The Alignment API 4.0

Jérôme, D.; Euzenat, Jérôme; Scharffe, François; Santos, Cássia Trojahn dos

doi:10.3233/sw-2011-0028

Cited by 143 publications

(87 citation statements)

References 13 publications

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“…For decidability reasons, the expressiveness of the correspondences for ontology merging has to be SROIQ [11]. For ontology merging, the Alignment API [5] can deal with complex alignments by translating an alignment into OWL axioms.…”

Section: Tasks Involving Complex Ontology Alignmentsmentioning

confidence: 99%

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Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

et al. 2018

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Simple ontology alignments, largely studied, link one entity of a source ontology to one entity of a target ontology. One of the limitations of these alignments is, however, their lack of expressiveness which can be overcome by complex alignments. Although different complex matching approaches have emerged in the literature, there is a lack of complex reference alignments on which these approaches can be systematically evaluated. This paper proposes two sets of complex alignments between 10 pairs of ontologies from the well-known OAEI conference simple alignment dataset. The methodology for creating the alignment sets is described and takes into account the use of the alignments for two tasks: ontology merging and query rewriting. The ontology merging alignment set contains 313 correspondences and the query rewriting one 431. We report an evaluation of state-of-the art complex matchers on the proposed alignment sets.been carried out over the last fifteen years in the context of the Ontology Alignment Evaluation Campaigns (OAEI) 1 . Even though this well-known campaign proposes a task-oriented benchmark (the OA4QA track [28]), it does not propose a complex alignment benchmark.This paper proposes two alignment sets to extend the OAEI conference track dataset [3,36] with complex alignments for two task purposes: ontology merging and query rewriting. The methodology for creating the alignment sets is described and takes into account the use of the alignments for the two targeted tasks. Here we extend the work presented in [33] and in [31] by enriching the alignment sets with new pairs of ontologies and by considering the task for which the alignment is needed. We also extend the work in [31] and by adding an evaluation of three systems [23,24,13]. We extend the evaluation of the work in [33] by adding a new system described in [13] and by evaluating all the three systems on the ten pairs of ontologies for each alignment set.The paper is organised as follows. After giving the background on ontology matching ( §2) and discussing related work ( §3), we describe the methodology to create the alignments ( §4), the alignments themselves and their use for the evaluation of approaches ( §5). We conclude with a discussion on the proposal.

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Section: Tasks Involving Complex Ontology Alignmentsmentioning

confidence: 99%

“…4986368.v7 under CC-BY license. They are expressed in first-order logic (FOL), EDOAL [5], and OWL for the ontology merging alignment set. EDOAL can express transformations as well as logical relations between entities.…”

Section: Complex Alignment Setmentioning

confidence: 99%

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

et al. 2018

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“…Recent semantic matching research has produced a taxonomy of complex correspondences, called Correspondence Patterns by Scharffe [5]. As of version 4.0, the INRIA Alignment API [6] has introduced the Expressive Declarative Ontology Alignment Language (EDOAL) for expressing complex relationships of this nature, but support among semantic matchers for discovering matches that make full use of EDOAL is still limited.…”

Section: Motivationmentioning

confidence: 99%

“…The INRIA Ontology Alignment API [6] provides a comprehensive set of automated match generators, drawn from the methods described by Shvaiko and Euzenat [8]. It also provides a format for describing matches, so that they may be exchanged.…”

Section: State Of the Artmentioning

confidence: 99%

“…Schemes exist for classifying more detailed forms of relationships, these include Correspondence Patterns [5], and the THALIA framework [10]. The EDOAL [6] language provides a method for describing more complicated correspondences, using an OWL-like syntax, and was developed in conjunction with correspondence patterns. As yet, there are few matchers capable of generating the complex matches that EDOAL allows [6].…”

Section: State Of the Artmentioning

confidence: 99%

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Identifying Complex Semantic Matches

Walshe

2012

Lecture Notes in Computer Science

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One-to-one correspondences are not always sufficient to accurately align ontologies, and instead complex correspondences with conditions and transformations may be required. Correspondence patterns provide models which can be used to guide the process of developing complex correspondences. However, it is necessary to first identify which pattern to apply to a given alignment problem. This PhD proposes the development of algorithms, methods and processes for refining elementary correspondences between concepts or relations into complex ones by identifying which correspondence pattern best represents a given correspondence. To date an evaluation of a system to refine correspondences between classes in the YAGO and DBpedia ontologies has been completed. This evaluation showed that for a subsumption correspondence, a training set of 30 instances of the class being mapped was sufficient to refine the match to a conditional one 89% of the time. Hence we have shown that this is a promising approach for correspondences with a conditional element, and correspondences with a translation element will be examined next.

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A comprehensive survey on semantic interoperability for Internet of Things: State‐of‐the‐art and research challenges

Rahman

Hussain

2020

Trans Emerging Tel Tech

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Internet of Things (IoT) is highly heterogeneous in terms of devices, communication technologies, protocols, data formats, and semantics. Data generated from various sources are represented using different semantics. It makes semantic interoperability as one of the significant issues in provisioning seamless communication and services over diverse IoT platforms. Semantic models help in exchanging semantically annotated information among such heterogeneous applications in a meaningful way. Seamless communication among different types of applications is commonly accomplished by using middleware, ontology, and semantic web technologies. In this paper, we present a comprehensive survey of different semantic interoperability solutions in the context of IoT. The pivotal semantic models available in the literature are compared with the help of a classification framework. This study identifies various open research issues and challenges for facilitating interoperable IoT communications. For the purpose of implementation and performance evaluation of various semantic models, we also suggest some important tools and frameworks. After pointing out different prospects and problems of the existing semantic interoperability schemes, the factors for improvement are identified indicating future research directions.

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The Alignment API 4.0

Cited by 143 publications

References 13 publications

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

Identifying Complex Semantic Matches

A comprehensive survey on semantic interoperability for Internet of Things: State‐of‐the‐art and research challenges

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