SWAT Abstracts 04

We describe an approach to partitioning a large OWL ABox with respect to a TBox so that specific kinds of reasoning can be performed separately on each partition and the results trivially combined in order to achieve complete answers. The main features of our approach include: a reasonable tradeoff between the complexity of the task and the granularity of partitioning; worst-case polynomial time complexity; and the ability to handle problems that are too large for main memory. In addition, we show promising experimental results on both the Lehigh University Benchmark data and the real world FOAF data. This work could contribute to the development of scalable Semantic Web systems that need to deal with large amounts of data.

A distributed, end-to-end information integration system that is based on the Semantic Web architecture is of considerable interest to both commercial and government organizations. However, there are a number of challenges that have to be resolved to build such a system given the currently available Semantic Web technologies. We describe here the ISENS prototype system we designed, implemented, and tested (on a small scale) to address this problem. We discuss certain system limitations (some coming from underlying technologies used) and future ISENS development to resolve them and to enable an extended set of capabilities.

We show that the Semantic Web needs a formal semantics for the various kinds of links between ontologies and other documents. We provide a model theoretic semantics that takes into account ontology extension and ontology versioning. Since the Web is the product of a diverse community, as opposed to a single agent, this semantics accommodates different viewpoints by having different entailment relations for different ontology perspectives. We extend our previous work to support deprecation in ontologies and to support retrospective as well as prospective versioning.

This report is an expanded version of a paper in AAAI-2006 proceedings. In this report, we investigate the challenges that must be addressed for the Semantic Web to become a feasible enterprise. Specifically we focus on the query answering capability of the Semantic Web. We put forward that two key challenges we face are heterogeneity and scalability. We propose a flexible and decentralized framework for addressing the heterogeneity problem and demonstrate that sufficient reasoning is possible over a large dataset by taking advantage of database technologies and making some tradeoff decisions. As a proof of concept, we collect a significant portion of the available Semantic Web data; use our framework to resolve some heterogeneity and reason over the data as one big knowledge base. In addition to demonstrating the feasibility of a "real" Semantic Web, our experiments have provided us with some interesting insights into how it is evolving and the type of queries that can be answered.

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Y. Guo, A. Qasem, Z. Pan and J. Heflin. A Requirements Driven Framework for Benchmarking Semantic Web Knowledge Base Systems. In IEEE Transactions on Knowledge and Data Engineering: Special Issue: Knowledge and Data Engineering in the Semantic Web Era, 2006

A key challenge for the Semantic Web is to acquire the capability to effectively query large knowledge bases. As there will be several competing systems, we need benchmarks that will objectively evaluate these systems. Development of effective benchmarks in an emerging domain is a challenging endeavor. In this paper, we propose a requirements driven framework for developing benchmarks for Semantic Web Knowledge Base Systems (SW KBSs). In this paper we make two major contributions. First, we provide a list of requirements for SW KBS benchmarks. This can serve as an unbiased guide to both the benchmark developers and personnel responsible for systems acquisition and benchmarking. Second, we provide an organized collection of techniques and tools needed to develop such benchmarks. In particular, the collection contains a detailed guide for generating benchmark workload, defining performance metrics and interpreting experimental results.

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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

We investigate the challenges that must be addressed for the Semantic Web to become a feasible enterprise. Specifically we focus on the query answering capability of the Semantic Web. We put forward that two key challenges we face are heterogeneity and scalability. We propose a flexible and de- centralized framework for addressing the heterogeneity prob- lem and demonstrate that sufficient reasoning is possible over a large dataset by taking advantage of database technologies and making some tradeoff decisions. As a proof of concept, we collect a significant portion of the available Semantic Web data; use our framework to resolve some heterogeneity and reason over the data as one big knowledge base. In addition to demonstrating the feasibility of a "rea" Semantic Web, our experiments have provided us with some interesting insights into how it is evolving and the type of queries that can be answered.

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Y. Guo, A. Qasem, J. Heflin. Large Scale Knowledge Base Systems: An Empirical Evaluation Perspective. In Proc. of the Twenty First National Conference on Artificial Intelligence (AAAI 2006), Boston, USA, 2006

In this paper, we discuss how our work on evaluating Semantic Web knowledge base systems (KBSs) contributes to address some broader AI problems. First, we show how our approach provides a benchmarking solution to the Semantic Web, a new application area of AI. Second, we discuss how the approach is also beneficial in a more traditional AI context. We focus on issues such as scalability, performance tradeoffs, and the comparison of different classes of systems.

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SWAT abstracts 2006