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Don't Like RDF Reification? Making Statements about Statements Using Singleton Property
Vinh Nguyen1, Olivier Bodenreider2, Amit Sheth1
1Kno.e.sis Center, Wright State University, Dayton, Ohio, USA.
Proceedings of the ... International World-Wide Web Conference. International WWW Conference
|March 10, 2015
Summary
We introduce Singleton Property, a novel approach for representing meta-knowledge in RDF data. This method efficiently handles provenance, time, and certainty, improving semantic knowledge bases.
Area of Science:
- Computer Science
- Semantic Web Technologies
Background:
- Existing methods for representing meta-knowledge in RDF (Resource Description Framework) are inefficient and lack formal semantics.
- Standard reification, while intuitive, is not widely adopted due to its limitations in formal semantics and practical application.
Purpose of the Study:
- To propose and formalize a novel approach, Singleton Property, for representing statements about RDF statements (meta triples).
- To demonstrate the effectiveness of Singleton Property in enhancing semantic knowledge bases with provenance, temporal, and certainty information.
Main Methods:
- Developed a formal semantics for the Singleton Property approach.
- Integrated Singleton Property with existing RDF syntax, semantics, and SPARQL query language.
- Applied and evaluated Singleton Property in YAGO2 and BKR knowledge bases.
Main Results:
- Singleton Property provides a formal semantic basis for meta-knowledge representation.
- Experiments on the BKR knowledge base show competitive performance in terms of triple count, query length, and execution time compared to existing methods.
- The approach is demonstrated to be simple and intuitive for representing and querying meta-knowledge.
Conclusions:
- Singleton Property offers an efficient and formally grounded method for managing meta-knowledge in semantic web knowledge bases.
- This approach can be readily adopted for representing and querying statements about statements in diverse knowledge base applications.
- The proposed method enhances the capabilities of semantic knowledge bases by incorporating provenance, temporal, and certainty data effectively.
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