Ontology-enabled Knowledge Management at Multiple Organizational Levels

Transcription

1 Ontology-enabled Knowledge Management at Multiple Organizational Levels Dimitris Apostolou, Gregoris Mentzas and Andreas Abecker Abstract Despite the relative maturity of knowledge management, the problem of facilitating knowledge creation and sharing at multiple organizational levels in a uniform way is still crucial. Moreover, managing knowledge at each organizational level implies a number of critical heterogeneities (representational, semantic, etc.) which current solutions do not address adequately. In this paper we present a faceted which addresses the content, context, community, domain and business issues of knowledge management. We describe the, its applicability and its benefits in three Knowledge Management System implementations. Index Terms Knowledge management, Knowledge management system K I. INTRODUCTION nowledge Management (KM) has expanded its focus of attention, gradually moving from internal to external and from organizational performance to personal and team productivity [2]. Research and industrial practice of KM focuses on one of four different levels: personal; team; organizational and inter-organizational [15]. These levels correspond to the ontological dimension that, in Nonaka and Takeuchi s [17] model of organizations as knowledge creating mechanisms, refers to the social interactions which begin at the individual level and then by communication between organizational boundaries let knowledge expand and ameliorate. The individual or personal level refers to activities such as acquisition, organization, maintenance and retrieval of knowledge that are performed everyday by knowledge workers in order to manage their personal information and knowledge spaces; such activities correspond to personal information and knowledge management [12]. The team level refers to communities of knowledge workers in which knowledge sharing occurs as a primary activity. Knowledge networks can be informal, i.e. self-organizing Manuscript received January 25, D. Apostolou, Informatics Department of the University of Piraeus, Karaoli & Dimitriou 80, Piraeus, Greece ( dapost@unipi.gr). G. Mentzas, School of Electrical and Computer Engineering of the National Technical University of Athens, 9 Iroon Polytechniou str., Zografou Campus, , Athens, Greece (corresponding author to provide phone: ; fax: ; gmentzas@mail.ntua.gr). A. Abecker, Forschungszentrum Informatik, Haid-und-Neu-Str , Karlsruhe, Germany ( abecker@fzi.de). teams of knowledge workers with common interests such as communities of interests or formal such as project teams and communities of practice involved in similar activities within an organization [4]. The organizational level refers to the development and exploitation of an organization s tangible and intangible knowledge resources which must be managed well and put to best use. Organizational KM is concerned with realizing the value of this intellectual capital, which exists as: tangible assets (such as R&D work, patent licenses and information held in databases on customers, suppliers, products and competitors, etc) and intangible assets (such as the skills, experience and knowledge of people within the organization). The inter-organizational level refers to value added networks where collaborating partners acquire integrate and benefit from external knowledge, competencies and capabilities. Moreover, this level includes inter-organizational knowledge exchanges [18] that provide the means to organizations to offer their knowledge as a product to customers. Besides knowledge management at each of the aforementioned levels, one should note the importance of knowledge interactions across the levels. As knowledge at one level interacts with that of another, new knowledge is created [9]. For example, collectives of individuals such as teams provide the context in which individuals tacit knowledge can be pooled and recombined to create group-level knowledge [5]. As this new knowledge is used, some of it is formalized and codified in systems and technologies, while some becomes embedded in organizational routines. This wide application of KM across multiple organizational levels challenges the capabilities of Knowledge Management Systems (KMSs) to provide seamless access to the whole wealth of explicit and tacit knowledge residing in individuals and personal computers, groups and group support systems, as well as organizational and inter-organizational repositories. Moreover, this must be done in situations which involve manifold dimensions of heterogeneity such as: system heterogeneity (e.g. among legacy systems and software applications); representational heterogeneity (the same content may be represented in manifold forms and stored in different media types); semantic heterogeneity (e.g. using different words to refer to the same concepts); and knowledge types heterogeneity (e.g., hard facts vs. rough estimations, strict rules vs. fuzzy recommendations, shallow brainstorming vs /08/$ IEEE

2 30 2 validated experience, etc); and knowledge content heterogeneity (e.g. product knowledge, know-how, market knowledge, competitive intelligence, etc). In this paper we present how semantic technologies can enable KMSs to deal with knowledge heterogeneities, to facilitate seamless access to explicit and tacit knowledge and to support the evolution from organizational to personal, team and inter-organizational KM. Section II describes our proposal for a faceted for describing knowledge objects, while section III presents the validation of our approach in research systems for organizational KM (Know-Net), for personal and team KM (IKOS) and for inter-organizational KM (INKASS). In section IV we discuss the benefits of our approach, while section V presents our concluding remarks. II. A FACETED ONTOLOGY FOR DESCRIBING KNOWLEDGE OBJECTS Ontologies are increasingly seen as a key semantic technology for addressing heterogeneities and mitigating the problems they create and for enabling semantics-driven knowledge processing [13]. Ontologies are formal structures enabling acquiring, maintaining, accessing, sharing and reusing information [10], [7]. KMSs benefit from ontologies that semantically enrich information and precisely define the meaning of various information artefacts. In the last decade, many projects aimed at creating ontologies for different purposes: word net [6], sumo [16], dolce [14], AIAI Enterprise Ontology [21] and the Business Management Ontology 1. Most relevant to KM is the PROTON that has been developed as a light-weight upper-level for usage in KM and Semantic Web applications [11]. In order to deal with the heterogeneities mentioned in the introduction, we developed a faceted based upon the idea of defining and managing Knowledge Objects (KOs) instead of directly managing pure content [15]. A KO contains content plus a set of metadata. From the content point of view, a KO can comprise, inter alia, a lesson learned document, a best practice template, a documented new product idea, the answer to a frequently asked question or a contribution to an online discussion. The faceted for describing KOs comprises: A specification of all attributes a KO may possess. The value ranges, and if necessary supplementing related ontologies for defining the ranges of attributes used. A specification of relationships that may exist between KOs (indicating, e.g., that some KOs could provide prior knowledge useful for understanding and applying some other KOs). The specification of aggregated KOs, which represent complex knowledge by an appropriate combination of several simpler objects. 1 The open source business management. (BMO). Available: Additional supporting data structures required, e.g., for representing contracts or transactions which are required for managing KOs in an inter-organizational scenario. The main facets of the proposed, presented in more detail in [3], are as follows: the content facet describes the core content of a KO; the context facet describes under which circumstances a KO may be used and applied in a situation at hand; the community facet addresses the whole community of systems and users interacting with a KO; the domain facet contains the domain vocabularies that describe knowledge of the domain where a KO and its description facets is situated in; and the business facet stores all data and information used to commercially exploiting a KO such as pricing information and related IPR issues. III. VALIDATION OF THE ONTOLOGY IN KM SYSTEMS A. Use of the in three KM Systems 1) Organizational Knowledge Management: The Know- Net System Know-Net is an organizational KMS, implemented as an intranet portal that supports the collection, categorization, annotation and retrieval of corporate content as well as collaboration facilities via on-line workspaces that allow corporate employees to work together on knowledge-intensive projects. Figure 1 gives a high-level schematic overview of the way in which ontologies are used in Know-Net [15]. Content is stored in native applications and systems (e.g. Lotus Domino) while metadata are stored in a metadata store. The approach is based upon a freely configurable metadata schema defined with the Ontology Editor, which is used to specify a hierarchical schema of metadata to be attached to each KO. When specifying the ranges of allowed values for a metadata attribute, the system does not only provide for simple data types like strings, dates or enumeration types but also gives the opportunity to describe the set of possible attribute values with so-called indexing ontologies. User s search interface annotation interface queries used in used in annotates fills metadata schema indexing ontologies Ontology engineer configures URI metadata store URI Domino CMS 1 CMS 2 Figure 1: Using the faceted in the Know-Net KMS editor Content CMS Management System The metadata schema is used for three purposes. First, it is the database schema defining allowed instances of KOs. The database of the Know-Net system contains an entry for each KO, which specifies its metadata values plus a link to the original information artifact (content) that may reside elsewhere (in a file server, for example). Second, the metadata

3 30 3 schema is used to configure the interface uses use to annotate KOs via the Annotation Interface. The Annotation Interface is created dynamically, depending on the current metadata schema and indexing ontologies. Third, the metadata schema is used in the dynamic generation of the Search Interface, which allows users to browse the metadata schema and specify their information needs by articulating search constraints (i.e. metadata-attribute values required for a relevant KO) as illustrated in section B below. 2) Personal and Team Knowledge Management: The IKOS System Personal KM is about letting people manage their information and knowledge in ways that make the most sense to them. Moreover, KMSs that provide shared searchable repositories of team members knowledge and mechanisms to disseminate local contextual knowledge to all team members are required in order to support KM at the team level [1]. IKOS is a KMS that is based on the Social Semantic Desktop framework [19] and which aims to provide unified access to electronic information a user uses on her/his regardless of how it was initially encountered (such as in , files, calendar information, instant messages, Web pages and digital photographs). Moreover, it provides means for extending the personal into a collaboration environment that supports sharing and exchange of knowledge across s in a peer-to-peer topology. metadata annotation user s A User s A editor local repository search interface User s B local repository corporate ontologies metadata alignment Figure 2. Using the faceted in the IKOS KMS user s B In a typical usage scenario, user A can select among existing information resources ( s, files, folders, bookmarks, images, etc.) from her local for inclusion in IKOS (Figure 2). A crawler component extracts full-text index from the selected resources while the user can add metadata to resources either manually or assisted by a metadata recommender component. For example, existing file folders and folders can be automatically associated with concepts. Resources are identified by their URIs and integrated automatically into the local repository. Metadata are modeled in an that represents the user s conceptualization of her domain. This allows a more finegrained resource classification than the one provided by most operating systems that only allow one file to exist in exactly one folder. At any point, user A can intervene and append her/his structures by adding deleting, or renaming concepts and relations between the concepts. Ontology concepts can be used for annotating resources. The is also used to generate a graphical knowledge map the user can use to browse through it. A key aspect in IKOS is the ability to import existing ontologies, such as corporate ontologies. These ontologies can then be used as templates for users to build their own ontologies upon. When searching, user s A queries are matched against both the local and the distributed indices. When the distributed index is accessed, alignment between local and distributed metadata should be performed in order to be able to retrieve content that is modeled according to different ontological structures. The main task in metadata alignment is to identify relationships between elements of the different users ontologies. These relationships are necessary to determine which actions to perform in order to create a merged. 3) Inter-organizational Knowledge Trade: The INKASS System Inter-organizational KMSs are an emerging niche in the KM market [18] that provide the means to organizations to offer their knowledge as a product to internal and external customers. The INKASS KMS aims to support the trade of explicit and tacit knowledge at an inter-organizational level. INKASS support the Information, Intention, Contracting and Settlement phases of a knowledge transaction [20]. In the Information Phase, users are able to locate KOs by expressing their needs in natural language queries. In the Intention Phase, a knowledge seeker employs a workflow engine capable of implementing a number of modes of interaction (and corresponding pricing schemes) to suit the specific knowledge exchange needs. For example, a Request for Proposal workflow can support a knowledge seeker to specify her/his requirements and get a quote from the knowledge supplier. In the Contracting Phase, which is applicable in cases where new knowledge is to be created, the knowledge seekers and suppliers negotiate contracts starting from the initial agreement reached in the intention phase through a contract editor. In the Settlement Phase, knowledge seekers and suppliers act according to the contract. INKASS provides a workspace, shared between the supplier and the seeker as a means for submitting deliverables and communicating messages related to the work specified in the contract.

4 30 4 User s search browse workspace workflow contract search, browse, buy, RFP, negotiate contract metadata, ontologies Case base cases refines Figure 3: Using the faceted in the INKASS KMS URI Administrator creates, modifies, deletes Improvement tools CMS requests new content In INKASS, each KO forms a case (Figure 3). Metadata, defined in the faceted ontologies, are used to describe a case. One KO can appear in more that one cases at the same time, similarly to a book offer that can appear in the science fiction shelf as well as the bestseller shelf. A user s query, even if formulated as free text, is automatically translated into a query case. The system then calculates how similar the query case is compared to the stored cases. The most similar the case is, the most relevant it is to the user s question. INKASS supports both automatic calculation of similarity measures from the semantic approximation of ontological concepts and manual definition of similarity measures [3]. B. Benefits of the in Know-Net, IKOS and INKASS The KM systems presented above use the various facets of our and have been piloted by a number of organizations in different application areas as shown in Table 1. Pilots ran for a period of at least six months. Pilots evaluations have been performed following the case study method. The case study evaluation method helps investigate a phenomenon in depth and provides rich understanding [22]. It refers to the identification of the key factors that may affect the impact of a system and then document the impacts, constraints, resources, outputs of the system on a set of issues under examination [8]. Qualitative data were collected through semistructured interviews while in some cases the on-site observation method was also used. In this section we provide a synthesis of the most critical benefits stemming from the use of the faceted in the KMSs, as these have been identified using the case study method. TABLE 1: OVERVIEW OF THE USE OF THE THREE KMSS KMS/Level Ontology Applications Know-Net/ Organizational IKOS/ Personal and Team facets used Content Context Domain Content Context Domain Community Banking (UBS - CH) Consulting (NAI Gooch Webster UK, Planet GR) Software development (Singular GR, Alphanova UK, DebusIT CZ, MDA - TR) Consulting (TMI UK, DK, GR) INKASS/ Inter-organizational Content Context Domain Business Consulting (Planet GR) Research and technology transfer in engineering (TWI UK) Chamber of commerce (ACCI GR) 1) Addressing Knowledge Heterogeneity A technical implementation that would deal with the system and representational heterogeneity levels of knowledge should ensure that all KOs are accessible seamlessly, independently of file formats and originating application and system. Moreover, in order to harmonize the codification and socialization approaches, applications and tools that support the socialization approach, such as groupware applications, should use KOs that are also accessible by applications and tools that support the codification approach, such as search and retrieval tools. Therefore KOs have to be represented in a way that is independent from the applications and tools that create or use them. A way to achieve this is to introduce a separate persistency layer for storing KO metadata. In Know-Net, IKOS and INKASS, the metadata persistency layer contains KO metadata; it does not store actual content of KOs, it rather knows where content resides and points to it. Therefore, the KMS acts as a broker rather than a repository of knowledge. This approach addresses system and representational heterogeneity of knowledge and ensures easier integration of KMSs with existing systems. Figure 4: Metadata annotation of KOs in Know-Net Metadata annotation is directly integrated into the input forms of any KMS tool, including those that primarily support the socialization approach to KM (e.g. communication tools) and those that support the codification approach (e.g. best practice repository). In the Know-Net KMS for example, each input interface of a discussion database contains input fields for the metadata attributes (Figure 4). Later, when a user searches for KOs annotated with particular metadata, the system is able to seamlessly retrieve both a discussion database entry, a best practice document or any representation of a KO annotated with the particular metadata.

5 30 5 The representation of human expertise is often necessary e.g., for the retrieval of knowledgeable people that could provide advice on knowledge seekers problems. In our approach experts are represented as KOs. A user, in addition to content-based KOs, can seamlessly retrieve links to experts that have the appropriate competencies to support her/his needs. In INKASS for example, if a user wishes to consult an expert, s/he can initiate the indicated workflow (e.g., Request for Proposal), negotiate the terms of the knowledge provision and utilize the automatically created workspace as a means for specifying knowledge needs in detail, exchange messages related to knowledge exchange and communicating delivery of KOs provided by the expert. Ontological heterogeneity is a prominent problem in cases where there is no commonly agreed upon terminology, such as the case in knowledge sharing in ad-hoc teams that IKOS supports. IKOS allows individual users to express their own mental models in a structured way. Based on the core upper elements, each user can extend his/her personal mental model in a non-restrictive manner. Finally, knowledge content and knowledge types heterogeneity are addressed by explicitly representing the different content and types of a particular KO in the content facet (content URI, content format and content types attributes). 2) Improving access to knowledge To be useful, knowledge must be visible and readily available, particularly when a user does not know that relevant knowledge is available. Based upon -defined metadata, the Know-Net, IKOS and INKASS KMSs offer a mixed browsing-searching approach over stored KOs. All three KMSs support the representation of textual and nontextual (e.g. experts, multimedia content) knowledge entities using metadata. In INKASS for example, the use of defined metadata improves access to knowledge in the following ways: 1. Use of background knowledge for query relaxation and query refinement in order to increase retrieval precision or recall. At any time, the KMS provides query-specific dialogue questions. By answering these questions some retrieved KOs are eliminated and the user is directed to more precise KOs. A KO is ruled out only if there is a conflict with the user s answer (Figure 5; area A). 2. Provision of different views (different interest, levels of detail, wording, language, only partial views, etc.) for different user classes. If information about user profiles is available then this information is used by the system to provide personalized views on KOs. 3. Ontology visualization for improved navigation in large knowledge spaces. At any time the user can choose to navigate through the ontologies and browse for relevant content. This view is an interactive, graphical visualization of (parts of) the ontologies. 4. Use of background knowledge for query disambiguation. The user enters her/his query in natural language. The KMS returns not only a list of matching KOs but also a list of query Figure 5: Improving access to knowledge with INKASS keywords it understands (i.e. exist in the ). These keywords provide feedback to the user related to how her/his

6 30 6 query relates to the and, consequently, to the retrieved KOs (Figure 5; area B). A second challenge is how to explain to users that particular content is relevant to her/his needs. This is even more important in cases of implicitly generated queries where the relation between the information used to generate the query (user context, user profile, user preferences, etc.) and results returned can be complex and difficult to describe. Approximate matches, i.e. matches in which the search keyword does not appear itself, but related concepts appear instead are explained by pop-up menus when the mouse is moved over such a keyword (Figure 5; area C). Further, the system presents the recognized keywords and their position in the and provides the ability to retrieve KOs which are clustered in the corresponding area of the. Finally, the KMS supports search result navigation and filtering. At any time the user can switch to an alternative result organization: The list of retrieved results can be structured based on differentiating concepts. For example, KOs related to project management can be presented into categories such as task scheduling, resource usage and project planning. I. CONCLUSIONS The field of KM has matured significantly during the recent years. Currently there exist various technological solutions for facilitating knowledge creation, storage and sharing at various levels: personal, team, organizational and inter-organizational. However, there still remain a number of critical heterogeneities (representational, semantic, etc.) which current solutions fall short to address in a successful manner. In this paper we briefly presented a faceted and examined its applicability in knowledge management situations using three KM systems that we have developed. The faceted presented here was derived from a broad range of scientific and practical inputs and has been up to now validated in eight cases in such knowledge-intensive sectors as banking, software development and management consulting. Although, we can not claim that it covers all possible KM initiatives, it provides an overall comprehensive structure that can be further applied and extended by researchers and practitioners working on personal, team, organizational or inter-organizational KM initiatives. [5] De Boer, M., Van den Bosch, F. and Volberda, H. Managing organizational knowledge integration in the emerging multimedia complex, Journal of Management Studies 36(3), 1999, [6] Fellbaum, C. WordNet, An Electronic Lexical Database, MIT Press, Cambridge, MA, [7] Fensel, D. Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce, Springer-Verlag, [8] Fenton, N. E., Pfleeger, S. L. (1997), Software Metrics, A Rigorous and Practical Approach, 2nd edition, PWS Publishing. [9] Garud, R. and Nayyar, P. Transformation capacity: Continual structuring by inter-temporal technology transfer, Strategic Management Journal 15, 1994, [10] Gruber, T. R. Toward principles for the design of ontologies used for knowledge sharing, International Journal of Human-Computer Studies, 43(5-6), Nov./Dec. 1995, [11] Kiryakov, A. Ontologies for Knowledge Management, in Davies, J., Studer, R., Warren, P. (Eds) Semantic Web Technologies: Trends and Research in Ontology-based Systems, Wiley, [12] Lansdale, M. "The psychology of personal information management, Applied Ergonomics 19(1), 1988, [13] Maedche, A., Motik, B., Stojanovic, L., Studer, R. and Volz, R. Ontologies for Enterprise Knowledge Management, IEEE Intelligent Systems, March / April [14] Masolo, C., Borgo, et al. The WonderWeb Library of Foundational Ontologies, WonderWeb Deliverable D17, August [15] Mentzas, G.N., Apostolou, D., Abecker A. and Young, R. Knowledge Asset Networking: a Holistic Approach for Leveraging Corporate Knowledge, Springer-Verlag, [16] Niles, I. and Pease, A. Towards a standard upper retrieved October 10, [17] Nonaka, I. and Takeuchi, H. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, [18] OVUM Knowledge Management: Building the Collaborative Enterprise, [19] Sauermann L., Bernardi A. and Dengel A. Overview and Outlook on the Semantic Desktop, Proceedings of the 1st Workshop on the Semantic Desktop at the ISWC 2005 Conference, [20] Schmid, B. and Lindemann, M. A. Elements of a Reference Model for Electronic Markets, in proceedings of the 31st Hawaii International Conference on Systems Science (HICCS'98), Hawaii, January 1998, [21] Uschold, M., King, M., Moralee, S. and Zorgios, Y. The enterprise, Knowledge Engineering Review 13, [22] Yin, R.K., Case Study Research: Design and Methods, Sage Publications, Beverly Hills, CA, REFERENCES [1] Alavi, M. and Tiwana, A. Knowledge Integration in Virtual Teams: The Potential Role of KMS, Journal of the American Society for Information Science and Technology 53(12), 2002, [2] Allard, S., Holsapple, C.W. Knowledge Management as a Key for E- business Competitiveness: From the Knowledge Chain to KM Audits, The Journal of Computer Information Systems, 5, 2002, 19. [3] Apostolou, D., Mentzas, G.N, Klein, B., Abecker, A., Maass, W. Inter- Organizational Knowledge Exchanges, IEEE Intelligent Systems, forthcoming. [4] Beerli Al., Falk Sv. and Diemers D. Knowledge Management and Networked Environments, Amacon, New York, 2003.