Automatic Background Knowledge Selection for Matching Biomedical Ontologies

Faria, Daniel; Pesquita, Cátia; Santos, Emanuel; Cruz, Isabel F.; Couto, Francisco M.

doi:10.1371/journal.pone.0111226

Cited by 27 publications

(35 citation statements)

References 18 publications

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“…In [23], an automatic background knowledge selection approach has been proposed for the particular task of matching biomedical ontologies based on the notion of mappings gain (MG). MG is used to estimate the individual usefulness of background knowledge sources and is defined as a function of the improvement of the number of correct mappings by using a given BK source as compared to a direct mapping of two ontologies.…”

Section: Automatic Selection Of Background Knowledgementioning

confidence: 99%

“…In contrast, we use as BKs well-established and widely used knowledge graphs that are much likely to be called upon as BK sources for solving a real-life alignment problem. In that line of thought, our approach is generic and can handle different domains, contrarily to [23].…”

Section: Automatic Selection Of Background Knowledgementioning

confidence: 99%

“…In both [24] and [23] the BK selection appears to be strongly coupled with the actual matching procedure. One of the main motivations of work is to dissociate completely the BK selection process from the alignment procedure.…”

Section: Automatic Selection Of Background Knowledgementioning

confidence: 99%

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Selecting Optimal Background Knowledge Sources for the Ontology Matching Task

Tigrine

Bellahsène

Todorov

2016

Lecture Notes in Computer Science

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Abstract. It is a common practice to rely on background knowledge (BK) in order to assist and improve the ontology matching process. The choice of an appropriate source of background knowledge for a given matching task, however, remains a vastly unexplored question. In the current paper, we propose an automatic BK selection approach that does not depend on an initial direct matching, can handle multilingualism and is domain independent. The approach is based on the construction of an index for a set of BK candidates. The couple of ontologies to be aligned is modeled as a query with respect to the indexed BK sources and the best candidate is selected by following an information retrieval paradigm. We evaluate our system in a series of experiments in both general-purpose and domain-specific matching scenarios. The results show that our approach is capable of selecting the BK that provides the best alignment quality with respect to a given reference alignment for each of the considered matching tasks.

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Section: Automatic Selection Of Background Knowledgementioning

confidence: 99%

Section: Automatic Selection Of Background Knowledgementioning

confidence: 99%

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Selecting Optimal Background Knowledge Sources for the Ontology Matching Task

Tigrine

Bellahsène

Todorov

2016

Lecture Notes in Computer Science

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“…In (Lin and Sandkuhl, 2008) a survey on methods that use Wordnet (Miller, 1995) for ontology alignment, is carried out. Approaches for exploiting other external knowledge sources have been presented (Sabou et al, 2006;Pesquita et al, 2014;Chen et al, 2014;Faria et al, 2014). Other similarity measures rely on the structure of the ontologies, such as the Similarity Flooding (Melnik et al, 2002) algorithm that stems from the relational databases world but has been successfully used for ontology alignment, while others exploit both schema and ontology semantics for mapping discovery.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Visual Similarities for Ontology Alignment

Doulaverakis

Vrochidis

Kompatsiaris

2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Abstract:Ontology alignment is the process where two different ontologies that usually describe similar domains are 'aligned', i.e. a set of correspondences between their entities, regarding semantic equivalence, is determined. In order to identify these correspondences several methods and metrics that measure semantic equivalence have been proposed in literature. The most common features that these metrics employ are string-, lexical-, structure-and semantic-based similarities for which several approaches have been developed. However, what hasn't been investigated is the usage of visual-based features for determining entity similarity in cases where images are associated with concepts. Nowadays the existence of several resources (e.g. ImageNet) that map lexical concepts onto images allows for exploiting visual similarities for this purpose. In this paper, a novel approach for ontology matching based on visual similarity is presented. Each ontological entity is associated with sets of images, retrieved through ImageNet or web-based search, and state of the art visual feature extraction, clustering and indexing for computing the similarity between entities is employed. An adaptation of a popular Wordnet-based matching algorithm to exploit the visual similarity is also proposed. Our method is compared with traditional metrics against a standard ontology alignment benchmark dataset and demonstrates promising results.

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“…The selection of background knowledge resources consists in choosing m resources among the n possible ones [23,27,9,37]. Although only fragments of the m resources may actually prove effective for discovering new mappings, entire resources are selected.…”

Section: Introductionmentioning

confidence: 99%

Building an effective and efficient background knowledge resource to enhance ontology matching

Annane

Bellahsène

Azouaou

et al. 2018

Journal of Web Semantics

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Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F-measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources.

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Automatic Background Knowledge Selection for Matching Biomedical Ontologies

Cited by 27 publications

References 18 publications

Selecting Optimal Background Knowledge Sources for the Ontology Matching Task

Selecting Optimal Background Knowledge Sources for the Ontology Matching Task

Exploiting Visual Similarities for Ontology Alignment

Building an effective and efficient background knowledge resource to enhance ontology matching

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