Ontology-based Standardization on Knowledge Ex-change in Social Knowledge Management Environments

Stefan Thalmann University of Innsbruck

Innsbruck, Austria Stefan.thalmann@uibk.ac.at

Isabella Seeber, Ronald Maier University of Innsbruck

Innsbruck, Austria

isabella.seeber@uibk.ac.at ronald.maier@uibk.ac.at

René Peinl Hof University of Applied Sciences

Hof, Germany rene.peinl@hof-university.de

Jan M. Pawlowski University of Jyväskylä

Jyväskylä, Finland

jan.pawlowski@jyu.fi

Lars Hetmank, Paul Kruse TU Dresden

Dresden, Germany lars.hetmank@tu-dresden.de paul.kruse@tu-dresden.de

Markus Bick ESCP Europe Berlin

Berlin, Germany

markus.bick@escpeurope.eu

ABSTRACT After the hype around Web 2.0, organizations have revisited their efforts on knowledge management in order to create beneficial effects from their employees’ activities in social environments. While there is not much doubt about the positive effects on crea-tivity and flexibility, handling the variety of knowledge work environments that are in use at different organizational units or business partners can negatively affect productivity. While stand-ards have generally mushroomed in the IT domain, there is a lack of standards in knowledge management. This paper argues for a standard on exchanging knowledge between social environments. We revisit related work, particularly standards which can be re-used for knowledge exchange, define a set of concepts that help to address the problem of exchanging contextualized knowledge as represented in social environments and present a first step towards formalizing the set of elements in an ontology.

Categories and Subject Descriptors H.3.4, H.3.4, I.2.4

General Terms Management and Standardization

Keywords Social software, knowledge management, context, metadata

1. INTRODUCTION The emergence of social software applications involves an ex-tended understanding of knowledge management (KM) and cre-ates new challenges for organizations [31]. Social software (SSW) is intended to support the communication and the collaboration among humans. Examples for SSW are social networks, blogs, wikis or social bookmarking tools. The four KM focus areas peo-ple, information management/IT, enterprise effectiveness and intellectual assets [41] have been revisited taking up on social relationships, collaboration and their effects on creativity, flexibil-ity and performance. SSW changes the way knowledge workers

interact with each other and the way information and communica-tion technology (ICT) is used [55].

In an increasingly rich SSW environment, employees can collabo-rate regardless of organizational hierarchies at any time. SSW tools enable a richer capturing of context in which content has been produced, modified and used [30]. Context in this regard is everything describing a situation. This context is considered bene-ficial for KM activities as ongoing knowledge activities can be captured and knowledge loss can be reduced [25]. Furthermore, if the knowledge worker’s context and needs are not considered, motivation, success and acceptance may decline [12]. Hence, this contextual information is considered beneficial for further usage and development of knowledge as well as for KM activities and it is highly necessary to be able to re-use contextualized knowledge.

However, it is currently not represented well in such systems and does not exploit the opportunities for further improving and sus-taining KM activities [44]. One major issue in this regard is the missing standardization [30] which hinders the exchange of con-textual data and their usage within KM systems. There is currently no standard to exchange contextualized information between such diverse systems and activities. Unfortunately, the KM community seems to have neglected standardization and standards [3] while other communities have invested into such efforts, e.g., the e-learning community or the Semantic Web community. As stand-ards aim at creating exchange formats and guidelines for interop-erable solutions, a specification which allows representing the creation context of knowledge as well as the exchange of knowledge activities between different (SSW) systems is needed.

Some related standardization approaches in the field become available. However, they are scattered and provide no holistic picture of the knowledge workers context. The main objective of this paper is to show how existing standardization approaches can be integrated by employing a KM ontology. In this regard, we show how standards and specifications that are already in use in other areas can be reused to capture information in KM settings. This paper intends to be a first step in the direction of a standard and should highlight the relevance, the need and the feasibility to build an application profile out of existing standards.

This paper is structured as follows: at first, a case example illus-trates the need of our approach and shows the selection of metada-ta elements. The related work and the main concepts of the con-ceptual model are introduced in the third section. In the fourth section, the main elements of the ontology are proposed out of

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existing standards. Afterwards, the resulting ontology is presented and its implications for the case example as well as for the further standardization process are discussed.

2. CASE EXAMPLE John from Dublin, Igor from Omsk, and Mia from Rio de Janeiro got to know each other during a trade fair and are now jointly working on a conference paper. In order to write the paper, a number of knowledge activities have to be performed. First they perform a literature review to identify related work and learn from other approaches. Then, they put the pieces together into a unified model that they want to formalize in an ontology and present in visual form like an enhanced database diagram. Finally, they have to describe their work in an 8-page manuscript and present it at a conference using some slides.

This involves a number of KOs created during this process. John creates a table listing of all the related work and their contribu-tions. Igor creates a first sketch that shows the relations between several concepts found in the literature. Mia develops a case ex-ample which is from the organizational knowledge base of the company she works for. The case example helps discussing the validity of the concepts, but not all of these KOs are included in the final paper.

The KOs are not created in a single step, but usually involve an iterative approach with multiple cycles of extending, reformulat-ing, correcting, and restructuring the first draft. This is supported by video conferences, chats, and e-mail conversations. Examples of KTs that occur are “Mia created a first draft of the case exam-ple” and “Igor created 12 comments in 3 areas of the conference paper”. Since KTs are not flat text, but structured objects, the latter could be detailed on click to “Igor created 3 comments for the case example, 5 comments for the abstract and 4 comments for the discussion”.

There are also some artifacts stored within local systems like the bibliography, e.g. in a local Zotero library, or in public systems unrelated to their project workspace like some social bookmarks that were created during the initial literature investigation, or re-cordings of some conference calls they did in FlashMeeting. All of them are part of the creation context.

Now consider that the team meets Jie from Shanghai after their presentation on the conference and based on her related research interests and complementary competencies, they decide to include her in the team and to jointly work on a follow-up paper for a well-known journal. How does Jie get all the information she needs to get up to speed and integrated into the group? This is where our proposed KC comes into place. It stores the KO, i.e. the conference paper, together with the KBs of each sub KO, so that Jie can learn about who created what KO. Since KTs are time-ordered she can rewind the creation process and replay it step by step. Even if some locally stored artifacts are not included in the KC, she has at least a reference to them and learns about their existence. References to public sources can directly be followed, so that she, e.g., can listen to the flash meeting recording and re-trace the discussion that was held a month ago.

3. RELATED WORK Online platforms in general and SSW in particular enable knowledge building on the individual and social level. In these virtual environments, knowledge gets manifested through a varie-ty of contents and online discourses [21]. The manifestation of knowledge is a complex phenomenon which relies on concepts

such as knowledge activities and knowledge objects (KO) as car-riers of knowledge.

As for the concept of knowledge activities, research has not yet agreed on a common comprehension which leaves this concept to be poorly understood in terms of different levels of granularity and scopes. Types of knowledge activities comprise, e.g., knowledge identification, creation, distribution (sharing), matur-ing, adoption and utilization. However, these are just a (common) subset [23] [34][45]. Knowledge sharing – as an integral activity – is a complex phenomenon which is challenging as knowledge held by a person cannot be completely codified and shared [27][29]. Moreover, codified knowledge can only be efficiently shared using IT, when contextual information as well as potential adaptation requirements are known about the collaborative setting or situation [11][18] in which the knowledge has been created or successfully applied. Especially for non-routine tasks, to which knowledge work can be counted, IT tools need to provide at least partial or fragmented context to enable knowledge building [38]. With regard to the understanding of knowledge as an object or product, which is situated and relational, it is associated with cur-rent context and activities as well as with historical context and activities [33]. KOs can be regarded as combination of knowledge content and metadata [24].

Knowledge work supported by SSW provides the advantage that the SSW environments broaden and deepen the range of user par-ticipation in sharing and acquiring new knowledge. SSW delivers both, maintenance of strong ties to frequent communication part-ners, as well as weak relationships which span sub-networks or communities [20][40]. The strong user participation in SSW and the direction of collaboration can be guided and used for the pur-poses of KM [36]. This enables and facilitates the sharing of knowledge and the construction of new knowledge through social interaction in organizations [29][49]. Even though current SSW applications offer a wide range of contextual information through users’ content metadata, there is still room for further improving and sustaining knowledge sharing along the knowledge building and maturing process. The increasing volume and complexity of information available in the Web as well as the proliferation of ICT emphasize the need for more efficient ways of sharing knowledge. To trace the creation and maturing history of a knowledge object along time-ordered entries in an activity stream could be an important help for understanding how knowledge maturing is happening in an organization [33][36].

One main concern in this regard is that knowledge sharing is handicapped by missing standardization streams which would enable and ease the capturing of relevant information for knowledge work in SSW. To the knowledge of the authors, there is neither a standardization effort, nor any kind of specification that covers a broad range of concepts and systems necessary to be applicable to knowledge sharing in SSW.

Due to the variety of methods, instruments and tools, there are only a few domain-specific standards in KM. Holsapple and Joshi already proposed a KM ontology in 2003, but it did not manage to become a widely used one nor a standard [1]. What they name KM episode roughly equals to the term KM activity used here. Although they are also considering a knowledge resource, their ontology has more an organizational focus and deals with influ-ences on KM episodes instead capturing what’s actually happen-ing. Abecker and van Elst do also talk about ontologies for KM but without getting concrete and proposing an own one [22]. Therefore, only metadata standards, processes and activities as well as Semantic Web standards [3] are relevant for KM settings

applying SSW. In addition to that, technical standards support document-oriented KM (such as document formats, metadata) as well as guidelines and good practices address human-oriented aspects in standardization institutions [34]. According to [44], it is of vital importance to combine these with technical base standards from similar domains which can serve as a starting point or candi-date standard. This becomes even more important as in general standards to describe documents or digital objects are rather ma-ture such as Learning Object Metadata (LOM) or Dublin Core (DC). Furthermore, base standards for semantic relations between aspects of the frameworks are also widely used and mature as well. Hence, in the following we identify concrete aspects from existing standards which are suitable to represent contextual in-formation in collaborative knowledge sharing activities and to discuss the applicability for a case. In this regard, we detail the following conceptual model proposed by [8] which is depicted in Figure 1.

Figure 1: Overview of the proposed terms and concepts

The knowledge trace (KT) as the central recording unit for con-textual information is defined as a codified representation of a user’s action within a knowledge activity (KA) that is recorded in an activity stream and captures contextual information. A knowledge activity stream (KAS) is a time-ordered list of entries that describes events that are the result of actions within knowledge activities that the user was involved in or monitors. It is a user-centric view of what has happened, i.e. the events that happened during the processes of, e.g., creating, sharing, adopting and using KO. A KA is a set of goal-directed actions within a user’s context which is in line with activity theory [15]. The ac-tions change the state and existence of KOs dynamically. KOs are electronically accessible and reusable externalized pieces of knowledge which are not restricted to a certain media format and which can be combined with other KOs [34][53]. In other words, KOs can relate to documents, pictures or even paragraphs of writ-ten text and tables. Knowledge bundles (KB) represent an object-centric view which is well-suited for transferring a KO together with valuable context information that helps during the reader’s reconstruction of the knowledge. A KB is a collection of KTs affiliated to a KO. The KTs can origin from multiple users. There-fore, KBs represent a KO-centric activity stream.

Currently, related container standards like SCORM or OpenDoc-ument are specified using XML Schema. This allows for precise descriptions of data types and easy transformation from one for-

mat into another [28]. However, plain XML data has some short-comings compared to semantic data expressed in RDF/XML that references an OWL ontology. While XML data can be trans-formed from one schema to another using XSLT, OWL mappings allow for reuse of data without transforming them, because they are understood on a semantic level. Due to this easy mapping, it is recommended to reuse well-known RDF vocabularies such as FOAF, SIOC, SKOS, DOAP, vCard, Dublin Core, OAI-ORE or GoodRelations [4]. We therefore do exactly that. Instead of mix-ing a lot of vocabularies, we propose to create an own vocabulary and providing mappings to related standards like suggested by the Linked Data initiative [57]. Linked Open Data currently suffers from poor performance, since most links are established on an instance level rather than the schema level [26], although [42] show, that the latter can be generated based on the instance level links. We provide links directly on the schema level from our proposed ontology capturing metadata on knowledge containers (KCs) to other standards in related areas. Some of those related standards are themselves expressed in OWL (e.g., Proton), so that we can provide direct links using owl:equivalentClass predicates. Others are specified in XML Schema (e.g., SCORM) or JSON syntax (e.g., activitystrea.ms). In those cases, we provide annota-tions in our ontology that point to equivalent concepts from these standards. Our standard does not directly become part of the LOD cloud, since so far we are not directly publishing the data, but only our schema. Nevertheless, this effort contributes to better interop-erability in the Web of Data.

4. ELEMENTS OF THE ONTOLOGY In this section, we introduce the components of the ontology by describing the main elements for each concept, as depicted in the conceptual model in Figure 1. These main elements are based on existing standards and specifications and their relevance is sup-ported by relating them to the case example as described above. In other words, the main elements are selected for the purpose of our case example and will be used to create the ontology. Administra-tive elements such as identifiers and URLs are presupposed and not discussed in the following. It should be noted that the goal of this selection is not to create a generalizable set of elements, but rather to show the feasibility of our proposed conceptual model. The negotiation of the most important and appropriate elements then should happen in the standardization process itself.

4.1 Knowledge Object The KO is the smallest explicit piece of documented knowledge [53]. The characteristics and the suitability of KOs for certain contexts has to be described in order to facilitate their usage [19] Hence, KOs can be regarded as amalgamation of documented knowledge and its associated metadata [24]. Standardization of metadata in the field of documents has a long tradition starting with standards from librarianship such as MARC21 over generic standards such as DC to domain-specific ones such as LOM. A common element in many standards is the person who created or published the KO. Also in our case, it is crucial for all team mem-bers to know which person created a KO. A title helps identify the document. An appropriate relationship to the main concepts of the team’s shared understanding of concepts is crucial for purposes of search and analysis. The model of the shared understanding of concepts, is ideally represented in an ontology. Then, KOs should be related to these main elements of the ontology to enable a se-mantically supported search and analysis of these KOs. In current

standards, this is typically done by using keywords. In our case, the keywords should be related to the knowledge ontology of the research community. The reuse of pieces of externalized knowledge frequently happens in our case example as well as in real world knowledge work situations [39]. Legal aspects are cru-cial when Mia develops the case example from KOs of the com-pany she works for. Hence, a description of legal aspects of reuse (rights) is needed for the description and this could be related to license concepts taken from digital rights ontology [16]. Finally, technical descriptions of the KOs are needed to process the KO in an automated way.

Table 1. Elements for knowledge object

creator (person)

LOM(contribute),DC(creator),MARC21(100, 110, 111, 700, 710, 711), TV-Anytime(content creator), DITA (author)

title LOM(title), DC(title), MARC21(245,246), DITA(title)

keyword (topic)

LOM(keyword), DC(subject), MARC21(050,060), TV-Anytime(descriptor), DITA(keyword)

rights (li-cense)

LOM(rights), LOM(copyright), SCORM(rights), DITA(copyright)

technical description

LOM(technical), SCORM(technical), DI-TA(technical)

4.2 Knowledge Activity KA represents a multifaceted concept that describes some kind of performance. The particular challenge of this element is that hier-archies of user actions or activities need to be represented. As in our case example, the knowledge workers perform activities such as doing a literature review, creating an artifact or co-authoring a paper which can be understood to be more coarse-grained activi-ties than extending, reformulating, correcting or restructuring their co-authored paper. Many research studies [17][48] are struggling with ways how to reason from low-level actions such as read, open or copy to higher-level actions such as distribute, develop, network or even general task descriptions such as writing a con-ference paper or the like [46]. The central element of KA is a specification of an activity and is composed of actions that are represented on the lowest level of abstraction, e.g., insert a pic-ture. Each activity includes an activityDescription which further specifies the activity. Low-level actions are currently taken from the User Interaction Context Ontology (UICO) and comprise e.g., create, post, replyAll, forward, or forwardAsAttachment. But also other approaches such as Contextual Attention Metdata (CAM) [56], the Task Management Ontology [17], or the information model of IMS learning design [32] include the concept of action and activity. To state the level of abstraction, we include the ele-ment dependency(activity) that specifies the hierarchical position of an activity related to another activity as described in [6].

Additionally, the KA is characterized by the element activityState which provides information on its condition which means whether the activity is running, has finished etc. For this purpose, we add he element activityState which specifies states such as TaskState-Created, TaskStateRunning or TaskStateTerminated [46].

You can further see from the case example that some activities run in parallel and are composed of other actions. For example, John creates a table including relevant concepts found in literature while Mia develops a case example. They all follow the goal to write a paper collaboratively. At the same time, dependencies exist such as that Igor can only make a sketch for relevant concept

as soon as John has started performing a literature review. Like-wise, all their activities are characterized by start and end times of their works.

Table 2. Elements for knowledge activity

activity UICO (eventType), TMO (pi-mo:Task), CAM (action), IMS LD (activities)

activityDescription TMO (tmo: taskDescription), IMS LD (title, activity Description)

dependency(activity) TMO (tmo:SuperSupTaskDependency)

activityState UICO (TaskState), TMO (tmo:TaskEffort, tmo:TaskState), CAM (duration)

4.3 Knowledge Trace A KT is a representation of a user’s action within a KA that is recorded in KASs or can be aggregated within KBs from a rather object-centric view and affiliated to a KO. Hence, the present specification must consider these low-level actions as building blocks for the above-mentioned concepts and provide suitable representations usually in terms of an actor – verb – object triple where activity verbs are used as types of activities (e.g. to post, or to bookmark a specific object [54]).

The Upper Tag Ontology (UTO) can be used as a starting point [13]. UTO formalizes social tagging actions based on a small ontology of eight concepts that denotes the tagging activity relat-ing an object with a number of tags at a given date by a tagger. TV-Anytime, a specification from the domain of multimedia con-tent delivery, strongly focuses on recording user interactions and, e.g., allows the description of relevant chunks within a stream and how these chunks can be related to users [14]. MPEG7 [10] also describes user interactions. It allows to create a usage history per-taining to the consumption of the multimedia material. All in all, only broad specifications for general context or specifications from specific domains are available. Hence, we identify suitable representations in existing standards in order to apply them in our specification.

KTs record events that can be described by an actor (person or organization) who performs an action and thereby contributes to a KO (e.g., Mia creating her draft). When Jie wants to rewind the creation process of Mia’s final conference paper, she has to rely on timestamps that allow her to follow the KTs step by step. She also might want to analyze when and where one event occurred in a time-ordered chain of events. Therefore, our specification pro-vides information about date, including a duration and a related location. Furthermore, KTs exercise their influence on specific targets and are connected to systems. A target is either a KO, a KC or an information system that was altered through a KT, e.g., when Igor added a comment to a chapter of the paper. The system describes the environment (i.e. information system) in which an event occurs or from which it is relayed. Igor, e.g., uses his text editing tool to add annotations.

Current standardization efforts already provide metadata which capture such contextual information. In order to describe the actor who contributes to a KO, standards, such as LOM, DC, ATOM, suggest the specification of a contributor or creator (see Table 3). We suggest to build on the verbs that are provided by activ-itystrea.ms for describing actions (i.e. Activity Base Schema). System and target can be found in the specification of JSON Ac-tivity Streams that detail the serialization of a stream of social

activities. Duration and time are applied in standards, such as DC, SCORM and UTO where date or contribute:date are specified. Furthermore, ATOM differentiates between a publishing date (atom:published) and the latest update (atom:updated) referring to Web feeds [43], while activitystrea.ms (i.e. Activity Base Sche-ma) considers startTime and endTime allowing to track the dura-tion of a KT. The Activity Base Schema from Activitystrea.ms also suggests the description of contextual information for the location [51]. Here, we find a specification that depicts the loca-tion or venue of an event including, e.g. street address, a free-form location name or any combination of these.

Table 3. Elements for knowledge trace

actor (person) LOM (contribute), DC (contributor), TV-Anytime (CreditsItem), SCORM (lifeCy-cle:contribute), MPEG7 (Creator), ATOM (atom:contributor)

action activitystrea.ms

system (system) activitystrea.ms

target activitystrea.ms

date SCORM (lifeCycle:contribute:date), UTO (date), ATOM (updated)

duration activitystrea.ms, hCalender (duration and dtstart/dtend)

location from activitystrea.ms, hCalender (location)

4.4 Knowledge Activity Stream and Knowledge Bundle (KB) “A user’s social activity stream can be seen as a historical dataset from which context-sensitive items can be derived” [7]. In analo-gy to that, we define a KAS as a time-ordered list of events that are the result of actions within knowledge activities that the user was involved in or monitors [3]. It is a user-centric view of what has happened, whereas the KB is an object-centric view of the KTs. As Figure 1 suggests, there is not much difference in the KAS and the KB besides the filter that decides which KTs get part of the KAS or KB. We highlight the KAS, since this what is cur-rently used in SSW system, whereas the KB is what we propose as useful for our container standard. In Igor’s KAS for example would be KTs about his teaching activities and from other col-leagues at his university, which are not part of the KB [50] stress, that social embeddedness and social context are crucial for work-place learning and therefore events that occur within learning activities have to be recorded.

Table 4. Elements for KAS and KB

title CAM dateAdded, dateRemoved CAM lastRead, readTimes CAM category (ontology concept) ATOM generator (information system) ATOM contributor (person) ATOM rights ATOM

The obvious equivalents in existing standards are RSS or ATOM feeds, or one of their extensions. Despite its name, the activ-itystrea.ms standard does not directly define an equivalent con-struct. Instead, it uses a generic collection object which holds items of type activity to do the job. The only metadata element defined is a URL for the collection and the total number of items. Nevertheless, it is suggested for reuse in other papers [54]. The CAM standard uses a feed element similar to ATOM and has

much more describing properties like title, dateAdded, lastRead and readTimes. ATOM adds a category, contributor, generator and rights element, which should be re-used.

The KB for the conceptual model could reference the ontology concepts drawing, ontology and KC as category metadata, the rights could point to a creative commons license, the contributor would be Igor and the generator could point to a social platform like Liferay or IBM Connections.

4.5 Knowledge Worker The knowledge worker can be involved in several KAs in which he applies a set of goal-directed actions to one or more KOs. The microformat hCard, the person type of schema.org as well as the person class of FOAF provide a basic set of general properties, such as name, address or group membership, to define a knowledge worker [5][9]. Additionally, the microformat XFN which outlines different types of relations in a social network, as well as the special affiliation types of the PROTON ontology allow a detailed description of the knowledge worker’s social network [8][52].

One of the basic elements to identify a knowledge worker is the personal name. Another important property, which should be con-sidered for our concept of the knowledge worker, is the address that describes the place (or places) at which the knowledge worker is located professionally. When referring to our case example, Jie from Shanghai might want to get in touch with Mia from Rio de Janeiro after they have met at the conference. For that matter, she has to consider that Mia lives in a different time zone.

Knowledge work is mainly characterized by ill-structured non-routine tasks in complex domains and often requires creative problem-solving abilities and specialized knowledge [37]. We propose that the concept should contain such information about skills, expertise or interests capturing stronger and weaker notions of ascribed competencies, so that other knowledge workers in the same or related KAs can get a deeper understanding under which prerequisites KOs were created. For example, Jie wants to screen and judge the research work, which has been already done by John, Igor, and Mia. Therefore, she filters all KOs of the KC that match her interests, skills, and expertise.

Furthermore, knowledge is mainly generated through social inter-action between knowledge workers who are members (member-ship) of formal organizational units or of informal knowledge communities (e.g., community of practice or interest). Thus, our ontology needs to record information to whom and how the knowledge worker is related. For our standard, we have to specify carefully which type of relation is needed and which can be drawn based on logical reasoning.

Table 5. Elements for knowledge worker

name name foaf:person (lastName), hCard (fami-ly-name), schema.org:person (name), activ-itystrea.ms:person (displayName)

address (ad-dress)

schema.org:person (address), proton (lo-catedIn)

expertise (skill, interest)

foaf:agent (interest), protont:person (hasProfession)

membership (community, organization)

schema.org:person (affiliation, alumniOf, memberOf, worksFor)

knows (person) foaf:person (knows), XFN (friend, col-league), protont:person (isBossOf)

4.6 Knowledge Container A KC holds both KOs and their corresponding KBs with the pur-pose of improving knowledge transferability. It can be seen as a compound document, with additional context information on mul-tiple aggregation levels [3] and therefore must not be mixed up with the homonym from the case-based reasoning (CBR) domain, where [47] defines it as containing and structuring the knowledge of a CBR system in form of a collection of knowledge that is rele-vant to several tasks instead of one.

[1] use the term KC in a very similar way in their effort for creat-ing self-organizing knowledge networks. Their KC consists of a description and a network of so-called knowledge atoms, which again consist of a description and a KO. Therefore, they require a flexible KC which implements designated functionality to (re-)configure relations of registered knowledge atoms and as such enables the construction of purpose-build collections of knowledge. They further mention purpose, validity, security and accessibility as properties of the description on KC level.

Industry standards in this area are SCORM and LOM in the e-learning community as well as OpenDocument and Office Open XML in the office software domain. Finally, in the multimedia domain, MPEG7 and XMP are used to store metadata on the con-tainer level.

OpenDocument uses metadata like initial-creator, editing-cycles, print-date and printed-by. Office Open XML adds category, key-words and contentStatus metadata. The latter defines, whether a document is approved, in draft status, or under revision and seems relevant for our purpose [34] as a distinction between formal, approved and informal, unapproved knowledge. SCORM defines a container for transferring learning objects. The metadata about the structure of the contained objects [1] allows distinguishing between atomic KOs, collections, networks, hierarchies or linear sequences of KOs. The conference paper from our example would be a linear sequence of KOs, although there is also a hierarchical aspect since sections can contain text and a figure or table. Se-quencing rules further describe the relation between KOs, e.g., some KOs may be prerequisites for others. Furthermore, the ag-gregation level describes the granularity of contents from a frag-ment over a lesson and course until a collection of courses.

Table 6. Elements for knowledge container

collection structure MPEG7, LOM aggregation level LOM sequencing rules SCORM SS relation to resources (contents) SCORM security [2] content status Office Open XML

5. DISCUSSION OF THE ONTOLOGY After introducing and discussing the main elements of our ontolo-gy, the following figure summarizes our results (Figure 2). The ontology consists of main classes with their data and object prop-erties. Our main entities are represented in ellipses with bold font and comprise knowledge object, knowledge worker, knowledge activity, knowledge trace, knowledge activity stream, knowledge bundle, and knowledge container. Shaded ellipses denote other classes, whereas white ellipses denote data properties of the on-tology and hence attributes of a class. Finally, the arrows indicate the object property and hence the relationship among class and data property.

Figure 2: Ontology for capturing context in SSW

The introduced ontology with its classes, data and object proper-ties can be used to integrate metadata from different SSW envi-ronments. The properties represent metadata descriptions and data which can be collected in various tools. Metadata about the knowledge worker for example, could be collected in professional business networks such as XING or LinkedIn. Here, the knowledge workers’ expertise, his relationships and his location could be extracted for example. Descriptions about the KB and KOs can be stored in collaboration platforms such as SharePoint or document sharing platforms such as Dropbox. Knowledge ac-tivities then can be deduced based on information captured in tool logs. Collaborative writing can happen in docs.google.com or etherpad, joint communication with a collaborative whiteboard or document transfer in Skype. With our exemplary set of metadata elements, descriptions about the knowledge activities of our group of knowledge workers could be collected from these applications.

The value of the ontology is that we can now easily integrate data collected from different applications within one or several organi-zations with other data published on the Internet and furthermore automatically import them into a target IT infrastructure. This integrated view on the happenings of our group from our case example could be useful to make the creation context of knowledge or the collaborative group processes traceable and understandable. This is important for the group itself, for new members such as Jie, other groups working on similar problems or towards similar goals or knowledge managers guiding the team. Creating awareness and transparency of knowledge activities in the context of group collaboration is considered beneficial for KM and the contribution of our approach from an organizational point of view. This rich contextual view on knowledge and especially knowledge related activities seems also very promising to foster the organizational knowledge maturing [34]. From the perspective of KM systems, our approach allows to bridge heterogeneous tools and systems and thus create a loosely coupled inter-networked environment for handling of knowledge in teams. Due to standardization of contents and context, these environments can be adapted flexibly. This allows for agile practices in organiza-tions and beyond the organizational boundaries in which individu-al knowledge workers are members of manifold formal and in-formal collaboration contexts between which they can switch without having to spend extra effort that so far hampers productiv-ity of knowledge work.

6. CONCLUSION This paper addresses a remarkable gap in KM research and prac-tice: the lack of standards that explicitly address the needs of knowledge work. While standards have been propelled in many other domains, the KM discipline is a late mover which, however, can benefit from already accomplished standards. We encourage academics and practitioners in KM alike to engage in standardiza-tion efforts and put forward their ideas on what knowledge work-ers need in order to drive innovation collaboratively avoiding negative effects on productivity and efficiency. The paper identi-fies seven major concepts that are needed to foster knowledge exchange between social environments. These concepts are for-mally described and related to already finalized standards. The interplay between the concepts has been illustrated with the help of a case example. In some cases (especially on context represen-tation), new specification efforts are necessary. As a next step we plan to collect examples from different application areas, describe those with our proposed standard and try to identify a common subset of metadata that is needed in every example.

From a standardization perspective, we see that the field requires anticipatory standards to advance within the community. We sug-gest to launch an interest group on a pre-standardization level (e.g., within the European Standardization Body CEN) consisting of stakeholders in Knowledge Management, Learning Technolo-gies, and in related domains. We see the current proposal incorpo-rating a variety of mature standards as a starting point for dis-course on the development towards a potential standard.

Acknowledgements This research has been co-funded by the European Commission under the Information and Communication Technologies theme of the 7th Framework Programme, Integrating Project ARISTO-TELE (contract no. FP7-257886), Integrating Project MATURE (Contract No. 216356) and within the eContentplus targeted pro-ject OpenScout, grant ECP 2008 EDU 428016.

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