Showing papers on "Ontology-based data integration published in 1999"

Ontobroker: Ontology Based Access to Distributed and Semi-Structured Information

Abstract: The World Wide Web (WWW) can be viewed as the largest multimedia database that has ever existed. However, its support for query answering and automated inference is very limited. Metadata and domain specific ontologies were proposed by several authors to solve this problem. We developed Ontobroker which uses formal ontologies to extract, reason, and generate metadata in the WWW. The paper describes the formalisms and tools for formulating queries, defining ontologies, extracting metadata, and generating metadata in the format of the Resource Description Framework (RDF), as recently proposed by the World Wide Web Consortium (W3C). These methods provide a means for semantic based query handling even if the information is spread over several sources. Furthermore, the generation of RDF descriptions enables the exploitation of the ontological information in RDF-based applications.

A Framework for Understanding and Classifying Ontology Applications

Abstract: In1 this paper, we draw attention to common goals and supporting technologies of several relatively distinct communities to facilitate closer cooperation and faster progress. The common thread is the need for sharing the meaning of terms in a given domain, which is a central role of ontologies. The different communities include ontology research groups, software developers and standards organizations. Using a broad definition of ‘ontology’, we show that much of the work being done by those communities may be viewed as practical applications of ontologies. To achieve this, we present a framework for understanding and classifying ontology applications. We identify three main categories of ontology applications: 1) neutral authoring, 2) common access to information, and 3) indexing for search. In each category, we identify specific ontology application scenarios. For each, we indicate their intended purpose, the role of the ontology, the supporting technologies and who the principal actors are and what they do. We illuminate the similarities and differences between scenarios. The copyright of this paper belongs to the papers authors. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage. Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5) Stockholm, Sweden, August 2, 1999 (V.R. Benjamins, B. Chandrasekaran, A. Gomez-Perez, N. Guarino, M. Uschold, eds.) http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-18/ 1The order of authors was determined by a coin flip.

Building a chemical ontology using Methontology and the Ontology Design Environment

Abstract: Methontology provides guidelines for specifying ontologies at the knowledge level, as a specification of a conceptualization. ODE enables ontology construction, covering the entire life cycle and automatically implementing ontologies. To meet the challenge of building ontologies, we have developed Methontology, a framework for specifying ontologies at the knowledge level, and the Ontology Development Environment. We present our experience in using Methontology and ODE to build the chemical ontology.

Semantic integration of semistructured and structured data sources

Abstract: Providing an integrated access to multiple heterogeneous sources is a challenging issue in global information systems for cooperation and interoperability. In this context, two fundamental problems arise. First, how to determine if the sources contain semantically related information, that is, information related to the same or similar real-world concept(s). Second, how to handle semantic heterogeneity to support integration and uniform query interfaces. Complicating factors with respect to conventional view integration techniques are related to the fact that the sources to be integrated already exist and that semantic heterogeneity occurs on the large-scale, involving terminology, structure, and context of the involved sources, with respect to geographical, organizational, and functional aspects related to information use. Moreover, to meet the requirements of global, Internet-based information systems, it is important that tools developed for supporting these activities are semi-automatic and scalable as much as possible.The goal of this paper is to describe the MOMIS [4, 5] (Mediator envirOnment for Multiple Information Sources) approach to the integration and query of multiple, heterogeneous information sources, containing structured and semistructured data. MOMIS has been conceived as a joint collaboration between University of Milano and Modena in the framework of the INTERDATA national research project, aiming at providing methods and tools for data management in Internet-based information systems. Like other integration projects [1, 10, 14], MOMIS follows a “semantic approach” to information integration based on the conceptual schema, or metadata, of the information sources, and on the following architectural elements: i) a common object-oriented data model, defined according to the ODLI3 language, to describe source schemas for integration purposes. The data model and ODLI3 have been defined in MOMIS as subset of the ODMG-93 ones, following the proposal for a standard mediator language developed by the I3/POB working group [7]. In addition, ODLI3 introduces new constructors to support the semantic integration process [4, 5]; ii) one or more wrappers, to translate schema descriptions into the common ODLI3 representation; iii) a mediator and a query-processing component, based on two pre-existing tools, namely ARTEMIS [8] and ODB-Tools [3] (available on Internet at http://sparc20.dsi.unimo.it/), to provide an I3 architecture for integration and query optimization. In this paper, we focus on capturing and reasoning about semantic aspects of schema descriptions of heterogeneous information sources for supporting integration and query optimization. Both semistructured and structured data sources are taken into account [5]. A Common Thesaurus is constructed, which has the role of a shared ontology for the information sources. The Common Thesaurus is built by analyzing ODLI3 descriptions of the sources, by exploiting the Description Logics OLCD (Object Language with Complements allowing Descriptive cycles) [2, 6], derived from KL-ONE family [17]. The knowledge in the Common Thesaurus is then exploited for the identification of semantically related information in ODLI3 descriptions of different sources and for their integration at the global level. Mapping rules and integrity constraints are defined at the global level to express the relationships holding between the integrated description and the sources descriptions. ODB-Tools, supporting OLCD and description logic inference techniques, allows the analysis of sources descriptions for generating a consistent Common Thesaurus and provides support for semantic optimization of queries at the global level, based on defined mapping rules and integrity constraints.

Some Issues on Ontology Integration

Abstract: The word integration has been used with different meanings in the ontology field. This article aims at clarifying the meaning of the word “integration” and presenting some of the relevant work done in integration. We identify three meanings of ontology “integration”: when building a new ontology reusing (by assembling, extending, specializing or adapting) other ontologies already available; when building an ontology by merging several ontologies into a single one that unifies all of them; when building an application using one or more ontologies. We discuss the different meanings of “integration”, identify the main characteristics of the three different processes and proposethree words to distinguish among those meanings:integration, merge and use.

Ontology-driven geographic information systems

Abstract: Information integration is the combination of different types of information in a framework so that it can be queried, retrieved, and manipulated. Integration of geographic data has gained in importance because of the new possibilities arising from the interconnected world and the increasing availability of geographic information. Many times the need for information is so pressing that it does not matter if some details are lost, as long as integration is achieved. To integrate information across computerized information systems it is necessary first to have explicit formalizations of the mental concepts that people have about the real world. Furthermore, these concepts need to be grouped by communities in order to capture the basic agreements that exist within different communities. The explicit formalization of the mental models within a community is an ontology. This thesis introduces a framework for the integration of geographic information. We use ontologies as the foundation of this framework. By integrating ontologies that are linked to sources of geographic information we allow for the integration of geographic information based primarily on its meaning. Since the integration may occurs across different levels, we also create the basic mechanisms for enabling integration across different levels of detail. The use of an ontology, translated into an active, information-system component, leads Ontology-Driven Geographic Information Systems. The results of this thesis show that a model that incorporates hierarchies and roles has the potential to integrate more information than models that do not incorporate these concepts. We developed a methodology to evaluate the influence of the use of roles and of hierarchical structures for representing ontologies on the potential for information integration. The use of a hierarchical structure increases the potential for information integration. The use of roles also improves the potential for information integration, although to a much lesser extent than did the use of hierarchies. The combined effect of roles and hierarchies had a more positive effect in the potential for information integration than the use of roles alone or hierarchies alone. These three combinations (hierarchies, roles, roles and hiearchies) gave better results than the results using neither roles nor hierarchies.

An ontology for bioinformatics applications.

Abstract: Motivation: An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. Results: This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontology's design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. Availability: The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL. Contact: tambis@cs.man.ac.uk.

SMART: Automated Support for Ontology Merging and Alignment

Abstract: As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user’s actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms. 1. Merging Versus Alignment In recent years, researchers have developed many ontologies. These different groups of researchers are now beginning to work with one another, so they must bring together these disparate source ontologies. Two approaches are possible: (1) merging the ontologies to create a single coherent ontology, or (2) aligning the ontologies by establishing links between them and allowing the aligned ontologies to reuse information from one another. As an illustration of the possible processes that establish correspondence between different ontologies, we consider the ontologies that natural languages embody. A researcher trying to find common ground between two such languages may perform one of several tasks. He may create a mapping between the two languages to be used in, say, a machine-translation system. Differences in the ontologies underlying the two languages often do not allow simple one-to-one correspondence, so a mapping must account for these differences. Alternatively, Esperanto language (an international language that was constructed from words in different European languages) was created through merging: All the languages and their underlying ontologies were combined to create a single language. Aligning languages (ontologies) is a third task. Consider how we learn a new domain language that has an extensive vocabulary, such as the language of medicine. The new ontology (the vocabulary of the medical domain) needs to be linked in our minds to the knowledge that we already have (our existing ontology of the world). The creation of these links is alignment. We consider merging and alignment in our work that we describe in this paper. For simplicity, throughout the discussion, we assume that only two ontologies are being merged or aligned at any given time. Figure 1 illustrates the difference between ontology merging and alignment. In merging, a single ontology that is a merged version of the original ontologies is created. Often, the original ontologies cover similar or overlapping domains. For example, the Unified Medical Language System (Humphreys and Lindberg 1993; UMLS 1999) is a large merged ontology that reconciles differences in terminology from various machine-readable biomedical information sources. Another example is the project that was merging the top-most

An overview of the ONIONS project: applying ontologies to the integration of medical terminologies

Abstract: The paper presents a review of the ONIONS project. ONIONS is committed to developing a large-scale, axiomatized ontology library for medical terminology. The developed methodology exploits a description logic-based design for the modules in the library and makes extended use of generic theories, thus creating a stratification of the modules. Terminological knowledge is acquired by conceptual analysis and ontology integration over a set of authoritative sources. After addressing general issues about conceptual analysis and integration, the methodology is briefly described. The central part of the paper presents the investigation we have made on the 476,000 medical concepts singled out by the National Library of Medicine as the Metathesaurus TM in the UMLS project. This is followed by several case studies concerning lexical polysemy, the interface between ontologies. A section describing the current structure of the library and the generic theories reused is provided. Current results of our research include the integration of some top-level ontologies in the ON9.2 ontology library, and the formalization of the terminological knowledge in the UMLS Metathesaurus.

Ontology-Based Geographic Data Set Integration

Abstract: In order to develop a system to propagate updates we investigate the semantic and spatial relationships between independently produced geographic data sets of the same region (data set integration). The goal of this system is to reduce operator intervention in update operations between corresponding (semantically similar) geographic object instances. Crucial for this reduction is certainty about the semantic similarity of different object representations. In this paper we explore a framework for ontology-based geographic data set integration, an ontology being a collection of shared concepts. Components of this formal approach are an ontology for topographic mapping (a domain ontology), an ontology for every geographic data set involved (the application ontologies), and abstraction rules (or capture criteria). Abstraction rules define at the class level the relationships between domain ontology and application ontology. Using these relationships, it is possible to locate semantic similarity at the object instance level with methods from computational geometry (like overlay operations). The components of the framework are formalized in the Prolog language, illustrated with a fictitious example, and tested on a practical example.

An Algorithm for Merging and Aligning Ontologies: Automation and Tool Support

Abstract: As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user’s actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms. 1 Merging Versus Alignment In recent years, researchers have developed many ontologies. These different groups of researchers are now beginning to work with one another, so they must bring together these disparate source ontologies. Two approaches are possible: (1) merging the ontologies to create a single coherent ontology, or (2) aligning the ontologies by establishing links between them and allowing them to reuse information from one another. As an illustration of the possible processes that establish correspondence between different ontologies, we consider the ontologies that natural languages embody. A researcher trying to find common ground between two such languages may perform one of several tasks. He may create a mapping between the two languages to be used in, say, a machine-translation system. Differences in the ontologies underlying the two languages often do not allow simple one-to-one correspondence, so a mapping must account for these differences. Alternatively, Esperanto language (an international language that was constructed from words in different European languages) was created through merging: All the languages and their underlying ontologies were combined to create a single language. Aligning languages (ontologies) is a third task. Consider how we learn a new domain language that has an extensive vocabulary, such as the language of medicine. The new ontology (the vocabulary of the medical domain) needs to be linked in our minds to the knowledge that we already have (our existing ontology of the world). The creation of these links is alignment. We consider merging and alignment in this paper. For simplicity, throughout the discussion, we assume that only two ontologies are being merged or aligned at any given time. Figure 1 illustrates the difference between ontology merging and alignment. In merging, a single ontology that is a merged version of the original ontologies is created. Often, the original ontologies cover similar or overlapping domains. For example, the Unified Medical Language System (Humphreys and Lindberg 1993; UMLS 1999) is a large merged ontology that reconciles differences in terminology from various machine-readable biomedical information sources. Another example is the project that was merging the top-most levels of two general commonsense-knowledge ontologies—SENSUS (Knight and Luk 1994) and Cyc (Lenat 1995)—to create a single top-level ontology of world knowledge (Hovy 1997). In alignment, the two original ontologies persist, with links established between them. Alignment usually is performed when the ontologies cover domains that are complementary to each other. For example, part of the High Performance Knowledge Base (HPKB) program sponsored by the Defense Advanced Research Projects Agency (DARPA) (Cohen et al. 1999) is structured around one central ontology, the Cyc knowledge base (Lenat 1995). Several teams of researchers develop ontologies in the domain of military tactics to cover the types of military units and weapons, tasks the units can perform, constraints on the units and tasks, and so on. These developers then align these more domain-specific ontologies to Cyc by establishing links into Cyc’c upperand middle-level ontologies. The domain-specific ontologies do not become part of the Cyc knowledge base; rather, they are separate ontologies that include Cyc and use its top-level distinctions. 1 Most knowledge representation systems would require one ontology to be included in the other for the links to be established.

Ontological Reengineering for Reuse

Abstract: This paper presents the concept of Ontological Reengineering as the process of retrieving and transforming a conceptual model of an existing and implemented ontology into a new, more correct and more complete conceptual model which is reimplemented. Three activities have been identified in this process: reverse engineering, restructuring and forward engineering. The aim of Reverse Engineering is to output a possible conceptual model on the basis of the code in which the ontology is implemented. The goal of Restructuring is to reorganize this initial conceptual model into a new conceptual model, which is built bearing in mind the use of the restructured ontology by the ontology/application that reuses it. Finally, the objective of Forward Engineering is output a new implementation of the ontology. The paper also discusses how the ontological reengineering process has been applied to the Standard-Units ontology [18], which is included in a Chemical-Elements [12] ontology. These two ontologies will be included in a Monatomic-Ions and Environmental-Pollutants ontologies.

Coping with Changing Ontologies in a Distributed Environment

Abstract: We discuss the problems associated with versioning ontologies in distributed environments. This is an important issue because ontologies can be of great use in structuring and querying intemet information, but many of the Intemet’s characteristics, such as distributed ownership, rapid evolution, and heterogeneity, make ontology management difficult. We present SHOE, a web-based knowledge representation language that supports multiple versions of ontologies. We then discuss the features of SHOE that address ontology versioning, the affects of ontology revision on SHOE web pages, and methods for implementing ontology integration using SHOE’s extension and version mechanisms.

Ontological Modelling for Designing Educational Systems

Abstract: Constructing ontologies in educational design is not really new. The specification of educational goals is what is called now a days an ontology. Although content has always been considered a crucial factor in education, the emphasis in educational research has been on form, as is also pointed out by [Mizoguchi et al., 1997]. Ontological engineering for constructing educational systems may look like putting the same old wine in new barrels, but we should be aware that that these new barrels may give a new flavour to this wine. As an example we discuss a core ontology about law, used in the development of educational systems. 1 A core ontology mediates a top ontology, that reflects our common sense understanding of the world, and an ontology that defines the concepts and structures in a domain. A core ontology tells us what a domain is about. The core ontology discussed is FOLaw [Valente, 1995], a functional ontology of law, as applied in PROSA, a system that trains students to solve problems (cases) in adminstrative law. A major conclusion is that emphasis has shifted from skill acquisition to obtaining insight and understanding. Anaother benefit of this ‘ontological view’ is that types of knowledge distinguished in core ontologies can make up categories that provide the similar decompositions as task analyses, but apparently in a more ‘natural’ way.

Combining artificial intelligence and databases for data integration

Abstract: Data integration is a problem at the intersection of the fields of Artificial Intelligence and Database Systems. The goal of a data integration system is to provide a uniform interfacc to a multitude of data sources, whether they are within one enterprise or on the World-Wide Web. The key challenges in data integration arise because the data sources being integrated have been designed independently for autonomous applications, and their contents are related in subtle ways. As a result, a data integration system requires rich formalisms for describing contents of data sources and relating between contents of different sources. This paper discusses works aimed at applying techniques from Artificial Intelligence to the problem of data integration. In addition to employing Knowledge Representation techniques for describing contents of information sources, projects have also made use of Machine Learning techniques for extracting data from sources and planning techniques for query optimization. The paper also outlines future opportunities for applying AI techniques in the context of data integration.

Personal ontologies

Abstract: Corporations can suffer from too much information, and it is often inaccessible, inconsistent, and incomprehensible. The corporate solution entails knowledge management techniques and data warehouses. The paper discusses the use of the personal ontology. The promising approach is an organization scheme based on a model of an office and its information, an ontology, coupled with the proper tools for using it.

Extracting ontologies from legacy systems for understanding and re-engineering

Abstract: Ontology has been investigated in the context of knowledge sharing among heterogeneous and disparate database and knowledge base systems. Our recent study and experiments suggest that ontology also have a great potential for legacy software understanding and re-engineering. In this paper we consider an ontology to be composed of four elements: classes, relations, functions and instances. We show these four elements forming an ontology for a legacy system can be extracted from the code of the concerned system using the existing software re-engineering tools. We then present our vision how the obtained ontology can be applied to understanding and eventually better re-engineering the legacy systems.

A Discovery-Based Approach to Database Ontology Design

Abstract: In this paper, we introduce an approach to task-driven ontology design which is based on information discovery from database schemas. Techniques for semi-automatically discovering terms and relationships used in the information space, denoting concepts, their properties and links are proposed, which are applied in two stages. At the first stage, the focus is on the discovery of heterogeneity/ambiguity of data representations in different schemas. For this purpose, schema elements are compared according to defined comparison features and similarity coefficients are evaluated. This stage produces a set of candidates for unification into ontology concepts. At the second stage, decisions are made on which candidates to unify into concepts and on how to relate concepts by semantic links. Ontology concepts and links can be accessed according to different perspectives, so that the ontology can serve different purposes, such as, providing a search space for powerful mechanisms for concept location, setting a basis for query formulation and processing, and establishing a reference for recognizing terminological relationships between elements in different schemas.

Semantic Ontology Tools in Information System Design

Abstract: The availability of computerized lexicons, thesauri and "ontologies" –we discuss this termi nology– makes it possible to formalize semantic aspects of information as used in the analysis, design and implementation of information systems (and in fact general software systems) in new and useful ways. We survey a selection of relevant ongoing work, discuss different issues of semantics that arise, and characterize the resulting computerized information systems, called CLASS for Computer-Lexicon Assisted Software Systems. The need for a "global" common ontology (lexicon, thesaurus) is conjectured, and some desirable properties are proposed. We give a few examples of such CLASS-s and indicate avenues of current and future research in this area. In particular, certain problems can be identified with well-known existing lexicons such as CYC and WordNet, as well as with sophisticated representationand inference engines such as KIF or SHOE. We argue nevertheless that large public lexicons should be simple, i.e. their semantics become implicit by agreement among "all" users, and ideally completely application independent. In short, the lexicon or thesaurus then becomes the semantic domain for

Toward principles for the representation of hierarchical knowledge in formal ontologies

Abstract: Early ontological engineering methodologies have necessarily focussed on the management of the whole ontology development process. There is a corresponding need to provide advice to the ontological engineer on the finer details of ontology construction. Here, we specifically address the representation of hierarchical relationships in an ontology. We identify five types of problem that may be encountered in moving from an informal description of a domain to a formal representation of hierarchical knowledge. Each problem type is discussed from the perspective of knowledge sharing and examples from biological ontologies are used to illustrate each type.

An experience with ontology clustering for information integration

Abstract: This article presents a structure of multiple shared ontologies to integrate heterogeneous sources This structure is intended to be easy to implement to maintain and to scale, and also close to the human model of conceptualisation The structure has been investigate in a small scale experiment set in the domain of the international coffee preparation The coffee-preparing domain is attractive as it serves to illustrate that different communities may share knowledge at different abstraction levels

Preprocessing and Integration of Data from Multiple Sources for Knowledge Discovery

Abstract: The explosive growth in the generation and collection of data has generated an urgent need for a new generation of techniques and tools that can assist in transforming these data intelligently and automatically into useful knowledge. Knowledge discovery is an emerging multidisciplinary field that attempts to fulfill this need. Knowledge discovery is a large process that includes data selection, cleaning, preprocessing, integration, transformation and reduction, data mining, model selection, evaluation and interpretation, and finally consolidation and use of the extracted knowledge. This paper addresses the issues of data cleaning and integration for knowledge discovery by proposing a systematic approach for resolving semantic conflicts that are encountered during the integration of data from multiple sources. Illustrated with examples derived from military databases, the paper presents a heuristics-based algorithm for identifying and resolving semantic conflicts at different levels of information granularity.

An Experience with Ontology-based Agent Clustering

Abstract: This article presents a structure of multiple shared ontologies to integrate heterogeneous sources. This specific structure is intended to be easy to implement to maintain and to scale, and also close to the human model of conceptualisation. The structure has been investigate in a small scale experiment set in the domain of the international coffee preparation. The experiment has also addressed, an identification of the different types of heterogeneity that can affect the resources.

Object-oriented integration testing

Abstract: Objectdoriented software development is an evolutionary process, and hence the opportunities for integration are abundant. Conceptually, classes are encapsulation of data attributes and their associated functions. Software components are amalgamation of logically and/or physically related classes. A complete software system is also an aggregation of software components. All of these various integration levels warrant contemporary integration techniques. Traditional integration techniques towards the end of software development process do not suffice any more. Integration strategies are needed at class level, component level, subdsystem level, and system levels. Classes require integration of methods. Various types of class interaction mechanisms demand different testing strategies. Integration of classes into components presses its own integration requirements. Finally, the system integration demands different types of integration testing strategies. This paper discusses the various integration levels prevalent in objectdoriented software development. The integration requirements of each level are met by suggesting a solution for the same. An integration framework for integrating classes into a system is also proposed.

Design support for database federations

Abstract: Federated database systems provide a homogeneous interface to possibly heterogeneous local database systems. This homogeneous interface consists of a global schema which is the result of a logical integration of the schemata of the corresponding local database systems and file systems. In this paper, we sketch the integration process and a set of tools for supporting the design process. Besides the classical database schema integration, the design process for federated information systems requires the integration of other aspects like integrity rules, authorization policies and transactional processes. This paper reports on an integrated approach to tool support of several of these integration aspects. The different integration facets are linked via the database integration method GIM allowing a high degree of automatic integration steps.

Ontology Suitability for Uncertain Extraction of Information from Multi-Record Web Documents

Abstract: Ontology based data extraction from multi-record Web documents works well, but only if the ontology is suitable for the Web document. How do we know whether the ontology is suitable? To resolve this question, we present an approach based on three heuristics: density, schema, and grouping. We encode the first heuristic as a density function and use probabilistic models for the second and third. We argue that these heuristics and our computational models for these heuristics correctly determine the suitability of a Web document for a given ontology.

Design Support for Database Federations

Abstract: Federated database systems provide a homogeneous interface to possibly heterogeneous local database systems. This homogeneous interface consists of a global schema which is the result of a logical integration of the schemata of the corresponding local database systems and file systems. In this paper, we sketch the integration process and a set of tools for supporting the design process. Besides the classical database schema integration, the design process for federated information systems requires the integration of other aspects like integrity rules, authorization policies and transactional processes. This paper reports on an integrated approach to tool support of several of these integration aspects. The different integration facets are linked via the database integration method GIM allowing a high degree of automatic integration steps.

A Generic Ontology for Spatial Reasoning

Abstract: In this paper we describe a generic ontology to support N-dimensional spatial reasoning applications. The ontology is intended to support both quantitative and qualitative approaches and is expressed using set notation. Using the ontology; spatial domains of discourse, spatial objects and their attributes, and the relationships that can link spatial objects can be expressed in terms of sets, and sets of sets. The ontology has been developed through a series of application studies. For each study a directed application ontology was first developed which was then merged into the generic ontology. Application areas that have been investigated include: Geographic Information Systems (GIS), noise pollution monitoring, environmental impact assessment, shape fitting, timetabling and scheduling, and AI problems such as the N-queens problem.

How to find suitable ontologies using an ontology-based WWW broker

Abstract: Knowledge reuse by means of ontologies now faces three important problems: (1) there are no standardized identifying features that characterize ontologies from the user point of view; (2) there are no web sites using the same logical organization, presenting relevant information about ontologies; and (3) the search for appropriate ontologies is hard, time-consuming and usually fruitless. To solve the above problems, we present: (1) a living set of features that allow us to characterize ontologies from the user point of view and have the same logical organization; (2) a living domain ontology about ontologies (called Reference Ontology) that gathers, describes and has links to existing ontologies; and (3) Reference Ontology as a source of its knowledge and retrieves descriptions of ontologies that satisfy a given set of constraints. (ONTO)2 Agent is available at http://delicias.dia.fi.upm.es/REFERENCE ONTOLOGY/