Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontologies. This paper provides a brief survey of the approaches to semantic integration developed by researchers in the ontology community. We focus on the approaches that differentiate the ontology research from other related areas. The goal of the paper is to provide a reader who may not be very familiar with ontology research with introduction to major themes in this research and with pointers to different research projects. We discuss techniques for finding correspondences between ontologies, declarative ways of representing these correspondences, and use of these correspondences in various semantic-integration tasks 1. ONTOLOGIES AND SEMANTIC INTE-

Ontologies are a key component for building open and dynamic distributed pervasive computing systems in which agents and devices share contextual information. We describe our use of the Web Ontology Language OWL and other tools for building the foundation ontology for the Context Broker Architecture (CoBrA), a new context-aware pervasive computing framework. The current version of the CoBrA ontology models the basic concepts of people, agents, places, and presentation events in an intelligent meeting room environment. It provides a vocabulary of terms for classes and properties suitable for building practical systems that model context in pervasive computing environments. We also describe our ongoing research in developing an OWL inference engine using Flora-2 and in extending the present CoBrA ontology to use the DAML spatial and temporal ontologies.

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In this paper we discuss the development and application of a large formal ontology to the semantic web. The Suggested Upper Merged Ontology (SUMO) (Niles & Pease, 2001) (SUMO, 2002) is a "starter document" in the IEEE Standard Upper Ontology effort. This upper ontology is extremely broad in scope and can serve as a semantic foundation for search, interoperation, and communication on the semantic web.

The increasing awareness of the benefits of ontologies for information processing has lead to the creation of a number of large ontologies about real world domains. The size of these ontologies and their monolithic character cause serious problems in handling them. In other areas, e.g. software engineering, these problems are tackled by partitioning monolithic entities into sets of meaningful and mostly self-contained modules. In this paper, we suggest a similar approach for ontologies. We propose a method for automatically partitioning large ontologies into smaller modules based on the structure of the class hierarchy. We show that the structure-based performs surprisingly well on real world ontologies.

Ontologies are becoming extremely useful tools for sophisticated software engineering. Designing applications, databases, and knowledge bases with reference to a common ontology can mean shorter development cycles, easier and faster integration with other software and content, and a more scalable product. Although ontologies are a very promising solution to some of the most pressing problems that confront software engineering, they also raise some issues and difficulties of their own. Consider, for example, the questions below: • How can a formal ontology be used effectively by those who lack extensive training in logic and mathematics? • How can an ontology be used automatically by applications (e.g. Information Retrieval and Natural Language Processing applications) that process free text? • How can we know when an ontology is complete? In this paper we will begin by describing the upper- level ontology SUMO (Suggested Upper Merged Ontology), which has been proposed as the initial version of an eventual Standard Upper Ontology (SUO). We will then describe the popular, free, and structured WordNet lexical database. After this preliminary discussion, we will describe the methodology that we are using to align WordNet with the SUMO. We close this paper by discussing how this alignment of WordNet with SUMO will provide answers to the questions posed above.

This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO’s precision at 95% – as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO’s data.

With the advances in telecommunications, and the introduction of the Internet, information systems achieved physical connectivity, but have yet to establish logical connectivity. Lack of logical connectivity is often inviting disaster as in the case of Mars Orbiter, which was lost because one team used metric units, the other English while exchanging a critical maneuver data. In this Thesis, we focus on the two intertwined sub problems of logical connectivity, namely data extraction and data interpretation in the domain of heterogeneous information systems. The first challenge, data extraction, is about making it possible to easily exchange data among semi-structured and structured information systems. We describe the design and implementation of a general purpose, regular expression based Caméléon wrapper engine with an integrated capabilities-aware planner/optimizer/executioner. The second challenge, data interpretation, deals with the existence of heterogeneous contexts, whereby each source of information and potential receiver of that information may operate with a different context, leading to large-scale semantic heterogeneity. We extend the existing formalization of the COIN framework with new logical formalisms and features to handle larger

This paper describes the first version of the Multilingual Central Repository, a lexical knowledge base developed in the framework of the MEANING project.

Query answering over commonsense knowledge bases typically employs a first-order logic theorem prover. While first-order inference is intractable in general, provers can often be hand-tuned to answer queries with reasonable performance in practice.