DOCSLIB.ORG

The Alignment API 4.0 1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Alignment API 4.0 1 Undefined 1 (2010) 1–8 1 IOS Press The Alignment API 4.0 1 Editor(s): Sören Auer, Universität Leipzig, Germany Solicited review(s): Jens Lehmann, Universität Leipzig, Germany; Jun Zhao, University of Oxford, UK Open review(s): Prateek Jain, Wright State University, USA Jérôme David a, Jérôme Euzenat a;∗, François Scharffe a, and Cássia Trojahn dos Santos a a INRIA & LIG, 655 avenue de l’Europe, 38330 Montbonnot Saint-Martin, France Abstract. Alignments represent correspondences between entities of two ontologies. They are produced from the ontologies by ontology matchers. In order for matchers to exchange alignments and for applications to manipulate matchers and alignments, a minimal agreement is necessary. The Alignment API provides abstractions for the notions of network of ontologies, alignments and correspondences as well as building blocks for manipulating them such as matchers, evaluators, renderers and parsers. We recall the building blocks of this API and present here the version 4 of the Alignment API through some of its new features: ontology proxys, the expressive alignment language EDOAL and evaluation primitives. Keywords: Alignment API, Ontology matching, Ontology alignment, Alignment management, Alignment evaluation, Alignment language, EDOAL, Ontowrap, OntoSim 1. Motivation – from a semantic web point of view, as it is possi- ble to dynamically find and reuse existing align- Using ontologies is the privileged way to achieve ments; interoperability among heterogeneous systems within – from a software engineering point of view, as the semantic web. However, as ontologies are not nec- alignments can be passed from a program to an- other; essarily compatible, they may in turn need to be rec- – from an ontology engineering and management onciled. Ontology reconciliation often requires to find point of view, as they will evolve together with the correspondences between entities, e.g., classes, ob- the ontology life cycle. jects, properties, occurring in these ontologies. Alignments are sets of correspondences between el- In order for alignments produced by any means to ements of two ontologies [13]. They are generated by be treated uniformly and for matchers and applications hand or by ontology matchers and they can be used to consume and produce such alignments, we designed for merging ontologies, transforming queries or link- a Java API for alignments: the Alignment API [9]. ing data sets. During their lifecycle, alignments may be The Alignment API is a set of abstractions for ex- written in files and further read, applied various thresh- pressing, accessing and sharing ontology alignments. olds, merged together and finally transformed into a Its reference implementation provides a minimal im- more operational format. plementation of this interface. It aims at facilitating the development of tools manipulating alignments and Considering ontology alignments as first class citi- calling matchers. It is also a way for matcher devel- zens has several benefits: opers to deliver alignments in a well-supported frame- work. We describe in this paper the version 4.0 of the 1Partial support provided by European Network of Excellence Knowledge web (FP6-IST-507482). Alignment API which has been deeply enhanced since *Corresponding author. E-mail: [email protected] its first version in 2003. We emphasise those new fea- 0000-0000/10/$00.00 c 2010 – IOS Press and the authors. All rights reserved 2 J. David, J. Euzenat, F. Scharffe and C. Trojahn / The Alignment API 4.0 Evaluator tures which were not available for [9]. After describing OntologyNetwork eval() the API itself (§2), we describe more precisely areas align1, align2 of major enhancement such as the abstraction from on- tologies and concrete measures (§3), the development 1..1 of an expressive alignment language (§4) and the eval- alignments 0..* ontologies uation of alignments (§5). Alignment 0..* onto1, onto2 id: uri Ontology type: string 1..1 level: string entities 2. The Alignment API in a nutshell map 0..* 0..* The Alignment API itself is a reduced set of Java Cell entity1 interfaces. The most important parts of the API are the id: uri Object representation interfaces which provide access to in- conf : double 1..1 formation about the API elements. These interfaces are relation first presented before considering the process interface 1..1 AlignmentProcess Relation and the reference implementation of the API. align() 2.1. Representation classes Fig. 1. Relations between the main classes of the Alignment API and The Alignment API offers four main representa- the classes of Ontowrap (Ontology). Only the main attributes and tional classes whose structure is represented in Fig- methods are presented. ure 1. 2.2. Processing OntologyNetwork is a container for a set of ontologies and a set of alignments. It makes it easy to retrieve In addition to being a storage structure, the Align- alignments tied to an ontology as well as to ma- ment API provides a minimal processing structure, nipulate them as a network, i.e., traversing them, which can be used by applications for manipulating closing them, etc. and consuming alignments. We present its most impor- Alignment is the main class of the API. An Alignment tant classes: is mostly made of a set of Cells and metadata AlignmentProcess is the interface for all matchers. about the alignment, such as the aligned ontolo- Matching two ontologies is achieved in two steps: gies, the alignment arity, provenance metadata, creating an instance of an Alignment implement- and any other metadata that can be tied to an ing AlignmentProcess and initialising it with alignment. two Ontology instances, then calling the align() Cell represents a correspondence: it relates two en- method. This method takes two arguments: an ini- tities with a Relation. The entities may be any tial Alignment which may be considered by the identified element of an Ontology (see §3) or matcher and a Property object providing param- any construct from the expressive EDOAL lan- eters for the matcher. guage (see §4). In addition, Cell supports any Evaluator is the interface for alignment evaluators type of additional metadata (including confidence which compare a first Alignment which may be values). taken as the reference and a second Alignment Relation represents the relation between two entities. (see §5). The set and type of relations are extensible in the AlignmentVisitor or Renderer is the interface for Alignment API and its implementation. defining Alignment visitors which can output These classes provide access to the information in alignments in different formats, when calling the instances. They also provide local methods for ma- render() method of Alignment. nipulating this information: adding correspondences These operations are presented in Figure 2. to alignments, cutting correspondences under a confi- dence threshold, etc. 2.3. API implementation An RDF vocabulary and an RDF/XML format, cor- responding to the Alignment class, can be used for se- Together with the API definition, a reference imple- rialising alignments. mentation of the Alignment API is available. It pro- J. David, J. Euzenat, F. Scharffe and C. Trojahn / The Alignment API 4.0 3 – a library of wrappers for several ontology APIs A0 (see §3); AlProcess Finally, the API implementation provides tools for align(p) eval(p) cut() manipulating alignments such as the batch utilities de- scribed in §5 or the AlignmentParser which can parse A Evaluator an RDF file in the Alignment format, or the EDOAL extension of this format (see §4), and return the corre- sponding Alignment. read() render() harden() RDF XSLT RDF 3. Abstract support for ontologies and distances AlParser SKOS OWL The implementation of the Alignment API relies on two external abstractions that we describe below: Fig. 2. The main operations of the Alignment API and associated Ontowrap provides uniform access to part of ontol- processing classes: alignments, evaluators and alignment networks. ogy APIs useful for matchers; OntoSim provides various distances and similarity vides, for each class of the API, a basic implementa- measures. It relies on Ontowrap. tion and offers the following services: – Storing, finding, and sharing alignments; 3.1. Ontowrap – Piping matching algorithms (improving an exist- ing alignment); There are many different APIs for ontologies. Even – Manipulating alignments (trimming under a thresh- if the Alignment API is independent from these APIs, old, merging, inverting, and hardening); it is often convenient to interact with them, in particu- – Generating processing output (transformations, lar when one wants to match ontologies. For that pur- axioms, rules); pose, we have designed the Ontowrap API which en- – Comparing alignments (finding differences). capsulates interactions with ontology APIs. The archi- tecture of Ontowrap has been designed to be easily ex- Thus, to quickly instantiate the API, it is sufficient tensible to other ontology APIs. to refine this basic implementation. This will take ad- The Ontowrap package defines the abstract factory vantage of all the services already implemented in the OntologyFactory which is used for generating ontolo- base implementation. It is also possible to define com- gies. Depending on the level of interaction needed with pletely new implementations of the API. The benefit of the ontologies, the factory may provide three levels of this is that such an implementation could still be used ontology interfaces, each one extending the previous by the tools which rely on the Alignment
Load more

Recommended publications
  • Internship Report — Mpri M2 Advances in Holistic Ontology Alignment
    Internship Report | Mpri M2 Advances in Holistic Ontology Alignment Internship Report | Mpri M2 Advances in Holistic Ontology Alignment Antoine Amarilli, supervised by Pierre Senellart, T´el´ecomParisTech General Context The development of the semantic Web has given birth to a large number of data sources (ontologies) with independent data and schemas. These ontologies are usually generated from existing relational databases or extracted from semi-structured data. Ontology alignment is a technique to automatically integrate them by discovering overlap in their instances and similarities in their schemas. Such integration makes it possible to access the combined information of all these data sources simultaneously rather than separately. Ontology alignment must deal with numerous challenges: the schemas are heterogeneous, the volume of data is huge, and the information can be partially incomplete or inconsistent. Problem Studied My internship focuses on the Paris (Probabilistic Alignment of Relations, Instances, and Schema) ontology alignment system [SAS11] developed by Fabian Suchanek, Pierre Senellart, and Serge Abiteboul at Inria Saclay's Webdam team. Paris is a generic system which aligns data and schema simultaneously (hence the adjective \holistic") and uses each of these alignments to cross-fertilize the other. The alignments produced are annotated with a confidence score and have a probabilistic interpretation. Paris was able to align large real-world datasets from the semantic Web. The aim of my internship is to propose improvements to Paris on some problems (both theoretical and practical) that were left open by the original system: achieving a better understanding of the behavior of Paris's equations, aligning heterogeneous schemas, being tolerant to differences in the strings of both ontologies, and improving the overall performance of the system.
    [Show full text]
  • Wiktionary Matcher
    Wiktionary Matcher Jan Portisch1;2[0000−0001−5420−0663], Michael Hladik2[0000−0002−2204−3138], and Heiko Paulheim1[0000−0003−4386−8195] 1 Data and Web Science Group, University of Mannheim, Germany fjan, [email protected] 2 SAP SE Product Engineering Financial Services, Walldorf, Germany fjan.portisch, [email protected] Abstract. In this paper, we introduce Wiktionary Matcher, an ontology matching tool that exploits Wiktionary as external background knowl- edge source. Wiktionary is a large lexical knowledge resource that is collaboratively built online. Multiple current language versions of Wik- tionary are merged and used for monolingual ontology matching by ex- ploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. We show that Wiktionary can be used as external background knowledge source for the task of ontology matching with reasonable matching and runtime performance.3 Keywords: Ontology Matching · Ontology Alignment · External Re- sources · Background Knowledge · Wiktionary 1 Presentation of the System 1.1 State, Purpose, General Statement The Wiktionary Matcher is an element-level, label-based matcher which uses an online lexical resource, namely Wiktionary. The latter is "[a] collaborative project run by the Wikimedia Foundation to produce a free and complete dic- tionary in every language"4. The dictionary is organized similarly to Wikipedia: Everybody can contribute to the project and the content is reviewed in a com- munity process. Compared to WordNet [4], Wiktionary is significantly larger and also available in other languages than English. This matcher uses DBnary [15], an RDF version of Wiktionary that is publicly available5. The DBnary data set makes use of an extended LEMON model [11] to describe the data.
    [Show full text]
  • Term-Based Ontology Alignment
    Term-Based Ontology Alignment Virach Sornlertlamvanich, Canasai Kruengkrai, Shisanu Tongchim, Prapass Srichaivattana, and Hitoshi Isahara Thai Computational Linguistics Laboratory National Institute of Information and Communications Technology 112 Paholyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand {virach,canasai,shisanu,prapass}@tcllab.org, [email protected] Abstract. This paper presents an efficient approach to automatically align concepts between two ontologies. We propose an iterative algorithm that performs finding the most appropriate target concept for a given source concept based on the similarity of shared terms. Experimental results on two lexical ontologies, the MMT semantic hierarchy and the EDR concept dictionary, are given to show the feasibility of the proposed algorithm. 1 Introduction In this paper, we propose an efficient approach for finding alignments between two different ontologies. Specifically, we derive the source and the target on- tologies from available language resources, i.e. the machine readable dictionaries (MDRs). In our context, we consider the ontological concepts as the groups of lexical entries having similar or related meanings organized on a semantic hier- archy. The resulting ontology alignment can be used as a semantic knowledge for constructing multilingual dictionaries. Typically, bilingual dictionaries provide the relationship between their native language and English. One can extend these bilingual dictionaries to multilingual dictionaries by exploiting English as an intermediate source and associations between two concepts as semantic constraints. Aligningconceptsbetweentwoontologiesisoftendonebyhumans,whichis an expensive and time-consuming process. This motivates us to find an auto- matic method to perform such task. However, the hierarchical structures of two ontologies are quite different. The structural inconsistency is a common problem [1].
    [Show full text]
  • Wiktionary Matcher Results for OAEI 2020
    Wiktionary Matcher Results for OAEI 2020 Jan Portisch1;2[0000−0001−5420−0663] and Heiko Paulheim1[0000−0003−4386−8195] 1 Data and Web Science Group, University of Mannheim, Germany fjan, [email protected] 2 SAP SE Product Engineering Financial Services, Walldorf, Germany [email protected] Abstract. This paper presents the results of the Wiktionary Matcher in the Ontology Alignment Evaluation Initiative (OAEI) 2020. Wiktionary Matcher is an ontology matching tool that exploits Wiktionary as exter- nal background knowledge source. Wiktionary is a large lexical knowl- edge resource that is collaboratively built online. Multiple current lan- guage versions of Wiktionary are merged and used for monolingual on- tology matching by exploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. This is the second OAEI participation of the matching system. Wiktionary Matcher has been improved and is the best performing system on the knowledge graph track this year.3 Keywords: Ontology Matching · Ontology Alignment · External Re- sources · Background Knowledge · Wiktionary 1 Presentation of the System 1.1 State, Purpose, General Statement The Wiktionary Matcher is an element-level, label-based matcher which uses an online lexical resource, namely Wiktionary. The latter is "[a] collaborative project run by the Wikimedia Foundation to produce a free and complete dic- tionary in every language"4. The dictionary is organized similarly to Wikipedia: Everybody can contribute to the project and the content is reviewed in a com- munity process. Compared to WordNet [2], Wiktionary is significantly larger and also available in other languages than English. This matcher uses DBnary [13], an RDF version of Wiktionary that is publicly available5.
    [Show full text]
  • An Ontology-Based Approach to Manage Conflicts in Collaborative Design Moisés Lima Dutra
    An ontology-based approach to manage conflicts in collaborative design Moisés Lima Dutra To cite this version: Moisés Lima Dutra. An ontology-based approach to manage conflicts in collaborative design. Other [cs.OH]. Université Claude Bernard - Lyon I, 2009. English. NNT : 2009LYO10241. tel-00692473 HAL Id: tel-00692473 https://tel.archives-ouvertes.fr/tel-00692473 Submitted on 30 Apr 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE PRESENTEE DEVANT L’UNIVERSITE CLAUDE BERNARD LYON 1 POUR L’OBTENTION DU DIPLOME DE DOCTORAT EN INFORMATIQUE (ARRETE DU 7 AOUT 2006) PRESENTEE ET SOUTENUE PUBLIQUEMENT LE 27 NOVEMBRE 2009 PAR M. MOISÉS LIMA DUTRA AN ONTOLOGY-BASED APPROACH TO MANAGE CONFLICTS IN COLLABORATIVE DESIGN (Une approche basée sur les ontologies pour la gestion de conflits dans un environnement collaboratif) DIRECTEURS DE THESE : PARISA GHODOUS (UNIVERSITE CLAUDE BERNARD LYON 1) RICARDO GONÇALVES (“ORIENTADOR PELA FCT/UNL”, UNIVERSITE NOUVELLE DE LISBONNE) JURY : ARIS OUKSEL (RAPPORTEUR,PROFESSEUR DES UNIVERSITES, UNIVERSITE D’ILLINOIS,
    [Show full text]
  • Results of the Ontology Alignment Evaluation Initiative 2020⋆
    Results of the Ontology Alignment Evaluation Initiative 2020? Mina Abd Nikooie Pour1, Alsayed Algergawy2, Reihaneh Amini3, Daniel Faria4, Irini Fundulaki5, Ian Harrow6, Sven Hertling7, Ernesto Jimenez-Ruiz´ 8;9, Clement Jonquet10, Naouel Karam11, Abderrahmane Khiat12, Amir Laadhar10, Patrick Lambrix1, Huanyu Li1, Ying Li1, Pascal Hitzler3, Heiko Paulheim7, Catia Pesquita13, Tzanina Saveta5, Pavel Shvaiko14, Andrea Splendiani6, Elodie Thieblin´ 15,Cassia´ Trojahn16, Jana Vatasˇcinovˇ a´17, Beyza Yaman18, Ondrejˇ Zamazal17, and Lu Zhou3 1 Linkoping¨ University & Swedish e-Science Research Center, Linkoping,¨ Sweden fmina.abd.nikooie.pour,patrick.lambrix,huanyu.li,[email protected] 2 Friedrich Schiller University Jena, Germany [email protected] 3 Data Semantics (DaSe) Laboratory, Kansas State University, USA fluzhou,reihanea,[email protected] 4 BioData.pt, INESC-ID, Lisbon, Portugal [email protected] 5 Institute of Computer Science-FORTH, Heraklion, Greece fjsaveta,[email protected] 6 Pistoia Alliance Inc., USA fian.harrow,[email protected] 7 University of Mannheim, Germany fsven,[email protected] 8 City, University of London, UK [email protected] 9 Department of Informatics, University of Oslo, Norway [email protected] 10 LIRMM, University of Montpellier & CNRS, France fjonquet,[email protected] 11 Fraunhofer FOKUS, Berlin, Germany [email protected] 12 Fraunhofer IAIS, Sankt Augustin, Germany [email protected] 13 LASIGE, Faculdade de Ciencias,ˆ Universidade de Lisboa, Portugal [email protected] 14 TasLab, Trentino Digitale SpA, Trento, Italy [email protected] 15 Logilab, France [email protected] 16 IRIT & Universite´ Toulouse II, Toulouse, France [email protected] 17 University of Economics, Prague, Czech Republic fjana.vatascinova,[email protected] 18 ADAPT Centre, Dublin City University, Ireland beyza.yamanadaptcentre.ie Abstract.
    [Show full text]
  • User Validation in Ontology Alignment
    User validation in ontology alignment Zlatan Dragisic, Valentina Ivanova, Patrick Lambrix, Daniel Faria, Ernesto Jimenez-Ruiz and Catia Pesquita Book Chapter N.B.: When citing this work, cite the original article. Part of: The Semantic Web – ISWC 2016 : 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part I, Paul Groth, Elena Simperl, Alasdair Gray, Marta Sabou, Markus Krötzsch, Freddy Lecue, Fabian Flöck and Yolanda Gil (Eds), 2016, pp. 200-217. ISBN: 978-3-319-46522-7 Lecture Notes in Computer Science, 0302-9743 (print), 1611-3349 (online), No. 9981 DOI: http://dx.doi.org/10.1007/978-3-319-46523-4_13 Copyright: Springer Publishing Company Available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-131806 User validation in ontology alignment Zlatan Dragisic1, Valentina Ivanova1, Patrick Lambrix1, Daniel Faria2, Ernesto Jimenez-Ruiz´ 3, and Catia Pesquita4 1 Linkoping¨ University and the Swedish e-Science Research Centre, Sweden 2 Gulbenkian Science Institute, Portugal 3 University of Oxford, UK 4 LaSIGE, Faculdade de Ciencias,ˆ Universidade de Lisboa, Portugal Abstract. User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of automated alignment algorithms. In this paper we present a broad study on user validation of ontology alignments that encompasses three distinct but interrelated aspects: the profile of the user, the services of the alignment system, and its user interface. We discuss key issues pertaining to the alignment validation process under each of these aspects, and provide an overview of how current systems address them.
    [Show full text]
  • Abstract Ontology Alignment Techniques for Linked Open
    ABSTRACT ONTOLOGY ALIGNMENT TECHNIQUES FOR LINKED OPEN DATA ONTOLOGIES by Chen Gu Ontology alignment (OA) addresses the Semantic Web challenge to enable information interoperability between related but heterogeneous ontologies. Traditional OA systems have focused on aligning well defined and structured ontologies from the same or closely related domains and producing equivalence mappings between concepts in the source and target ontologies. Linked Open Data (LOD) ontologies, however, present some different characteristics from standard ontologies. For example, equivalence relations are limited among LOD concepts; thus OA systems for LOD ontology alignment should be able to produce subclass and superclass mappings between the source and target. This thesis overviews the current research on aligning LOD ontologies. An important research aspect is the use of background knowledge in the alignment process. Two current OA systems are modified to perform alignment of LOD ontologies. For each modified OA system, experiments have been performed using a set of LOD reference alignments to evaluate their alignment results using standard OA performance measures. ONTOLOGY ALIGNMENT TECHNIQUES FOR LINKED OPEN DATA ONTOLOGIES A Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Computer Science Department of Computer Science and Software Engineering by Chen Gu Miami University Oxford, Ohio 2013 Advisor________________________________ Valerie Cross, PhD. Reader_________________________________
    [Show full text]
  • Alignment Incoherence in Ontology Matching
    Alignment Incoherence in Ontology Matching Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universitat¨ Mannheim vorgelegt von Christian Meilicke aus Bensheim Mannheim, 2011 Dekan: Professor Dr. Heinz Jurgen¨ Muller,¨ Universitat¨ Mannheim Referent: Professor Dr. Heiner Stuckenschmidt, Universitat¨ Mannheim Koreferent: Directeur de recherche Dr. habil Jer´ omeˆ Euzenat, INRIA Grenoble Tag der mundlichen¨ Prufung:¨ 21.10.2011 iii Abstract Ontology matching is the process of generating alignments between ontologies. An alignment is a set of correspondences. Each correspondence links concepts and properties from one ontology to concepts and properties from another ontology. Obviously, alignments are the key component to enable integration of knowledge bases described by different ontologies. For several reasons, alignments contain often erroneous correspondences. Some of these errors can result in logical con- flicts with other correspondences. In such a case the alignment is referred to as an incoherent alignment. The relevance of alignment incoherence and strategies to resolve alignment incoherence are in the center of this thesis. After an introduction to syntax and semantics of ontologies and alignments, the importance of alignment coherence is discussed from different perspectives. On the one hand, it is argued that alignment incoherence always coincides with the incorrectness of correspondences. On the other hand, it is demonstrated that the use of incoherent alignments results in severe problems for different types of applications. The main part of this thesis is concerned with techniques for resolving align- ment incoherence, i.e., how to find a coherent subset of an incoherent alignment that has to be preferred over other coherent subsets. The underlying theory is the theory of diagnosis.
    [Show full text]
  • Agreement Technologies
    Agreement Technologies Action IC0801 Semantics in Agreement Technologies Editors: Antoine Zimmermann, George Vouros, Axel Polleres Presented by: Antoine Zimmermann http://www.agreement-technologies.eu Semantics l Originally, semantics is the study of meaning l Semantics defines the relationship between symbols and what they denote Syntax Semantics means Apple (or refers to) Agreement Technologies: Applications Semantics in computer science l Formal semantics does not give access to the true meaning of symbols; l Formal semantics only constrain how a symbol can possibly be interpreted; l This allows computer systems to make automatic deductions. Universe Apple ⊆ Fruit Fruit Apple Granny-Smith ∊ Apple Granny-Smith ∊ Fruit Agreement Technologies: Applications Semantics in computer science l Formal semantics does not give access to the true meaning of symbols; l Formal semantics only constrain how a symbol can possibly be interpreted; l This allows computer systems to make automatic deductions. Universe ? ? XYZ ⊆ ABC XYZ ABC foo ∊ XYZ ? ? ? ? ? foo foo ∊ ABC ? ? ? ? ? ? ? ? Agreement Technologies: Applications Agreeing on a formal semantics Semantic Web l Brings standards from the W3C: − A common data model: RDF All of those have a − Ontology languages: RDFS and OWL standard formal − Rule interchange format: RIF semantics − Query language: SPARQL Agreement Technologies: Applications Standard formats but different ontologies... Car ⊆ Vehicle Automobile ⊆ Transportation = = To produce useful inferences with heterogeneous knowledge, ontologies must be aligned, i.e., correspondences must be found Agreement Technologies: Applications …different meanings Apple ⊆ Fruit Apple ∊ Company ≠ The same symbol does not always mean the same thing Agreement Technologies: Applications ...different modelling Clio3-RS ⊆ Car Clio3-RS ∊ RenaudCar myCar ∊ Clio3-RS = This is a class This is an instance Different granularity, different level of abstraction, different viewpoint, etc.
    [Show full text]
  • Ontology Matching Techniques: a Gold Standard Model
    Ontology Matching Techniques: A Gold Standard Model Alok Chauhan1, Vijayakumar V2 , Layth Sliman3 1,2School of Computing Science & Engineering, VIT University, Chennai; 3EFREI, PARIS [email protected], [email protected], [email protected] Abstract. Typically an ontology matching technique is a combination of much different type of matchers operating at various abstraction levels such as structure, semantic, syntax, instance etc. An ontology matching technique which employs matchers at all possible abstraction levels is expected to give, in general, best results in terms of precision, recall and F-measure due to improvement in matching opportunities and if we discount efficiency issues which may improve with better computing resources such as parallel processing. A gold standard ontology matching model is derived from a model classification of ontology matching techniques. A suitable metric is also defined based on gold standard ontology matching model. A review of various ontology matching techniques specified in recent research papers in the area was undertaken to categorize an ontology matching technique as per newly proposed gold standard model and a metric value for the whole group was computed. The results of the above study support proposed gold standard ontology matching model. Keywords: Ontology matching, gold standard, metric etc. 1 Introduction Reported slowing down in speed of improvement in the field of ontology matching is the motivation behind present work [1]. It requires a fresh look into the field of ontology matching. Ontology matching can be performed at various levels, criteria, and environments leading to different kind of techniques (Fig. 1). It may be done either locally at the element level or globally at the structure level of ontologies.
    [Show full text]
  • Addressing the Limited Adoption of Semantic Web for Data Integration
    Addressing the limited adoption of Semantic Web for data integration in AEC industry: Exploration of an RDF-based approach depending on available ontologies and literals matching Dimitrios Patsias* University of Twente, Faculty of Engineering Technology, P.O. Box 217, 7500 AE Enschede, The Netherlands *Corresponding author: [email protected] A B S T R A C T The limitations of Building Information Modeling (BIM) regarding the complete and integrated management support for building data, which are vast, diverse and distributed across different sources, systems and actors and which often need to be combined for the purpose of several analyses, stimulated the use of Semantic Web. The latter enables the uniform representation of heterogeneous data in structured graphs, using the Resource Description Framework (RDF) and common languages that describe relationships among these, namely ontologies. By deploying Semantic Web technologies, several research streams have focused on the integration of BIM with cross-domain data such as GIS, product and material data, sensor and cost data among others, by suggesting the development of several ontologies that represent concepts and relationships from different domains and the establishment of semantic links between them. At the same time, people without prior relevant awareness, perceive the concept of ontologies and tasks related to them as something too complicated. This is considered the main reason for the slow adoption of Semantic Web technologies, which is obvious within the AEC-FM industry as well. In this paper, the feasibility of a data integration approach that uses available ontologies and avoids ontology alignment is explored. For that reason, an approach based on the RDF representation of diverse datasets, the semantic description of these using available ontologies and the integration of these by matching literals between datasets, instead of establishing semantic links, is presented.
    [Show full text]