Jeff Heflin

Associate Professor
Department of Computer Science and Engineering,
Lehigh University


Contact Info:

Address:
Dept. of Computer Science and Engineering
Lehigh University
19 Memorial Drive West
Bethlehem, PA 18015

Office: 330 Packard Lab
Office Hours: During the summer, office hours are by appointment only

E-Mail: heflin@cse.lehigh.edu

Phone: (610) 758-6533
Fax: (610) 758-4096

Courses:

Here are the courses I am currently teaching or have taught recently. For a complete list of the courses I have taught, click here

Research:

The Semantic Web and Agent Technologies (SWAT) Lab
The Semantic Web is a vision for extending the Web so that machines can more intelligently integrate and process the wealth of information that is available. Unlike HTML and ordinary XML, Semantic Web languages such as SHOE, DAML+OIL, and OWL (a W3C Recommendation), allow semantics (i.e., meaning) to be explicitly associated with the content. The semantics are formally specified in ontologies, which can be shared via the Internet and extended for local needs. The SWAT lab is at the forefront of Semantic Web research by studying issues such as interoperability of distributed ontologies, ontology evolution, and system architectures and tools for the Semantic Web. See the group's homepage for details.

Selected Publications:

Also see the full list of SWAT publications and the list of my publications prior to directing the SWAT Lab at Lehigh.
Y. Li, and J. Heflin. Using Reformulation Trees to Optimize Queries over Distributed Heterogeneous Sources. Ninth International Semantic Web Conference (ISWC 2010). 2010.
This paper describes an algorithm that uses the structure of a rule-goal tree expressing the rewrites of a given query to efficiently locate the relevant sources. It starts with the most selective query nodes, and incrementally loads sources, using the information to refine queries of subsequent sources. Our experiments show that this algorithm can answer many randomly-generated complex queries against 20 million heterogeneous data sources in less than 30 seconds.
A. Qasem, D.A. Dimitrov and J. Heflin. Efficient Selection and Integration of Data Sources for Answering Semantic Web Queries. Second IEEE International Conference on Semantic Computing (ICSC 08). IEEE Computer Society Press. 2008. pp.245-252.
This paper introduces our approach to federated queries over numerous, heterogeneously described, Semantic Web sources by extending algorithms from the information integration literature. It presents a formal model of the problem, and demonstrated that the dominant factor in query response time was the time to actually retrieve the sources relevant to the query.
Y. Guo and J. Heflin. Document-Centric Query Answering for the Semantic Web. 2007 IEEE/WIC/ACM International Conference on Web Intelligence (WI '07), pp. 409-415, 2007.
This paper presents an approach to querying the Semantic Web that considers the document-oriented nature of Semantic Web data. It defines queries in which one can ask what is entailed by a given subset of the documents in the knowledge base as well as queries in which one asks for which documents entail specificanswers. It provides algorithms for solving theses problems and describes an experiment in which many of the queries can be answered in miliseconds.
Z. Pan, A. Qasem, J. Heflin. An Investigation into the Feasibility of the Semantic Web. In Proc. of the Twenty First National Conference on Artificial Intelligence (AAAI 2006), Boston, USA, 2006. pp. 1394-1399.
This is the first paper to discuss our attempts to realize the vision of the Semantic Web as a Web-scale query-answering system. We loaded nearly 350,000 real-world semantic web documents that committed to 41,000 ontologies into our DLDB system and then used additional "mapping ontologies" to integrate them. This experiment yielded promising results in that query times ranged from a few milliseconds to 5 seconds.
Y. Guo, Z. Pan, and J. Heflin. LUBM: A Benchmark for OWL Knowledge Base Systems. Journal of Web Semantics 3(2), 2005, pp158-182.
This is the definitive reference on the Lehigh University Benchmark (LUBM) and on empirical evaluation of Semantic Web knowledge base systems in general. This journal article coalesces the results from the ISWC 2003 and ISWC 2004 papers, the latter of which won the best paper award at the conference. In addition, it includes a discussion of preliminary tests on Jena and SPARQL versions of the benchmark queries.

Recent Service Activities:

Education:

Memberships:

Information for Prospective Graduate Students:

Semantic Web Resources:

Enhanced with SHOE [OWL Markup]