Survey on complex ontology matching

Thiéblin, Elodie; Haemmerlé, Ollivier; Hernandez, Nathalie; Santos, Cássia Trojahn dos

doi:10.3233/sw-190366

Cited by 45 publications

(29 citation statements)

References 82 publications

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“…However, since the entities' semantic relationships in the lightweight ontologies could be more complicated and richer, lightweight sensor ontology alignment is more complex, that is, one source ontology entity is mapping with more than one target ontology entity, and the relationships could be equivalence or subsumption. To address this complex matching problem [51], a feasible method is to introduce various mapping patterns [52] into CcFA to detect the complex correspondences, which is one of our future work.…”

Section: The Results Of Statistical Comparisonmentioning

confidence: 99%

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Xue

Chen

2020

Sensors

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Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm’s exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal’s performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences.

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Section: The Results Of Statistical Comparisonmentioning

confidence: 99%

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Xue

Chen

2020

Sensors

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“…A semantic correspondence between ontology entities can be simple or complex [36]. In the former case, the correspondence expresses a relation between two simple entities, such as named classes, properties, or individuals; for instance, it may state that a named class in an ontology is equivalent to a named class in another ontology.…”

Section: Related Workmentioning

confidence: 99%

Wikidata Support in the Creation of Rich Semantic Metadata for Historical Archives

et al. 2021

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The research question this paper aims at answering is the following: In an ontology-driven annotation system, can the information extracted from external resources (namely, Wikidata) provide users with useful suggestions in the characterization of entities used for the annotation of documents from historical archives? The context of the research is the PRiSMHA project, in which the main goal is the development of a proof-of-concept prototype ontology-driven system for semantic metadata generation. The assumption behind this effort is that an effective access to historical archives needs a rich semantic knowledge, relying on a domain ontology, that describes the content of archival resources. In the paper, we present a new feature of the annotation system: when characterizing a new entity (e.g., a person), some properties describing it are automatically pre-filled in, and more complex semantic representations (e.g., events the entity is involved in) are suggested; both kinds of suggestions are based on information retrieved from Wikidata. In the paper, we describe the automatic algorithm devised to support the definition of the mappings between the Wikidata semantic model and the PRiSMHA ontology, as well as the process used to extract information from Wikidata and to generate suggestions based on the defined mappings. Finally, we discuss the results of a qualitative evaluation of the suggestions, which provides a positive answer to the initial research question and indicates possible improvements.

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“…As described by Thiéblin et al . (2018b), the metrics of accuracy and top-x accuracy have been also applied in evaluation settings in which the number of correspondences is predefined, for example, there is one correspondence for each entity of the target schema/ontology. The accuracy is then the percentage of predefined questions having a correct answer.…”

Section: Related Workmentioning

confidence: 99%

“…The queries written for the source ontology were rewritten automatically when dealing with (1:1) or (1: n ) correspondences, using the system described by Thiéblin et al . (2016) and manually when dealing with ( m : n ) correspondences.…”

Section: Evaluation Without a Reference Alignmentmentioning

confidence: 99%

Towards evaluating complex ontology alignments

Zhou

Thiéblin

Cheatham

et al. 2020

The Knowledge Engineering Review

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The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.

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Survey on complex ontology matching

Cited by 45 publications

References 82 publications

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Wikidata Support in the Creation of Rich Semantic Metadata for Historical Archives

Towards evaluating complex ontology alignments

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