Instance Data Evaluation for Semantic Web-Based Knowledge Management Systems

Printer-friendly version


As semantic web technologies are increasingly used to empower knowledge management systems (KMSs), there is a growing need for mechanisms and automated tools for checking content generated by semantic-web tools. The content in a KMS includes both the knowledge management (KM) schema and the data contained within. KM schemas can be viewed as ontologies and the data contained within can be viewed as instance data. Thus we can apply semantic web ontology and instance data processing techniques and tools in KM settings. There are many semantic web tools aimed at ontology evaluation, however there is little, if any, research focusing on instance data evaluation. Although instance data evaluation has many issues in common with ontology evaluation, there are some issues that are either more prominent in or unique to instance data evaluation. Instance data often accounts for orders of magnitude more data than ontology data in organization intranets, thus our work focuses on evaluation techniques that help users of KMSs to determine when certain instance data is ready for use. We present our work on semantic web instance data evaluation for KMSs. We define the instance data evaluation research problem and design a general evaluation process GEP. We identify three categories of issues that may occur in instance data: syntax errors, logical inconsistencies, and potential issues. For each category of issues, we provide illustrative examples, describe the symptoms, analyze the causes, and present our detection solution. We implement our design in TW OIE which is an online instance data evaluation service. We perform experiments that show that the TW OIE is more comprehensive than most existing online semantic web data evaluators.


DateCreated ByLink
July 14, 2011
Jiao TaoDownload

Related Projects:

Inference Web Project LogoInference Web
Principal Investigator: Deborah L. McGuinness
Description: The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust. Provenance - if users (humans and agents) are to use and integrate data from unknown, uncertain, or multiple sources, they need provenance metadata for evaluation Interoperability - more systems are using varied sources and multiple information manipulation engines, thus increasing interoperability requirements Explanation/Justification - if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available Trust - if some sources are more trustworthy than others, trust ratings are desired The Inference Web consists of two important components: Proof Markup Language (PML) Ontology - Semantic Web based representation for exchanging explanations including provenance information - annotating the sources of knowledge justification information - annotating the steps for deriving the conclusions or executing workflows trust information - annotating trustworthiness assertions about knowledge and sources IW Toolkit - Web-based and standalone tools that facilitate human users to browse, debug, explain, and abstract the knowledge encoded in PML.

Related Research Areas:

Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: , Semantic Web
Ontology Engineering Environments
Lead Professor: Deborah L. McGuinness
Description: Ontology Engineering Environments
Concepts: Semantic Web
Semantic Foundations
Lead Professor: Deborah L. McGuinness
Description: Semantic Foundations
Concepts: Semantic Web