctive query in clausal form

The query asks for MetOcean’s type of sea that can be found in sea places particularly in Carigali branch which has the characteristics of sea level and wind parameter.

To translate the query into an equivalent concept expression in oil and gas domain, we categorize the concepts and employ normal inference techniques to verify if the individual can be an instance in the query or not. The process depends on the point that binary relations in the conjunctive query result in an existential restrictions.

Theorem 1(Role Role-Up (Horrock and Tessari, 2000 cited in [1])

Let (C, R, A) be a DL knowledge base with concept definitions C, relation definitions R and assertions A. Further, let R be a role, C1 concept names in T and a, b individual names in A. Given a new concept name Pb not appearing in T, then:

(C, R, A) ╞ (a, b) : R b: C1 . . . b : Ck

Iff:

(C, R, A ∪ {b : Pb}) ╞ a : ∃R (Pb ∩ C1 ∩ … ∩ Ck)Thus, Figure 1.4 below is the result of applying translation

and query roll-up techniques of our conjunctive query example.

(MetoceanSea ⊓ (∃ hasMetoceanseaplaces.{Carigali}) ⊓ (∃ hasSeaplaces.{SeaLevel}) ⊓

(∃ TimeSeriesData.{WindParameter} ⊓ (∃ OceanographicParameter.{Petroleum}))

Figure 1.4: The representation of ontology concept

The above has been tested in Protégé 4.1 through making query as concept MetoceanSea from the MetOcean’s ontology using necessary and sufficient condition that identifies the query terminology. The reasoner is then used from the protégé to develop inferred hierarchy. See Figure 1.5 below:

Figure 1.5: Query and other conceptual interfaces

This shows that the sequence is reduced to knowledge base (KB) satisfiability test, transform query into concepts by “rolling up” method. But the approach can contribute to the

problem of determining relevant information in different information sources, if the query result is too large. This is also found in [1] experiment. Thus, the knowledge acquisition instrument allows experts annotate new knowledge, create instances and manage the database.

V. CONCLUSION AND FUTURE WORKS

While the conjunctive queries aid the application as well as computation. The application receives queries from users and returns results to them. Consequently, in particular, we present MetOcean’s ontology as the key structure in designing and developing the Semantic Web in oil and gas environment. In this, it builds and identifies the relationship between MetOcean and other oil and gas sectors: weather forecast data, time series data and so on. Each instance of this ontology represents the process or experiment research via a query language. For example, “Which sea is related to Carigali?” The map represents the relationship in which it enables the visualization of the existing knowledge graphically.

In general, applying conjunctive queries in logics and reasoning aspect would lead us to the real query answering techniques. Also, the omnipresence of heterogeneity in Semantic Web can be structured using logical reasoning at the World Wide Web level. Admittedly, we point that computations using conjunctive queries are good options to enable tractability, satisfiability and decidability in DLs as well as reasoning systems.

Many research lines could be followed in future work. Firstly, annotation and comparing of ontologies will be governed soonest possible. Secondly, our application domain will concentrate on the integration of fuzzy logic and structured logical recognition in an ocean-based Semantic information system. This will reduce the complexity within the database as well as computational environment. Finally, an interesting future line of the research is the creation of reasoning algorithm that will work with fuzzy logics.

ACKNOWLEDGMENT

The authors would like to thank Universiti Teknologi PETRONAS for funding the presentation of this paper at the conference.

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