Matcher composition for identification of subsumption relations in ontology matching

Vennesland, Audun

doi:10.1145/3106426.3106503

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…We performed a baseline evaluation of the chosen ontology matching strategy by selecting a subset of two (cmt and conference datasets) of the manually curated reference alignments of the OAEI Conference track [48]. However, since this reference alignment evaluates only equivalent mappings, we added the COMPOSE reference [46], where the author extended some of the conference reference mappings to also include subsumption correspondences.…”

Section: Creating the Ontology Graphmentioning

confidence: 99%

Extracting data models from background knowledge graphs

Oliveira

d’Aquin

2022

Knowledge-Based Systems

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Knowledge Graphs have emerged as a core technology to aggregate and publish knowledge on the Web. However, integrating knowledge from different sources, not specifically designed to be interoperable, is not a trivial task. Finding the right ontologies to model a dataset is a challenge since several valid data models exist and there is no clear agreement between them. In this paper, we propose to facilitate the selection of a data model with the RICDaM (Recommending Interoperable and Consistent Data Models) framework. RICDaM generates and ranks candidates that match entity types and properties in an input dataset. These candidates are obtained by aggregating freely available domain RDF datasets in a knowledge graph and then enriching the relationships between the graph's entities. The entity type and object property candidates are obtained by exploiting the instances and structure of this knowledge graph to compute a score that considers both the accuracy and interoperability of the candidates. Datatype properties are predicted with a random forest model, trained on the knowledge graph properties and their values, so to make predictions on candidate properties and rank them according to different measures. We present experiments using multiple datasets from the library domain as a use case and show that our methodology can produce meaningful candidate data models, adaptable to specific scenarios and needs.

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Section: Creating the Ontology Graphmentioning

confidence: 99%

Extracting data models from background knowledge graphs

Oliveira

d’Aquin

2022

Knowledge-Based Systems

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“…To completely explore semantics implicated in ontology (see [37], [38] for definitions of formalized semantics) and directly manipulate the reasoning process, proper reasoning algorithms should be selected to analyze semantic information during the ontology matching process.…”

Section: Future Workmentioning

confidence: 99%

“…However, these schemes fail to define formalized semantics of the information analysis results, as well as the relation between the final correspondence and ontology semantics. Therefore, it is not properly interpreted in the aggregation methods, including the interpretation of the domain I  and the function () I  (refer to [37], [38] for a definition of the interpretation).…”

Section: B Challengesmentioning

confidence: 99%

Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

et al. 2021

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Information used in existing ontology matching solutions are usually grouped into four categories: lexical information, structural information, semantic information, and external information, respectively. By summarizing and analyzing the approaches for utilizing the same kind of information, this paper finds that lexical information is mainly analyzed based on text and dictionary similarity. Similarly, structural information and semantic information are mainly analyzed based on graph structure and reasoner, respectively. The approaches for aggregating information analysis results are discussed. Challenges in the analysis of various types of information for existing ontology matching solutions are also described, and insights into directions for future research are provided.

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“…S-Match [16] and STROMA [17] are able to distinguish the different kinds of correspondences including equivalence, is-a and part-of correspondences between concepts of two ontologies based on linguistic characteristics and background knowledge from WordNet. COMPOSE [18] developed a three-stage alignment framework to detect equivalence and is-a correspondences. Recently, SBOMT [19] employed a statistical model to generate one-to-many alignment between two ontologies, which can discover all semantic relations for any concept pairs.…”

Section: Related Workmentioning

confidence: 99%

“…If the current node searched by a search path is an integrated equivalent concept, the searching will be temporarily stopped to estimate whether these searching paths have converged to a node or not (lines 10-17). If the current nodes being searching refer to a node, then the node is the source point and a block has been found; otherwise, the searching path at the highest level does not move, and the remaining paths continue to search upwards (lines [18][19][20][21][22][23]. This entire process is continued until the source node is found out.…”

Section: Detecting Blocksmentioning

confidence: 99%

A multi-strategy approach for the merging of multiple taxonomies

Mao

Yang

et al. 2020

Journal of Information Science

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Taxonomy merging is an important work to provide a uniform schema for several heterogeneous taxonomies. Previous studies primarily focus on merging two taxonomies in a specific domain, while the merging of multiple taxonomies has been neglected. This article proposes a taxonomy merging approach to automatically merge multiple source taxonomies into a target taxonomy in an asymmetric manner. The approach adopts a strategy of breaking up the whole into parts to decrease the complexity of merging multiple taxonomies and employs a block-based method to reduce the scale of measuring semantic relations between concept pairs. In addition, for the problem of multiple inheritance, a method of topical coverage is proposed. Experiments conducted on synthetic and real-world scenarios indicate that the proposed merging approach is feasible and effective to merge multiple taxonomies. In particular, the proposed approach works well in the aspects of limiting the semantic redundancy and establishing high-quality hierarchical relations between concepts.

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Matcher composition for identification of subsumption relations in ontology matching

Cited by 5 publications

References 11 publications

Extracting data models from background knowledge graphs

Extracting data models from background knowledge graphs

Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

A multi-strategy approach for the merging of multiple taxonomies

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