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: Tue., 4/29: 1-2:30pm. Otherwise, during exam week and the rest of 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.
Dezhao Song and Jeff Heflin. Automatically Generating Data Linkages Using a Domain-Independent Candidate Selection Approach. 10th International Semantic Web Conference. Bonn, Germany. LNCS 7031. Springer. November 2011.
This paper describes how to improve the speed of determining mappings between objects described in RDF (although it can be easily applied to any graph data). The process requires no domain-specific information other than what classes and properties are comparable, which can be found in existing ontologies or by ontology-alignment techniques. We show that mappings between 1 million instance can be performed in under one hour on a Sun workstation. Surprisingly, this high recall, low precision filtering mechanism frequently leads to higher F-scores in the overall system.
Yang Yu and and Jeff Heflin. Extending Functional Dependency to Detect Abnormal Data in RDF Graphs. The 10th International Semantic Web Conference. Bonn, Germany. Springer. November 2011.
This paper describes a domain-independent approach to determining the data quality of graph data. The approach first learns probable functional dependencies in the graph, considering a fuzzy matching of values to account for some variation in the data. These functional dependencies are then used to test for data that does not fit the pattern. Experimental tests identified over 2800 anomalous triples in DBPedia, and investigation of a random sample found that 86.5% of these were actual errors.
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.
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.

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Enhanced with SHOE [OWL Markup]