Instance Data Evaluation for Semantic Web-Based Knowledge Management Systems

Abstract

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.