Aligning Ontologies of Geospatial Linked Data
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Mapping Spatiotemporal Data to RDF: a SPARQL Endpoint for Brussels
International Journal of Geo-Information Article Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels Alejandro Vaisman 1, * and Kevin Chentout 2 1 Instituto Tecnológico de Buenos Aires, Buenos Aires 1424, Argentina 2 Sopra Banking Software, Avenue de Tevuren 226, B-1150 Brussels, Belgium * Correspondence: [email protected]; Tel.: +54-11-3457-4864 Received: 20 June 2019; Accepted: 7 August 2019; Published: 10 August 2019 Abstract: This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis. -
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. -
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. -
Discovering Alignments in Ontologies of Linked Data∗
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Discovering Alignments in Ontologies of Linked Data∗ Rahul Parundekar, Craig A. Knoblock, and Jose´ Luis Ambite University of Southern California Information Sciences Institute 4676 Admiralty Way Marina del Rey, CA 90292 Abstract The problem of schema linking (aka schema matching in databases and ontology alignment in the Semantic Web) has Recently, large amounts of data are being published received much attention [Bellahsene et al., 2011; Euzenat and using Semantic Web standards. Simultaneously, Shvaiko, 2007; Bernstein et al., 2011; Gal, 2011]. In this there has been a steady rise in links between objects paper we present a novel extensional approach to generate from multiple sources. However, the ontologies alignments between ontologies of linked data sources. Sim- behind these sources have remained largely dis- ilar to previous work on instance-based matching [Duckham connected, thereby challenging the interoperabil- and Worboys, 2005; Doan et al., 2004; Isaac et al., 2007], ity goal of the Semantic Web. We address this we rely on linked instances to determine the alignments. Two problem by automatically finding alignments be- concepts are equivalent if all (or most of) their respective in- tween concepts from multiple linked data sources. stances are linked (by owl:sameAs or similar links). How- Instead of only considering the existing concepts ever, our search is not limited to the existing concepts in in each ontology, we hypothesize new composite the ontology. We hypothesize new concepts by combining concepts, defined using conjunctions and disjunc- existing elements in the ontologies and seek alignments be- tions of (RDF) types and value restrictions, and tween these more general concepts. -
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]. -
Linked Data Enables Such Web of Data Global Identifier
Lift your data Introduction to Linked Data Ghislain Atemezing1 , Boris Villazón-Terrazas2 1 EURECOM, France [email protected] 2 Ontology Engineering Group , FI , UPM [email protected] Slides available at: http://www.slideshare.net/atemezing/ INSPIRE Conference 2012, Istanbul Acknowledgements: Oscar Corcho, Asunción Gómez-Pérez, Luis Vilches, Raphaël Troncy, Olaf Hartig, Luis Vilches and many others that we may have omitted. Workdistributed under the license Creative Commons Attribution- Noncommercial-Share Alike 3.0 Agenda Linked Data Geospatial LD Datasets 5-star deployment scheme for Linked Open Data Tutorial “Lift your data”, Istanbul 2012 2 Agenda Linked Data Geospatial LD Datasets 5-star deployment scheme for Linked Open Data Tutorial “Lift your data”, Istanbul 2012 3 Current Web Geospatial Database (Spain) Data exposed to the Web via HTML, pdf, etc. Statistical Database (Spain) © Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig Tutorial “Lift your data”, Istanbul 2012 4 Information from ssgeingle pages can be found via sear ch eegngin es © Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig Tutorial “Lift your data”, Istanbul 2012 5 Complex queries over muliltipl e pages/data sources? © Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig Tutorial “Lift your data”, Istanbul 2012 6 What do we actually want? • Use the Web like a single global database • web of documents -> web of data Geospatial Statistical Database Database (Spain) (Spain) © Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig 7 Tutorial “Lift your data”, Istanbul 2012 7 Linked Data enables such Web of Data Global Identifier: URI (Uniform Resource Identifier), which is a string of characters used to identify a name or a resource on the Internet. -
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. -
Historical Gazetteer System Integration: CHGIS, Regnum Francorum, and Geonames
p. 1 Historical Gazetteer System Integration: CHGIS, Regnum Francorum, and GeoNames Merrick Lex Berman (CGA, Harvard University) Johan Åhlfeldt (Regnum Francorum Online) Marc Wick 24 Jul 2014 [draft chapter for submission to publisher] Abstract: Integrating digital gazetteers involves the matching of place name records, disambiguation of unique places and conflation of duplicates or variant place names. The challenge of mapping between historical instances of place names is also an ongoing concern for several important projects dealing with ancient place names. Here the matching of historical place names from two unrelated datasets (for China and France) to gazetteer web services is undertaken using a basic geospatial and geonomial algorithm. The quantitative results of the matching trials are considered, problems in dealing with vernacular scripts considered, and practical implications for integrating historical gazetteers discussed. 1. Gazetteer Web Services and Digital Historical Gazetteers Online gazetteers provide essential services. They serve as authority records for geocoding place names and for retrieval of place names associated with real world coordinates (reverse geocoding). For example, the expanding interconnections of LinkedOpenData [LOD] 1 on the semantic web have consistently placed GeoNames 2 at or near the center of the semantic web's social graph. The centrality of GeoNames is largely due to three factors: first, it is a free and open Application Programming Interface [API]; second, the API is simple and easy-to-use; and third, it is currently the only global geographic resource with stable URIs. Traffic for the GeoNames web service topped twenty million requests per day (in 2012); and since half of these requests are from smart phones, 3 it is clear that geographic information retrieval [GIR] is being p. -
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, -
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. -
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. -
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_________________________________