EKOSS: A Knowledge-User Centered Approach to Knowledge Sharing, Discovery, and Integration on the Semantic Web

2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM)

2010

Self Cite

Repositories of semantic statements that are created manually can contain detailed domain-specific knowledge with "heavy-weight" semantics that support intelligent computer processing services. However, such repositories often lack common knowledge since the authors of semantic statements often leave out details that human readers would already know. Some of this common knowledge can be encoded in ontologies that provide computers with background facts for a particular domain. However, the scalability of this approach is limited. We describe a stepwise procedure to enrich a repository of user contributed semantic statements with a set of standard entity statements that provide information about commonly known entities in the domain. We apply the procedure to extract common knowledge about city entities from Linked Open Data sources into standard entity statements that we use to enrich the semantic statements in the EKOSS failure cases knowledge base, which contains knowledge about failures in engineering. We then present a qualitative evaluation through a scenario, which demonstrates that using this simple procedure to enrich domain-specific semantic statements with common knowledge makes it possible to answer interesting search queries that would otherwise not get any results.

Section: B Approachmentioning

confidence: 99%

“…Structured data with semantics can be utilized by computers more intelligently than data having only human-readable semantics [1].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enriching city entities in the EKOSS failure cases knowledge base with Linked Open Data

2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM)

2010

Self Cite

“…EKOSS (expert knowledge ontology-based semantic search) is a Web-based system for supporting the computer-aided sharing, discovery and integration of knowledge resources (Kraines et al, 2006a). Building on the premise that knowledge sharing and discovery could benefit greatly if human creators of knowledge resources created computer-interpretable descriptors of those resources (Gerstein et al, 2007;Marcondes, 2004), EKOSS provides knowledge creators with tools enabling them to easily construct computer-understandable descriptors, which we call "semantic statements", that describe their resources using ontologies based on description logics (DL) as knowledge representation languages.…”

Section: Introductionmentioning

confidence: 99%

Supporting Reuse of Knowledge of Failures Through Ontology-Based Semantic Search

Proceedings of the International Conference on Knowledge Management and Information Sharing

2010

In order to increase the effectiveness of sharing and reusing knowledge about failures, we have applied the expert knowledge ontology-based semantic search system, EKOSS, to the "failure knowledge database". An ontology based on description logics is used as a formalized knowledge representation language for creating semantic statements describing 212 of the JST failure cases. Using the EKOSS reasoner, the similarity between a search statement giving the conditions of a new project or project design and the semantic statements describing failure cases can be quantified by inferring specific semantic relationships between entities involved in the case. The corpus of semantic statements is described, and results of applying the EKOSS semantic search to the semantic statements are analyzed. Finally, the effectiveness of the SCINTENG ontology for expressing the underlying failure mechanisms of the cases is discussed.

Explicit scientific knowledge comparison based on semantic description matching

2008

Proc of Assoc for Info

Self Cite

Researchers begin new research by acquiring pre-existing explicit scientific knowledge that is potentially relevant to the research subject. In order to find some potentially relevant explicit scientific knowledge items, such as knowledge whose content is similar to the targeted research, a researcher must examine the semantics of each item. In this paper, after reviewing related work, an automated semantic description matching-based approach is presented for comparing items of explicit scientific knowledge. This approach obtains a matching score between semantic descriptions of two items of explicit scientific knowledge that indicates their similarity. Three dimensions are considered in this approach: matching granularity, similarity scale for instance classes, and logic similarity scale. In order to match two semantic descriptions, a six-step method is presented: creation of atomic queries, generalization of query classes, generalization of query properties, addition of rules, creation of instances implied by complex class definition, and semi-automatic pruning of matching results. Finally, some conclusions regarding the approach are presented together with plans for future work.