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.
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.
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.
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. In addition, it includes a discussion of preliminary tests on Jena and SPARQL versions of the benchmark queries.
This paper, which won the Best Paper Award at ISWC 2004, is the first comprehensive experiment to compare different Semantic Web reasoners. Using our Lehigh University Benchmark (LUBM), we analyze the performance of Sesame (both a memory-based and data-based versions), OWL Jess KB and DLDB on different sizes of OWL datasets.
This paper discusses the crucial problems that occur when distributed ontologies evolve over time. It provides a formal semantics for ontology perspective theory, which is our recommended solution to the problem.
Please do not send me e-mail asking me to evaluate your chances of
admission to the department. I typically do not respond to such requests.
If you are interested in joining my research group, then send me an
e-mail that specifically describes what you would like to do and what prior
qualifications you have. However, I recommend that you read some of my
publications and explore our
current research first. If I think your interests
match our research, then I will contact you for further information.
Semantic Web Resources:
Semantic Web Activity at W3C
The World Wide Web Consortium's collection of specifications, working groups, and resources related to the Semantic Web.
SemanticWeb.org
A Semantic Wiki for the Semantic Web community. Includes information on tools, ontologies, people, and events.
SemWebCentral
A web site for non-developers to learn about the Semantic Web and for developers to share Semantic Web tools.
The Semantic Web by Tim Berners-Lee, James Hendler, and Ora Lassila
The Scientific American article that presents the vision of the Semantic Web.