We review the use on ontologies for the integration of heterogeneous information sources. Based on an in-depth evaluation of existing approaches to this problem we discuss how ontologies are used to support the integration task. We evaluate and compare the languages used to represent the ontologies and the use of mappings between ontologies as well as to connect ontologies with information sources. We also enquire into ontology engineering methods and tools used to develop ontologies for information integration. Based on the results of our analysis we summarize the state-of-the-art in ontology-based information integration and name areas of further research activities.

The development and deployment of practical Semantic Web applications requires technologies for the storage and retrieval of RDF data that are robust and scalable. In this paper, we describe the 3store RDF storage and query engine developed within the Advanced Knowledge Technologies project, and discuss the design rationale and optimisations behind it which enable the efficient handling of large RDF knowledge bases.

The World Wide Web’s phenomenal growth rate is making it increasingly difficult to locate, organize, and integrate the available information. To cope with this enormous quantity of data, we need to hand off portions of these tasks to machines. However, since natural language processing is still an unsolved problem, machines cannot understand the web pages to the extent required to perform the desired tasks. An alternative is to change the Web to make it more understandable by machines, thereby creating a Semantic Web. Many researchers believe that the key to building this new web lies in the development of semantically-enriched languages. Early languages, such as RDF, SHOE, and Ontobroker, have led to more recent efforts, such as the DARPA Agent Markup Language (DAML). It is argued that languages such as these will revolutionize the Web, but if so, how will the new Web work? In this article, we will put a Semantic Web language through its paces and try to answer questions about how it can be used in practice. How are the semantic descriptions generated? How are these descriptions discovered by agents? How can information from different sites be integrated? How can the Semantic Web be queried? We will present a system that addresses these questions, and describe a suite of tools to help users in their various interactions with the

It is our contention that the World Wide Web poses challenges to knowledge representation systems that fundamentally change the way we should design KR languages. In this paper, we describe the Simple HTML Ontology Extensions (SHOE), a KR language which allows web pages to be annotated with semantics. We present a formalism for the language and discuss the features which make it well suited for the Web. We describe the syntax and semantics of this language, and discuss the differences from traditional KR systems that make it more suited to modern web applications. We also describe some generic tools for using the language and demonstrate its capabilities by describing two prototype systems that use it. We also discuss some future tools currently being developed for the language. The language, tools, and details of the applications are all available on the World Wide Web at http://www.cs.umd.edu/projects/plus/SHOE. 1 Introduction One of the venerable sub-fields of artificial ...

Abstract: We present DLDB, a knowledge base system that extends a relational database management system with additional capabilities for DAML+OIL inference. We discuss a number of database schemas that can be used to store RDF data and discuss the tradeoffs of each. Then we describe how we extend our design to support DAML+OIL entailments. The most significant aspect of our approach is the use of a description logic reasoner to precompute the subsumption hierarchy. We describe a lightweight implementation that makes use of a common RDBMS (MS Access) and the FaCT description logic reasoner. Surprisingly, this simple approach provides good results for extensional queries over a large set of DAML+OIL data that commits to a representative ontology of moderate complexity. As such, we expect such systems to be adequate for personal or small-business usage.

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This paper describes how SHOE, a set of Simple HTML Ontological Extensions, can be used to discover implicit knowledge from the World-Wide Web (WWW). SHOE allows authors to annotate their pages with ontology-based knowledge about page contents. In previous papers, we discussed how the semantic knowledge provided by SHOE allows users to issue queries that are much more sophisticated than keyword search techniques, including queries that require retrieval of information from many sources. Here, we expand upon this idea by describing how SHOE's ontologies allow agents to understand more than what is explicitly stated in Web pages through the use of context, inheritance and inference. We use examples to illustrate the usefulness of these features to Web agents and query engines. 1. Introduction The Web is a growing, uncontrolled, incomplete, and distributed information resource. Users are overwhelmed by information overload and need ways to efficiently access the information that is most ...

XML will have a profound impact on the way data is exchanged on the Internet. An important feature of this language is the separation of content from presentation, which makes it easier to select and/or reformat the data. However, due to the likelihood of numerous industry and domain specific DTDs, those who wish to integrate information will still be faced with the problem of semantic interoperability. In this paper we discuss why this problem is not solved by XML, and then discuss why the Resource Description Framework is only a partial solution. We then present the SHOE language, which we feel has many of the features necessary to enable a semantic web, and describe an existing set of tools that make it easy to use the language.

This paper introduces the concept of learning agent shell as a new class of tools for rapid development of practical endto-end knowledge-based agents, by domain experts, with limited assistance from knowledge engineers. A learning agent shell consists of a learning and knowledge acquisition engine as well as an inference engine and supports building an agent with a knowledge base consisting of an ontology and a set of problem solving rules. The paper describes a specific learning agent shell and its associated agent building methodology. The process of developing an agent relies on importing ontologies from existing repositories of knowledge, and on teaching the agent how to perform various tasks, in a way that resembles how an expert would teach a human apprentice when solving problems in cooperation. The shell and methodology represent a practical integration of knowledge representation, knowledge acquisition, learning and problem solving. This work is illustrated with an example of developing a hierarchical non-linear planning agent.

In this paper we benchmark three popular database representations of RDF/S schemata and data: (a) a schema-aware (i.e., one table per RDF/S class or property) with explicit (ISA) or implicit (NOISA) storage of subsumption relationships, (b) a schema-oblivious (i.e., a single table with triples of the form #subject-predicate-object#), using (ID) or not (URI) identifiers to represent resources and (c) a hybrid of the schema-aware and schema-oblivious representations (i.e., one table per RDF/S meta-class by distinguishing also the range type of properties). Furthermore,