Wikitology: A Novel Hybrid Knowledge Base Derived from Wikipedia

World knowledge may be available in different forms such as relational databases, triple stores, link graphs, meta-data and free text. Human minds are capable of understanding and reasoning over knowledge represented in different ways and are influenced by social, contextual and environmental factors. By following a similar model, we have integrated a variety of knowledge sources in a novel way to produce a single hybrid knowledge base, Wikitology, enabling applications to better access and exploit knowledge hidden in different forms.

Research projects like Cyc have resulted in the development of a complex broad coverage knowledge base however, relatively few applications have been built that really exploit it. In contrast, the design and development of the Wikitology KB has been incremental and driven and guided by a variety of applications, including document concept prediction, cross document co-reference resolution defined as a task in Automatic Content Extraction, linking entity mentions to KB entries defined as a part of TAC Knowledge Base Population Track, and interpreting tables. These use cases directly serve to evaluate the utility of the knowledge base for different applications and also demonstrate how the knowledge base could be exploited in different ways. We have also developed an approach for automatically enriching the Wikitology KB by unsupervised discovery of ontology elements from Wikipedia article links.

• Dr. Tim Finin (Chair)
• Dr. Anupam Joshi
• Dr. Tim Oates
• Dr. Evelyne Viegas
• Dr. Laura Zavala