On-To-Knowledge: Semantic Web-Enabled Knowledge Management

Sure, York; Akkermans, Hans; Broekstra, Jeen; Davies, John; Ding, Ying; Duke, Alistair; Engels, Robert; Fensel, Dieter; Horrocks, Ian; Iosif, Victor; Kampman, Arjohn; Kiryakov, Atanas; Klein, Michel C. A.; Lau, Thorsten; Ognyanov, Damyan; Reimer, Ulrich; Simov, Kiril; Studer, Rudi; Meer, Jos van der; Harmelen, Frank van

doi:10.1007/978-3-662-05320-1_13

Cited by 18 publications

(6 citation statements)

References 4 publications

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“…Furthermore different persons may have varying information needs with respect to the same tasks, depending on their personal skills and knowledge [28]. The user ontology described in the On-To-Knowledge project [29] uses manually constructed ontologies about skills, job functions, and education. This ontology is dedicated for skills management application.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ontology-Based Framework for Modeling User Behavior—A Case Study in Knowledge Management

Razmerita

2011

IEEE Trans. Syst., Man, Cybern. A

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This article focuses on the role of user modeling and semantic-enhanced representations for personalization. The paper presents a generic Ontology-based User Modeling framework (OntobUMf), its components and its associated user modeling processes. This framework models the behavior of the users and classifies its users according to their behavior. The user ontology is the backbone of OntobUMf and has been designed according to the Information Management System Learning Information Package (IMS LIP). The user ontology includes a behavior concept that extends IMS LIP specification and defines characteristics of the users interacting with the system. Concrete examples of how OntobUMf is used in the context of a Knowledge Management System (KMS) are provided. The paper discusses some of the implications of ontology-based user modeling for semantic-enhanced Knowledge Management (KM), and in particular for personal KM. The results of this research may contribute to the development of other frameworks for modeling user behavior, other semantic-enhanced user modeling frameworks or other semantic-enhanced information systems.

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Section: Literature Reviewmentioning

confidence: 99%

An Ontology-Based Framework for Modeling User Behavior—A Case Study in Knowledge Management

Razmerita

2011

IEEE Trans. Syst., Man, Cybern. A

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“…The On-To-Knowledge project [15] provided one of the first suits of tools to be used for semantics based knowledge management. This tool suite not only supports semantic data acquisition from heterogeneous sources, but also supports ontology sharing, editing, versioning, and visualization.…”

Section: State Of the Artmentioning

confidence: 99%

An Infrastructure for Acquiring High Quality Semantic Metadata

Lei

Sabou

López

et al. 2006

Lecture Notes in Computer Science

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Abstract. Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a verification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation comparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata.

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“…METHONTOLOGY [1] and the OTK methodology [7] are good examples for this approach. They offer guidance for building ontologies either from scratch, reusing other ontologies as they are, or re-engineering them.…”

Section: Ontology Engineeringmentioning

confidence: 99%

“…From a DILIGENT process point of view, we are currently in the revision phase. Some of the partners have already used a previous version of PROTON and adapted it according to their needs 7 . Others have just recently joined the process.…”

Section: Use Casementioning

confidence: 99%

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An Argumentation Ontology for DIstributed, Loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT)

Tempich

Pinto

Sure

et al. 2005

Lecture Notes in Computer Science

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Abstract.A prerequisite to the success of the Semantic Web are shared ontologies which enable the seamless exchange of information between different parties. Engineering a shared ontology is a social process. Since its participants have slightly different views on the world, a harmonization effort requires discussing the resulting ontology. During the discussion, participants exchange arguments which may support or object to certain ontology engineering decisions. Experience from software engineering shows that tracking exchanged arguments can help users at a later stage to better understand the assumptions underlying the design decisions. Furthermore, as the constructed ontology becomes larger, ontology engineers might argue in a contradictory way without knowing so. In this paper we present an ontology which formalizes the main concepts which are used in an DILIGENT ontology engineering discussion and thus enables tracking arguments and allows for inconsistency detection. We provide an example which is drawn from experiments in an ontology engineering process to construct an ontology for knowledge management in our institute. Having constructed the ontology we also show how automated ontology learning algorithms could be taken as participants in the OE discussion. Hence, we enable the integration of manual, semi-automatic and automatic ontology creation approaches.

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On-To-Knowledge: Semantic Web-Enabled Knowledge Management

Cited by 18 publications

References 4 publications

An Ontology-Based Framework for Modeling User Behavior—A Case Study in Knowledge Management

An Ontology-Based Framework for Modeling User Behavior—A Case Study in Knowledge Management

An Infrastructure for Acquiring High Quality Semantic Metadata

An Argumentation Ontology for DIstributed, Loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT)

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