Showing papers on "Ontology-based data integration published in 2004"

Semantic integration: a survey of ontology-based approaches

Abstract: 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

SOUPA: standard ontology for ubiquitous and pervasive applications

Abstract: We describe a shared ontology called SOUPA - standard ontology for ubiquitous and pervasive applications. SOUPA is designed to model and support pervasive computing applications. This ontology is expressed using the Web ontology language OWL and includes modular component vocabularies to represent intelligent agents with associated beliefs, desires, and intentions, time, space, events, user profiles, actions, and policies for security and privacy. We discuss how SOUPA can be extended and used to support the applications of CoBrA, a broker-centric agent architecture for building smart meeting rooms, and MoGATU, a peer-to-peer data management for pervasive environments.

SNAP and SPAN: Towards Dynamic Spatial Ontology

Abstract: (2004). SNAP and SPAN: Towards Dynamic Spatial Ontology. Spatial Cognition & Computation: Vol. 4, No. 1, pp. 69-104.

Ontology Evolution: Not the Same as Schema Evolution

Abstract: As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.

Ontology Matching: A Machine Learning Approach

Abstract: This chapter studies ontology matching: the problem of finding the semantic mappings between two given ontologies. This problem lies at the heart of numerous information processing applications. Virtually any application that involves multiple ontologies must establish semantic mappings among them, to ensure interoperability. Examples of such applications arise in myriad domains, including e-commerce, knowledge management, e-learning, information extraction, bio-informatics, web services, and tourism (see Part D of this book on ontology applications).

Ontology Mapping – An Integrated Approach

Abstract: Ontology mapping is important when working with more than one ontology Typically similarity considerations are the basis for this In this paper an approach to integrate various similarity methods is presented In brief, we determine similarity through rules which have been encoded by ontology experts These rules are then combined for one overall result Several boosting small actions are added All this is thoroughly evaluated with very promising results

An ontology of time for the semantic web

Abstract: In connection with the DAML project for bringing about the Semantic Web, an ontology of time is being developed for describing the temporal content of Web pages and the temporal properties of Web services This ontology covers topological properties of instants and intervals, measures of duration, and the meanings of clock and calendar terms

Similarity-based ontology alignment in OWL-Lite

Abstract: Interoperability of heterogeneous systems on the Web will be admittedly achieved through an agreement between the underlying ontologies. However, the richer the ontology description language, the more complex the agreement process, and hence the more sophisticated the required tools. Among current ontology alignment paradigms, similarity-based approaches are both powerful and flexible enough for aligning ontologies expressed in languages like OWL. We define a universal measure for comparing the entities of two ontologies that is based on a simple and homogeneous comparison principle: Similarity depends on the type of entity and involves all the features that make its definition (such as superclasses, properties, instances, etc.). One-to-many relationships and circularity in entity descriptions constitute the key difficulties in this context: These are dealt with through local matching of entity sets and iterative computation of recursively dependent similarities, respectively.

Data Driven Ontology Evaluation

Abstract: The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

Ontologies: How can They be Built?

Abstract: Ontologies are an important component in many areas, such as knowledge management and organization, electronic commerce and information retrieval and extraction. Several methodologies for ontology building have been proposed. In this article, we provide an overview of ontology building. We start by characterizing the ontology building process and its life cycle. We present the most representative methodologies for building ontologies from scratch, and the proposed techniques, guidelines and methods to help in the construction task. We analyze and compare these methodologies. We describe current research issues in ontology reuse. Finally, we discuss the current trends in ontology building and its future challenges, namely, the new issues for building ontologies for the Semantic Web.

Biodynamic Ontology: Applying BFO in the Biomedical Domain

Abstract: We propose a modular formal ontology of the biomedical domain with two components, one for biological objects, corresponding broadly to anatomy, and one for biological processes, corresponding broadly to physiology. The result constitutes what might be described as a joint venture between two perspectives--of so-called three-dimensionalism and four-dimensionalism--which are normally regarded as incompatible. We outline an approach which allows them to be combined together, and provide examples of its application in biomedicine.

A hybrid approach for searching in the semantic web

Abstract: This paper presents a search architecture that combines classical search techniques with spread activation techniques applied to a semantic model of a given domain. Given an ontology, weights are assigned to links based on certain properties of the ontology, so that they measure the strength of the relation. Spread activation techniques are used to find related concepts in the ontology given an initial set of concepts and corresponding initial activation values. These initial values are obtained from the results of classical search applied to the data associated with the concepts in the ontology. Two test cases were implemented, with very positive results. It was also observed that the proposed hybrid spread activation, combining the symbolic and the sub-symbolic approaches, achieved better results when compared to each of the approaches alone.

A protégé plug-in for ontology extraction from text based on linguistic analysis

Abstract: In this paper we describe a plug-in (OntoLT) for the widely used Protege ontology development tool that supports the interactive extraction and/or extension of ontologies from text. The OntoLT approach provides an environment for the integration of linguistic analysis in ontology engineering through the definition of mapping rules that map linguistic entities in annotated text collections to concept and attribute candidates (i.e. Protege classes and slots). The paper ex-plains this approach in more detail and discusses some initial experiments on deriving a shallow ontology for the neurology domain from a corresponding collection of neurological scientific abstracts.

A probabilistic extension to ontology language OWL

Abstract: To support uncertain ontology representation and ontology reasoning and mapping, we propose to incorporate Bayesian networks (BN), a widely used graphic model for knowledge representation under uncertainty and OWL, the de facto industry standard ontology language recommended by W3C. First, OWL is augmented to allow additional probabilistic markups, so probabilities can be attached with individual concepts and properties in an OWL ontology. Secondly, a set of translation rules is defined to convert this probabilistically annotated OWL ontology into the directed acyclic graph (DAG) of a BN. Finally, the BN is completed by constructing conditional probability tables (CPT) for each node in the DAG. Our probabilistic extension to OWL is consistent with OWL semantics, and the translated BN is associated with a joint probability distribution over the application domain. General Bayesian network inference procedures (e.g., belief propagation or junction tree) can be used to compute P(C/spl bsol/e): the degree of the overlap or inclusion between a concept C and a concept represented by a description e. We also provide a similarity measure that can be used to find the most similar concept that a given description belongs to.

DILIGENT: towards a fine-grained methodology for distributed, loosely-controlled and evolving Engineering of ontologies

Abstract: Ontology engineering processes in truly distributed settings like the Semantic Web or global peer-to-peer systems may not be adequately supported by conventional, centralized ontology engineering methodologies. In this paper, we present our work towards the DILIGENT methodology, which is intended to support domain experts in a distributed setting to engineer and evolve ontologies with the help of a fine-grained methodological approach based on Rhetorical Structure Theory, viz. the DILIGENT model of ontology engineering by argumentation.

A formal knowledge management ontology: Conduct, activities, resources, and influences

Abstract: This article describes a collaboratively engineered general-purpose knowledge management (KM) ontology that can be used by practitioners, researchers, and educators. The ontology is formally characterized in terms of nearly one hundred definitions and axioms that evolved from a Delphi-like process involving a diverse panel of over 30 KM practitioners and researchers. The ontology identifies and relates knowledge manipulation activities that an entity (e.g., an organization) can perform to operate on knowledge resources. It introduces a taxonomy for these resources, which indicates classes of knowledge that may be stored, embedded, and/or represented in an entity. It recognizes factors that influence the conduct of KM both within and across KM episodes. The Delphi panelists judge the ontology favorably overall: its ability to unify KM concepts, its comprehensiveness, and utility. Moreover, various implications of the ontology for the KM field are examined as indicators of its utility for practitioners, educators, and researchers.

State of the art on ontology alignment

Abstract: In this document we provide an overall view of the state of the art in ontology alignment. It is organised as a description of the need for ontology alignment, a presentation of the techniques currently in use for ontology alignment and a presentation of existing systems. The state of the art is not restricted to any discipline and consider as some form of ontology alignment the work made on schema matching within the database area for instance. Keyword list: schema matching, ontology matching, ontology alignment, ontology mapping, schema mapping, Copyright c 2004 The contributors Document Identifier KWEB/2004/D2.2.3/v1.2 Project KWEB EU-IST-2004-507482 Version v1.2 Date August 2, 2004 State final Distribution public Knowledge Web Consortium This document is part of a research project funded by the IST Programme of the Commission of the European Communities as project number IST-2004-507482. University of Innsbruck (UIBK) Coordinator Institute of Computer Science Technikerstrasse 13 A-6020 Innsbruck Austria Contact person: Dieter Fensel E-mail address: dieter.fensel@uibk.ac.at École Polytechnique Fédérale de Lausanne (EPFL) Computer Science Department Swiss Federal Institute of Technology IN (Ecublens), CH-1015 Lausanne Switzerland Contact person: Boi Faltings E-mail address: boi.faltings@epfl.ch France Telecom (FT) 4 Rue du Clos Courtel 35512 Cesson Sévigné France. PO Box 91226 Contact person : Alain Leger E-mail address: alain.leger@rd.francetelecom.com Freie Universität Berlin (FU Berlin) Takustrasse 9 14195 Berlin Germany Contact person: Robert Tolksdorf E-mail address: tolk@inf.fu-berlin.de Free University of Bozen-Bolzano (FUB) Piazza Domenicani 3 39100 Bolzano Italy Contact person: Enrico Franconi E-mail address: franconi@inf.unibz.it Institut National de Recherche en Informatique et en Automatique (INRIA) ZIRST 655 avenue de l’Europe Montbonnot Saint Martin 38334 Saint-Ismier France Contact person: Jérôme Euzenat E-mail address: Jerome.Euzenat@inrialpes.fr Centre for Research and Technology Hellas / Informatics and Telematics Institute (ITI-CERTH) 1st km Thermi Panorama road 57001 Thermi-Thessaloniki Greece. Po Box 361 Contact person: Michael G. Strintzis E-mail address: strintzi@iti.gr Learning Lab Lower Saxony (L3S) Expo Plaza 1 30539 Hannover Germany Contact person: Wolfgang Nejdl E-mail address: nejdl@learninglab.de National University of Ireland Galway (NUIG) National University of Ireland Science and Technology Building University Road Galway Ireland Contact person: Christoph Bussler E-mail address: chris.bussler@deri.ie The Open University (OU) Knowledge Media Institute The Open University Milton Keynes, MK7 6AA United Kingdom Contact person: Enrico Motta E-mail address: e.motta@open.ac.uk Universidad Politécnica de Madrid (UPM) Campus de Montegancedo sn 28660 Boadilla del Monte Spain Contact person: Asunción Gómez Pérez E-mail address: asun@fi.upm.es University of Karlsruhe (UKARL) Institut für Angewandte Informatik und Formale Beschreibungsverfahren AIFB Universität Karlsruhe D-76128 Karlsruhe Germany Contact person: Rudi Studer E-mail address: studer@aifb.uni-karlsruhe.de University of Liverpool (UniLiv) Chadwick Building, Peach Street L697ZF Liverpool United Kingdom Contact person: Michael Wooldridge E-mail address: M.J.Wooldridge@csc.liv.ac.uk University of Manchester (UoM) Room 2.32. Kilburn Building, Department of Computer Science, University of Manchester, Oxford Road Manchester, M13 9PL United Kingdom Contact person: Carole Goble E-mail address: carole@cs.man.ac.uk University of Sheffield (USFD) Regent Court, 211 Portobello street S14DP Sheffield United Kingdom Contact person: Hamish Cunningham E-mail address: hamish@dcs.shef.ac.uk University of Trento (UniTn) Via Sommarive 14 38050 Trento Italy Contact person: Fausto Giunchiglia E-mail address: fausto@dit.unitn.it Vrije Universiteit Amsterdam (VUA) De Boelelaan 1081a 1081HV. Amsterdam The Netherlands Contact person: Frank van Harmelen E-mail address: Frank.van.Harmelen@cs.vu.nl Vrije Universiteit Brussel (VUB) Pleinlaan 2, Building G1

Learning ontologies from natural language texts

Abstract: Research on ontology is becoming increasingly widespread in the computer science community. The major problems in building ontologies are the bottleneck of knowledge acquisition and time-consuming construction of various ontologies for various domains/ applications. Meanwhile moving toward automation of ontology construction is a solution.We proposed an automatic ontology building approach. In this approach, the system starts from a small ontology kernel and constructs the ontology through text understanding automatically. The kernel contains the primitive concepts, relations and operators to build an ontology. The features of our proposed model are being domain/application independent, building ontologies upon a small primary kernel, learning words, concepts, taxonomic and non-taxonomic relations and axioms and applying a symbolic, hybrid ontology learning approach consisting of logical, linguistic based, template driven and semantic analysis methods.Hasti is an ongoing project to implement and test the automatic ontology building approach. It extracts lexical and ontological knowledge from Persian (Farsi) texts.In this paper, at first, we will describe some ontology engineering problems, which motivated our approach. In the next sections, after a brief description of Hasti, its features and its architecture, we will discuss its components in detail. In each part, the learning algorithms will be described. Then some experimental results will be discussed and at last, we will have an overview of related works and will introduce a general framework to compare ontology learning systems and will compare Hasti with related works according to the framework.

Information access using ontologies

Abstract: A method for data access includes defining an ontology for application to a set of diverse data sources (58) comprising data having predefined semantics, and associating with the ontology one or more logical rules applicable to the semantics of the data in the data sources. Upon receiving a query from a user regarding the data, a query plan is determined for responding to the query by selecting one or more of the data sources responsively to the ontology and by identifying an operation to be applied to the data responsively to the applicable logical rules. A response to the query is then generated in accordance with the query plan. 2

Ontology versioning in an ontology management framework

Abstract: Ontologies have become ubiquitous in information systems. They constitute the semantic Web's backbone, facilitate e-commerce, and serve such diverse application fields as bioinformatics and medicine. As ontology development becomes increasingly widespread and collaborative, developers are creating ontologies using different tools and different languages. These ontologies cover unrelated or overlapping domains at different levels of detail and granularity. A uniform framework, which we present here, helps users manage multiple ontologies by leveraging data and algorithms developed for one tool in another. For example, by using an algorithm we developed for structural evaluation of ontology versions, this framework lets developers compare different ontologies and map similarities and differences among them. Multiple-ontology management includes these tasks: maintain ontology libraries, import and reuse ontologies, translate ontologies from one formalism to another, support ontology versioning, specify transformation rules between different ontologies and version, merge ontologies, align and map between ontologies, extract an ontology's self-contained parts, support inference across multiple ontologies, support query across multiple ontologies.

Value Webs: using ontologies to bundle real-world services

Abstract: Real-world services - that is, nonsoftware-based services - differ significantly from Web services, usually defined as software functionality accessible and configurable over the Web. Because of the economic, social, and business importance of the service concept in general, we believe it's necessary to rethink what this concept means in an ontological and computational sense. We deal about the OBELIX (ontology-based electronic integration of complex products and value chains) project has therefore developed a generic component-based ontology for real-world services. This OBELIX service ontology is first of all a formalization of concepts that represent the consensus in the business science literature on service management and marketing. We express our service ontology in a graphical, network-style representation, and we've developed support tools that facilitate end-user modeling of services. Then, automated knowledge-based configuration methods let business designers and analysts analyze service bundles. We've tested our ontology, methods, and tools on applications in real-world case studies of different industry sectors.

Semantic conflict resolution ontology (SCROL): an ontology for detecting and resolving data and schema-level semantic conflicts

Abstract: Establishing semantic interoperability among heterogeneous information sources has been a critical issue in the database community for the past two decades. Despite the critical importance, current approaches to semantic interoperability of heterogeneous databases have not been sufficiently effective. We propose a common ontology called semantic conflict resolution ontology (SCROL) that addresses the inherent difficulties in the conventional approaches, i.e., federated schema and domain ontology approaches. SCROL provides a systematic method for automatically detecting and resolving various semantic conflicts in heterogeneous databases. SCROL provides a dynamic mechanism of comparing and manipulating contextual knowledge of each information source, which is useful in achieving semantic interoperability among heterogeneous databases. We show how SCROL is used for detecting and resolving semantic conflicts between semantically equivalent schema and data elements. In addition, we present evaluation results to show that SCROL can be successfully used to automate the process of identifying and resolving semantic conflicts.

Integration of biological sources: current systems and challenges ahead

Abstract: This paper surveys the area of biological and genomic sources integration, which has recently become a major focus of the data integration research field. The challenges that an integration system for biological sources must face are due to several factors such as the variety and amount of data available, the representational heterogeneity of the data in the different sources, and the autonomy and differing capabilities of the sources.This survey describes the main integration approaches that have been adopted. They include warehouse integration, mediator-based integration, and navigational integration. Then we look at the four major existing integration systems that have been developed for the biological domain: SRS, BioKleisli, TAMBIS, and DiscoveryLink. After analyzing these systems and mentioning a few others, we identify the pros and cons of the current approaches and systems and discuss what an integration system for biologists ought to be.

Information Sharing on the Semantic Web

Abstract: The large-scale and almost ubiquitous availability of information has become as much of a curse as it is a blessing. The more information is available, the harder it is to locate any particular piece of it. And even when it has been successfully found, it is even harder still to usefully combine it with other information we may already possess. This problem occurs at many different levels, ranging from the overcrowded disks of our own PCs to the mass of unstructured information on the World Wide Web. It is commonly understood that this problem of information sharing can only be solved by giving computers better access to the semantics of the information. While it has been recognized that ontologies play a crucial role in solving the open problems, most approaches rely on the existence of well-established data structures. To overcome these shortcomings, Stuckenschmidt and van Harmelen describe ontology-based approaches for resolving semantic heterogeneity in weakly structured environments, in particular the World Wide Web. Addressing problems like missing conceptual models, unclear system boundaries, and heterogeneous representations, they design a framework for ontology-based information sharing in weakly structured environments like the Semantic Web. For researchers and students in areas related to the Semantic Web, the authors provide not only a comprehensive overview of the State of the art, but also present in detail recent research in areas like ontology design for information integration, metadata generation and management, and representation and management of distributed ontologies. For professionals in areas such as e-commerce (e.g., the exchange of product knowledge) and knowledge management (e.g., in large and distributed organizations), the book provides decision support on the use of novel technologies, information about potential problems, and guidelines for the successful application of existing technologies.

A framework to review the information integration of the enterprise

Abstract: Organizations face the challenging task of integrating their distributed organizational units, information systems, and business processes for improved operation and attainment of organizational goals. There is the difficulty of dealing with heterogeneous applications that use different formats (syntax) and apply different meanings (semantics) to the data. There is the difficulty of coordinating the workflow so as the disparate organizational units act as a harmonious whole. The broad scope of the enterprise integration problem precludes approaches that tackle the entire problem but rather requires approaches that address a limited but useful integration type. The various information integration types and how they are related to each other is poorly defined. This article presents an enterprise information integration framework that aims to coalesce parallel approaches towards integration so that the information integration problem can be better understood as a whole. The enterprise information integration...

An overview of methods and tools for ontology learning from texts

Abstract: Ontology learning aims at reducing the time and efforts in the ontology development process. In recent years, several methods and tools have been proposed to speed up this process using different sources of information and different techniques. In this paper, we have reviewed 13 methods and 14 tools for semi-automatically building ontologies from texts and their relationships with the techniques each method follows. The methods have been grouped according to the main techniques followed and three groups have been identified: one based on linguistics, one on statistics, and one on machine learning. Regarding the tools, the criterion for grouping them, which has been the main aim of the tool, is to distinguish what elements of the ontology can be learned with each tool. According to this, we have identified three kinds of tools: tools for learning relations, tools for learning new concepts, and assisting tools for building up taxonomies.

Why evaluate ontology technologies? Because it works!

Abstract: We deal with two types of ontology evaluation, content evaluation and ontology technology evaluation. Evaluating content is a must for preventing applications from using inconsistent, incorrect, or redundant ontologies. It's unwise to publish an ontology that one or more software applications will use without first evaluating it. A well-evaluated ontology won't guarantee the absence of problems, but it makes its use safer. Similarly, evaluating ontology technology eases its integration with other software environments, ensuring a correct technology transfer from the academic to the industrial world. We also discuss ontology libraries, ontology tool, and formal evaluation of ontology quality.

ON ONTOLOGY MATCHING PROBLEMS - for building a corporate Semantic Web in a multi-communities organization

Abstract: Ontologies are nowadays used in many domains such as Semantic Web, information systems... to represent meaning of data and data sources. In the framework of knowledge management in an heterogeneous organization, the materialization of the organizational memory in a “corporate semantic web” may require to integrate the various ontologies of the different groups of this organization. To be able to build a corporate semantic web in an heterogeneous, multi-communities organization, it is essential to have methods for comparing, aligning, integrating or mapping different ontologies. This paper proposes a new algorithm for matching two ontologies based on all the information available about the given ontologies (e.g. their concepts, relations, information about the structure of each hierarchy of concepts, or of relations), applying TF/IDF scheme (a method widely used in the information retrieval community) and integrating WordNet (an electronic lexical database) in the process of ontology matching.