Rong Pan

Swoogle: a search and metadata engine for the semantic web

Swoogle is a crawler-based indexing and retrieval system for the Semantic Web. It extracts metadata for each discovered document, and computes relations between documents. Discovered documents are also indexed by an information retrieval system which can use either character N-Gram or URIrefs as keywords to find relevant documents and to compute the similarity among a set of documents. One of the interesting properties we compute is <i>ontology rank</i>, a measure of the importance of a Semantic Web document.

BayesOWL: Uncertainty Modeling in Semantic Web Ontologies

It is always essential but di±cult to capture incomplete, partial or uncertain knowledge when using ontologies to conceptualize an application domain or to achieve semantic interoperability among heterogeneous systems. This chapter presents an on-going research on developing a framework which augments and supplements the semantic web ontology language OWL for representing and reasoning with uncertainty based on Bayesian networks (BN), and its application in ontology mapping.

Search on the Semantic Web

To help human users and software agents find relevant knowledge on the Semantic Web, the Swoogle search engine discovers, indexes, and analyzes the ontologies and facts that are encoded in Semantic Web documents.

Belief Update in Bayesian Networks Using Uncertain Evidence

This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three existing belief update methods with uncertain evidences: virtual evidence method, Jeffreys rule, and IPFP (iterative proportional fitting procedure), and analyze the relations between these methods. This in-depth understanding leads us to propose two algorithms for belief update with multiple soft evidences. Both of these algorithms can be seen as integrating the techniques of virtual evidence method, IPFP and traditional BN evidential inference, and they have clear computational and practical advantages over the methods proposed by others in the past

Bayesian Network Reasoning with Uncertain Evidences

This paper investigates the problem of belief update in Bayesian networks (BN) with uncertain evidence. Two types of uncertain evidences are identified: virtual evidence (reflecting the uncertainty one has about a reported observation) and soft evidence (reflecting the uncertainty of an event one observes). Each of the two types of evidence has its own characteristics and obeys a belief update rule that is different from hard evidence, and different from each other. The particular emphasis is on belief update with multiple uncertain evidences. Efficient algorithms for BN reasoning with consistent and inconsistent uncertain evidences are developed, and their convergences analyzed. These algorithms can be seen as combining the techniques of traditional BN reasoning, Pearls virtual evidence method, Jeffreys rule, and the iterative proportional fitting procedure.

Using semantic web technology in multi-agent systems: a case study in the TAGA trading agent environment

Travel Agent Game in Agentcities (TAGA) is the framework that extends and enhances the Trading Agent Competition (TAC) scenario to work in Agentcities, an open multi agent environment based on FIPA compliant pla tforms. TAGA uses the semantic web languages and tools (RDF and OWL) to specify and publish the underlying common ontologies; as a content language within the FIPA ACL messages; as the basis for agent knowledge bases via XSB-based reasoning tools; to describe and reason about services. TAGA extends the FIPA protocols to support open market auctions and enriches the Agentcities with auction services. The introducing of the semantic web languages improves the interoperability among agents. TAGA is intended as a platform for research in multi-agent systems, the semantic web and automated trading in dynamic markets as well as a self-contained application for teaching and experimentation with these technologies.

BAYESIAN NETWORK REVISION WITH PROBABILISTIC CONSTRAINTS

This paper deals with an important probabilistic knowledge integration problem: revising a Bayesian network (BN) to satisfy a set of probability constraints representing new or more specific knowledge. We propose to solve this problem by adopting IPFP (iterative proportional fitting procedure) to BN. The resulting algorithm E-IPFP integrates the constraints by only changing the conditional probability tables (CPT) of the given BN while preserving the network structure; and the probability distribution of the revised BN is as close as possible to that of the original BN. Two variations of E-IPFP are also proposed: 1) E-IPFP-SMOOTH which deals with the situation where the probabilistic constraints are inconsistent with each other or with the network structure of the given BN; and 2) D-IPFP which reduces the computational cost by decomposing a global E-IPFP into a set of smaller local E-IPFP problems.