Data interlinking/Liage de données in areas (2024-03-15)
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Oktie Hassanzadeh, Cássia Trojahn dos Santos (eds), Proc. 18th ISWC workshop on ontology matching (OM), Athens (GR), 202p., 2023
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Oktie Hassanzadeh, Cássia Trojahn dos Santos (eds), Proc. 17th ISWC workshop on ontology matching (OM), (online), 230p., 2022
Manuel Atencia, Jérôme David, Jérôme Euzenat, On the relation between keys and link keys for data interlinking, Semantic web journal 12(4):547-567, 2021
Both keys and their generalisation, link keys, may be used to perform data interlinking, i.e. finding identical resources in different RDF datasets. However, the precise relationship between keys and link keys has not been fully determined yet. A common formal framework encompassing both keys and link keys is necessary to ensure the correctness of data interlinking tools based on them, and to determine their scope and possible overlapping. In this paper, we provide a semantics for keys and link keys within description logics. We determine under which conditions they are legitimate to generate links. We provide conditions under which link keys are logically equivalent to keys. In particular, we show that data interlinking with keys and ontology alignments can be reduced to data interlinking with link keys, but not the other way around.
Ontology alignment, Key, Link key, Data interlinking
Manuel Atencia, Jérôme David, Jérôme Euzenat, Amedeo Napoli, Jérémy Vizzini, Relational concept analysis for circular link key extraction, Deliverable 1.2, ELKER, 57p., December 2021
A link key extraction procedure in case of circular dependencies is presented. It uses relational concept analysis and extends the procedure of Deliverable 1.1. This leads to investigate more closely the semantics of relational concept analysis which is given in terms of fixed points. Extracting all fixed points may offer more link key candidates to consider.
Formal Concept Analysis, Relational Concept Analysis, linked data, link key, data interlinking, Resource Description Framework
Jérôme Euzenat, Fixed-point semantics for barebone relational concept analysis, in: Proc. 16th international conference on formal concept analysis (ICFCA), Strasbourg (FR), (Agnès Braud, Aleksey Buzmakov, Tom Hanika, Florence Le Ber (eds), Proc. 16th international conference on formal concept analysis (ICFCA), Lecture notes in computer science 12733, 2021), pp20-37, 2021
Relational concept analysis (RCA) extends formal concept analysis (FCA) by taking into account binary relations between formal contexts. It has been designed for inducing description logic TBoxes from ABoxes, but can be used more generally. It is especially useful when there exist circular dependencies between objects. In this case, it extracts a unique stable concept lattice family grounded on the initial formal contexts. However, other stable families may exist whose structure depends on the same relational context. These may be useful in applications that need to extract a richer structure than the minimal grounded one. This issue is first illustrated in a reduced version of RCA, which only retains the relational structure. We then redefine the semantics of RCA on this reduced version in terms of concept lattice families closed by a fixed-point operation induced by this relational structure. We show that these families admit a least and greatest fixed point and that the well-grounded RCA semantics is characterised by the least fixed point. We then study the structure of other fixed points and characterise the interesting lattices as the self-supported fixed points.
Formal Concept Analysis
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Oktie Hassanzadeh, Cássia Trojahn dos Santos (eds), Proc. 16th ISWC workshop on ontology matching (OM), (online), 218p., 2021
Manuel Atencia, Jérôme David, Jérôme Euzenat, Liliana Ibanescu, Nathalie Pernelle, Fatiha Saïs, Élodie Thiéblin, Cássia Trojahn dos Santos, Discovering expressive rules for complex ontology matching and data interlinking, in: Pavel Shvaiko, Jérôme Euzenat, Oktie Hassanzadeh, Ernesto Jiménez-Ruiz, Cássia Trojahn dos Santos (eds), Proc. 14th ISWC workshop on ontology matching (OM), Auckland (NZ), pp199-200, 2020
Ontology matching and data interlinking as distinguished tasks aim at facilitating the interoperability between different knowledge bases. Although the field has fully developed in the last years, most works still focus on generating simple correspondences between entities. These correspondences are however insufficient to fully cover the different types of heterogeneity between the knowledge base and complex correspondences are therefore required. Compared to simple matching, few approaches for complex matching have been proposed, focusing on correspondence patterns or exploiting common instances between the ontologies. Similarly, unsupervised data interlinking approaches (which do not require labelled data samples) have recently been developed. One approach consists in discovering linking rules such as simple keys or conditional keys on unlabelled data. The results have shown that the more expressive the rules, the higher the recall. Even more expressive rules (referential expressions, graph keys, etc.) are rather required, however naive approaches to the discovery of these rules can not be envisaged on large data sets. Existing approaches presuppose either that the data conform to the same ontology or that all possible pairs of properties be examined. Complementary, link keys are a set of pairs of properties that identify the instances of two classes of two RDF datasets. Such, link keys may be directly extracted without the need for an alignment. We introduce here an approach that aims at evaluating the impact of complex correspondences in the task of data interlinking established from the application of keys.
Data interlinking, Ontology matching, Complex correspondence
Manuel Atencia, Jérôme David, Jérôme Euzenat, Amedeo Napoli, Jérémy Vizzini, Link key candidate extraction with relational concept analysis, Discrete applied mathematics 273:2-20, 2020
Linked data aims at publishing data expressed in RDF (Resource Description Framework) at the scale of the worldwide web. These datasets interoperate by publishing links which identify individuals across heterogeneous datasets. Such links may be found by using a generalisation of keys in databases, called link keys, which apply across datasets. They specify the pairs of properties to compare for linking individuals belonging to different classes of the datasets. Here, we show how to recast the proposed link key extraction techniques for RDF datasets in the framework of formal concept analysis. We define a formal context, where objects are pairs of resources and attributes are pairs of properties, and show that formal concepts correspond to link key candidates. We extend this characterisation to the full RDF model including non functional properties and interdependent link keys. We show how to use relational concept analysis for dealing with cyclic dependencies across classes and hence link keys. Finally, we discuss an implementation of this framework.
Formal Concept Analysis, Relational Concept Analysis, Linked data, Link key, Data interlinking, Resource Description Framework
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Oktie Hassanzadeh, Cássia Trojahn dos Santos (eds), Proc. 14th ISWC workshop on ontology matching (OM), Auckland (NZ), 210p., 2020
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Oktie Hassanzadeh, Cássia Trojahn dos Santos (eds), Proc. 15th ISWC workshop on ontology matching (OM), Athens (GR), 253p., 2020
Manuel Atencia, Jérôme David, Jérôme Euzenat, Amedeo Napoli, Jérémy Vizzini, A guided walk into link key candidate extraction with relational concept analysis, in: Claudia d'Amato, Lalana Kagal (eds), Proc. on journal track of the International semantic web conference, Auckland (NZ), 2019
Data interlinking is an important task for linked data interoperability. One of the possible techniques for finding links is the use of link keys which generalise relational keys to pairs of RDF models. We show how link key candidates may be directly extracted from RDF data sets by encoding the extraction problem in relational concept analysis. This method deals with non functional properties and circular dependent link key expressions. As such, it generalises those presented for non dependent link keys and link keys over the relational model. The proposed method is able to return link key candidates involving several classes at once.
Formal Concept Analysis, Relational Concept Analysis, Linked data, Link key, Data interlinking, Resource Description Framework
Manuel Atencia, Jérôme David, Jérôme Euzenat, Several link keys are better than one, or extracting disjunctions of link key candidates, in: Proc. 10th ACM international conference on knowledge capture (K-Cap), Marina del Rey (CA US), pp61-68, 2019
Link keys express conditions under which instances of two classes of different RDF data sets may be considered as equal. As such, they can be used for data interlinking. There exist algorithms to extract link key candidates from RDF data sets and different measures have been defined to evaluate the quality of link key candidates individually. For certain data sets, however, it may be necessary to use more than one link key on a pair of classes to retrieve a more complete set of links. To this end, in this paper, we define disjunction of link keys, propose strategies to extract disjunctions of link key candidates from RDF data, and apply existing quality measures to evaluate them. We also report on experiments with these strategies.
Linked data, RDF, Data interlinking, Link key, Antichain
Manuel Atencia, Jérôme Euzenat, Khadija Jradeh, Chan Le Duc, Tableau methods for reasoning with link keys, Deliverable 2.1, ELKER, 32p., 2019
Data interlinking is a critical task for widening and enhancing linked open data. One way to tackle data interlinking is to use link keys, which generalise keys to the case of two RDF datasets described using different ontologies. Link keys specify pairs of properties to compare for finding same-as links between instances of two classes of two different datasets. Hence, they can be used for finding links. Link keys can also be considered as logical axioms just like keys, ontologies and ontology alignments. We introduce the logic ALC+LK extending the description logic ALC with link keys. It may be used to reason and infer entailed link keys that may be more useful for a particular data interlinking task. We show that link key entailment can be reduced to consistency checking without introducing the negation of link keys. For deciding the consistency of an ALC+LK ontology, we introduce a new tableau-based algorithm. Contrary to the classical ones, the completion rules concerning link keys apply to pairs of individuals not directly related. We show that this algorithm is sound, complete and always terminates.
link keys, reasoning, tableau method
Manuel Atencia, Jérôme David, Jérôme Euzenat, Amedeo Napoli, Jérémy Vizzini, Candidate link key extraction with formal concept analysis, Deliverable 1.1, ELKER, 29p., October 2019
A link key extraction procedure using formal concept analysis is described. It is shown to extract all link key candidates.
Formal Concept Analysis, linked data, link key, data interlinking, Resource Description Framework
Jérôme David, Jérôme Euzenat, Jérémy Vizzini, Linkky: Extraction de clés de liage par une adaptation de l'analyse relationnelle de concepts, in: Actes 29e journées francophones sur Ingénierie des connaissances (IC), Nancy (FR), pp271-274, 2018
RDF, Clé de liage, Liage de données, Analyse relationelle de concepts, Analyse formelle de concepts, Network of ontologies
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh (eds), Proc. 13th ISWC workshop on ontology matching (OM), Monterey (CA US), 227p., 2018
Manel Achichi, Michelle Cheatham, Zlatan Dragisic, Jérôme Euzenat, Daniel Faria, Alfio Ferrara, Giorgos Flouris, Irini Fundulaki, Ian Harrow, Valentina Ivanova, Ernesto Jiménez-Ruiz, Kristian Kolthoff, Elena Kuss, Patrick Lambrix, Henrik Leopold, Huanyu Li, Christian Meilicke, Majid Mohammadi, Stefano Montanelli, Catia Pesquita, Tzanina Saveta, Pavel Shvaiko, Andrea Splendiani, Heiner Stuckenschmidt, Élodie Thiéblin, Konstantin Todorov, Cássia Trojahn dos Santos, Ondřej Zamazal, Results of the Ontology Alignment Evaluation Initiative 2017, in: Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh (eds), Proc. 12th ISWC workshop on ontology matching (OM), Wien (AT), pp61-113, 2017
Ontology matching consists of finding correspondences between semantically related entities of different ontologies. The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2017 campaign offered 9 tracks with 23 test cases, and was attended by 21 participants. This paper is an overall presentation of that campaign.
Michelle Cheatham, Isabel Cruz, Jérôme Euzenat, Catia Pesquita (eds), Special issue on ontology and linked data matching, Semantic web journal (special issue) 8(2):183-251, 2017
Michelle Cheatham, Isabel Cruz, Jérôme Euzenat, Catia Pesquita, Special issue on ontology and linked data matching, Semantic web journal 8(2):183-184, 2017
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh (eds), Proc. 12th ISWC workshop on ontology matching (OM), Wien (AT), 225p., 2017
Manel Achichi, Michelle Cheatham, Zlatan Dragisic, Jérôme Euzenat, Daniel Faria, Alfio Ferrara, Giorgos Flouris, Irini Fundulaki, Ian Harrow, Valentina Ivanova, Ernesto Jiménez-Ruiz, Elena Kuss, Patrick Lambrix, Henrik Leopold, Huanyu Li, Christian Meilicke, Stefano Montanelli, Catia Pesquita, Tzanina Saveta, Pavel Shvaiko, Andrea Splendiani, Heiner Stuckenschmidt, Konstantin Todorov, Cássia Trojahn dos Santos, Ondřej Zamazal, Results of the Ontology Alignment Evaluation Initiative 2016, in: Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh, Ryutaro Ichise (eds), Proc. 11th ISWC workshop on ontology matching (OM), Kobe (JP), pp73-129, 2016
Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation, or consensus. OAEI 2016 offered 9 tracks with 22 test cases, and was attended by 21 participants. This paper is an overall presentation of the OAEI 2016 campaign.
Michelle Cheatham, Zlatan Dragisic, Jérôme Euzenat, Daniel Faria, Alfio Ferrara, Giorgos Flouris, Irini Fundulaki, Roger Granada, Valentina Ivanova, Ernesto Jiménez-Ruiz, Patrick Lambrix, Stefano Montanelli, Catia Pesquita, Tzanina Saveta, Pavel Shvaiko, Alessandro Solimando, Cássia Trojahn dos Santos, Ondřej Zamazal, Results of the Ontology Alignment Evaluation Initiative 2015, in: Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh (eds), Proc. 10th ISWC workshop on ontology matching (OM), Bethlehem (PA US), pp60-115, 2016
Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation and consensus. OAEI 2015 offered 8 tracks with 15 test cases followed by 22 participants. Since 2011, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2015 campaign.
Jérôme Euzenat, Extraction de clés de liage de données (résumé étendu), in: Actes 16e conférence internationale francophone sur extraction et gestion des connaissances (EGC), Reims (FR), (Bruno Crémilleux, Cyril de Runz (éds), Actes 16e conférence internationale francophone sur extraction et gestion des connaissances (EGC), Revue des nouvelles technologies de l'information E30, 2016), pp9-12, 2016
De grandes quantités de données sont publiées sur le web des données. Les lier consiste à identifier les mêmes ressources dans deux jeux de données permettant l'exploitation conjointe des données publiées. Mais l'extraction de liens n'est pas une tâche facile. Nous avons développé une approche qui extrait des clés de liage (link keys). Les clés de liage étendent la notion de clé de l'algèbre relationnelle à plusieurs sources de données. Elles sont fondées sur des ensembles de couples de propriétés identifiant les objets lorsqu'ils ont les mêmes valeurs, ou des valeurs communes, pour ces propriétés. On présentera une manière d'extraire automatiquement les clés de liage candidates à partir de données. Cette opération peut être exprimée dans l'analyse formelle de concepts. La qualité des clés candidates peut-être évaluée en fonction de la disponibilité (cas supervisé) ou non (cas non supervisé) d'un échantillon de liens. La pertinence et de la robustesse de telles clés seront illustrées sur un exemple réel.
Maroua Gmati, Manuel Atencia, Jérôme Euzenat, Tableau extensions for reasoning with link keys, in: Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh, Ryutaro Ichise (eds), Proc. 11th ISWC workshop on ontology matching (OM), Kobe (JP), pp37-48, 2016
Link keys allow for generating links across data sets expressed in different ontologies. But they can also be thought of as axioms in a description logic. As such, they can contribute to infer ABox axioms, such as links, or terminological axioms and other link keys. Yet, no reasoning support exists for link keys. Here we extend the tableau method designed for ALC to take link keys into account. We show how this extension enables combining link keys with terminological reasoning with and without ABox and TBox and generate non trivial link keys.
Link key, Tableau method, Description logics, Semantic web
Tatiana Lesnikova, Jérôme David, Jérôme Euzenat, Cross-lingual RDF thesauri interlinking, in: Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis (eds), Proc. 10th international conference on Language resources and evaluation (LREC), Portoroz (SI), pp2442-2449, 2016
Various lexical resources are being published in RDF. To enhance the usability of these resources, identical resources in different data sets should be linked. If lexical resources are described in different natural languages, then techniques to deal with multilinguality are required for interlinking. In this paper, we evaluate machine translation for interlinking concepts, i.e., generic entities named with a common noun or term. In our previous work, the evaluated method has been applied on named entities. We conduct two experiments involving different thesauri in different languages. The first experiment involves concepts from the TheSoz multilingual thesaurus in three languages: English, French and German. The second experiment involves concepts from the EuroVoc and AGROVOC thesauri in English and Chinese respectively. Our results demonstrate that machine translation can be beneficial for cross-lingual thesauri interlining independently of a dataset structure.
Cross-lingual data interlinking, owl:sameAs, Thesaurus alignment
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh (eds), Proc. 10th ISWC workshop on ontology matching (OM), Bethlehem (PA US), 239p., 2016
Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh, Ryutaro Ichise (eds), Proc. 11th ISWC workshop on ontology matching (OM), Kobe (JP), 252p., 2016
Tatiana Lesnikova, Jérôme David, Jérôme Euzenat, Interlinking English and Chinese RDF data using BabelNet, in: Pierre Genevès, Christine Vanoirbeek (eds), Proc. 15th ACM international symposium on Document engineering (DocEng), Lausanne (CH), pp39-42, 2015
Linked data technologies make it possible to publish and link structured data on the Web. Although RDF is not about text, many RDF data providers publish their data in their own language. Cross-lingual interlinking aims at discovering links between identical resources across knowledge bases in different languages. In this paper, we present a method for interlinking RDF resources described in English and Chinese using the BabelNet multilingual lexicon. Resources are represented as vectors of identifiers and then similarity between these resources is computed. The method achieves an F-measure of 88%. The results are also compared to a translation-based method.
Cross-lingual instance linking, Cross-lingual link discovery, owl:sameAs
Manuel Atencia, Jérôme David, Jérôme Euzenat, Data interlinking through robust linkkey extraction, in: Torsten Schaub, Gerhard Friedrich, Barry O'Sullivan (eds), Proc. 21st european conference on artificial intelligence (ECAI), Praha (CZ), pp15-20, 2014
Links are important for the publication of RDF data on the web. Yet, establishing links between data sets is not an easy task. We develop an approach for that purpose which extracts weak linkkeys. Linkkeys extend the notion of a key to the case of different data sets. They are made of a set of pairs of properties belonging to two different classes. A weak linkkey holds between two classes if any resources having common values for all of these properties are the same resources. An algorithm is proposed to generate a small set of candidate linkkeys. Depending on whether some of the, valid or invalid, links are known, we define supervised and non supervised measures for selecting the appropriate linkkeys. The supervised measures approximate precision and recall, while the non supervised measures are the ratio of pairs of entities a linkkey covers (coverage), and the ratio of entities from the same data set it identifies (discrimination). We have experimented these techniques on two data sets, showing the accuracy and robustness of both approaches.
Manuel Atencia, Jérôme David, Jérôme Euzenat, What can FCA do for database linkkey extraction?, in: Proc. 3rd ECAI workshop on What can FCA do for Artificial Intelligence? (FCA4AI), Praha (CZ), pp85-92, 2014
Links between heterogeneous data sets may be found by using a generalisation of keys in databases, called linkkeys, which apply across data sets. This paper considers the question of characterising such keys in terms of formal concept analysis. This question is natural because the space of candidate keys is an ordered structure obtained by reduction of the space of keys and that of data set partitions. Classical techniques for generating functional dependencies in formal concept analysis indeed apply for finding candidate keys. They can be adapted in order to find database candidate linkkeys. The question of their extensibility to the RDF context would be worth investigating.
Zhengjie Fan, Jérôme Euzenat, François Scharffe, Learning concise pattern for interlinking with extended version space, in: Dominik l zak, Hung Son Nguyen, Marek Reformat, Eugene Santos (eds), Proc. 13th IEEE/WIC/ACM international conference on web intelligence (WI), Warsaw (PL), pp70-77, 2014
Many data sets on the web contain analogous data which represent the same resources in the world, so it is helpful to interlink different data sets for sharing information. However, finding correct links is very challenging because there are many instances to compare. In this paper, an interlinking method is proposed to interlink instances across different data sets. The input is class correspondences, property correspondences and a set of sample links that are assessed by users as either "positive" or "negative". We apply a machine learning method, Version Space, in order to construct a classifier, which is called interlinking pattern, that can justify correct links and incorrect links for both data sets. We improve the learning method so that it resolves the no-conjunctive-pattern problem. We call it Extended Version Space. Experiments confirm that our method with only 1% of sample links already reaches a high F-measure (around 0.96-0.99). The F-measure quickly converges, being improved by nearly 10% than other comparable approaches.
Tatiana Lesnikova, Jérôme David, Jérôme Euzenat, Interlinking English and Chinese RDF data sets using machine translation, in: Johanna Völker, Heiko Paulheim, Jens Lehmann, Harald Sack, Vojtech Svátek (eds), Proc. 3rd ESWC workshop on Knowledge discovery and data mining meets linked open data (Know@LOD), Hersounisos (GR), 2014
Data interlinking is a difficult task particularly in a multilingual environment like the Web. In this paper, we evaluate the suitability of a Machine Translation approach to interlink RDF resources described in English and Chinese languages. We represent resources as text documents, and a similarity between documents is taken for similarity between resources. Documents are represented as vectors using two weighting schemes, then cosine similarity is computed. The experiment demonstrates that TF*IDF with a minimum amount of preprocessing steps can bring high results.
Semantic web, Cross-lingual link discovery, Cross-lingual instance linking, owl:sameAs
Zhengjie Fan, Thin Dong Ngoc Nguyen, Jérôme Euzenat, Fayçal Hamdi, François Scharffe, Dataset interlinking module, Deliverable 4.2, Datalift, 34p., 2013
This report presents the second version of the interlinking module for the
Datalift platform as well as strategies for future developments.
data interlinking, linked data, instance matching
Jérôme Euzenat, A modest proposal for data interlinking evaluation, in: Pavel Shvaiko, Jérôme Euzenat, Anastasios Kementsietsidis, Ming Mao, Natalya Noy, Heiner Stuckenschmidt (eds), Proc. 7th ISWC workshop on ontology matching (OM), Boston (MA US), pp234-235, 2012
Data interlinking is a very important topic nowadays. It is sufficiently similar to ontology matching that comparable evaluation can be overtaken. However, it has enough differences, so that specific evaluations may be designed. We discuss such variations and design.
Data interlinking, Evaluation, Benchmark, Blocking, Instance matching
François Scharffe, Ghislain Atemezing, Raphaël Troncy, Fabien Gandon, Serena Villata, Bénédicte Bucher, Fayçal Hamdi, Laurent Bihanic, Gabriel Képéklian, Franck Cotton, Jérôme Euzenat, Zhengjie Fan, Pierre-Yves Vandenbussche, Bernard Vatant, Enabling linked data publication with the Datalift platform, in: Proc. AAAI workshop on semantic cities, Toronto (ONT CA), 2012
As many cities around the world provide access to raw public data along the Open Data movement, many questions arise concerning the accessibility of these data. Various data formats, duplicate identifiers, heterogeneous metadata schema descriptions, and diverse means to access or query the data exist. These factors make it difficult for consumers to reuse and integrate data sources to develop innovative applications. The Semantic Web provides a global solution to these problems by providing languages and protocols for describing and accessing datasets. This paper presents Datalift, a framework and a platform helping to lift raw data sources to semantic interlinked data sources.
Jérôme Euzenat, Nathalie Abadie, Bénédicte Bucher, Zhengjie Fan, Houda Khrouf, Michael Luger, François Scharffe, Raphaël Troncy, Dataset interlinking module, Deliverable 4.2, Datalift, 32p., 2011
This report presents the first version of the interlinking module for the Datalift platform as well as strategies for future developments.
data interlinking, linked data, instance matching
François Scharffe, Jérôme Euzenat, MeLinDa: an interlinking framework for the web of data, Research report 7641, INRIA, Grenoble (FR), 21p., July 2011
The web of data consists of data published on the web in such a way that they can be interpreted and connected together. It is thus critical to establish links between these data, both for the web of data and for the semantic web that it contributes to feed. We consider here the various techniques developed for that purpose and analyze their commonalities and differences. We propose a general framework and show how the diverse techniques fit in the framework. From this framework we consider the relation between data interlinking and ontology matching. Although, they can be considered similar at a certain level (they both relate formal entities), they serve different purposes, but would find a mutual benefit at collaborating. We thus present a scheme under which it is possible for data linking tools to take advantage of ontology alignments.
Semantic web, Data interlinking, Instance matching, Ontology alignment, Web of data
François Scharffe, Jérôme Euzenat, Linked data meets ontology matching: enhancing data linking through ontology alignments, in: Proc. 3rd international conference on Knowledge engineering and ontology development (KEOD), Paris (FR), pp279-284, 2011
The Web of data consists of publishing data on the Web in such a way that they can be connected together and interpreted. It is thus critical to establish links between these data, both for the Web of data and for the Semantic Web that it contributes to feed. We consider here the various techniques which have been developed for that purpose and analyze their commonalities and differences. This provides a general framework that the diverse data linking systems instantiate. From this framework we consider the relation between data linking and ontology matching activities. Although, they can be considered similar at a certain level (they both relate formal entities), they serve different purposes: one acts at the schema level and the other at the instance level. However, they would find a mutual benefit at collaborating. We thus present a scheme under which it is possible for data linking tools to take advantage of ontology alignments. We present the features of expressive alignment languages that allows linking specifications to reuse ontology alignments in a natural way.
Semantic web, Linked data, Data linking, Ontology alignment, Ontology matching, Entity reonciliation, Object consolidation
François Scharffe, Jérôme Euzenat, Méthodes et outils pour lier le web des données, in: Actes 17e conférenceAFIA-AFRIF sur reconnaissance des formes et intelligence artificielle (RFIA), Caen (FR), pp678-685, 2010
Le web des données consiste à publier des données sur le web de telle sorte qu'elles puissent être interprétées et connectées entre elles. Il est donc vital d'établir les liens entre ces données à la fois pour le web des données et pour le web sémantique qu'il contribue à nourrir. Nous proposons un cadre général dans lequel s'inscrivent les différentes techniques utilisées pour établir ces liens et nous montrons comment elles s'y insèrent. Nous proposons ensuite une architecture permettant d'associer les différents systèmes de liage de données et de les faire collaborer avec les systèmes développés pour la mise en correspondance d'ontologies qui présente de nombreux points communs avec la découverte de liens.
Semantic web, Data interlinking, Instance matching, Ontology alignment, Web of data