Improving the interoperability of biomedical ontologies with compound alignments

Oliveira, Daniela; Pesquita, Cátia

doi:10.1186/s13326-017-0171-8

Cited by 32 publications

(19 citation statements)

References 30 publications

(21 reference statements)

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“…A notable alignment set has been proposed between biology ontologies in [19]. In opposite to "classical" alignments, the correspondences of these alignments, called "compound alignments" involve entities from more than two ontologies.…”

Section: Evaluation Of Matchersmentioning

confidence: 99%

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: Evaluation Of Matchersmentioning

confidence: 99%

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

et al. 2018

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“…Fifth, we plan to extract knowledge using additional sources than SNOMED CT such as NCI Thesaurus (Sioutos et al, 2007) that could be useful to build definitions for MedDRA terms that describe cancer-related adverse reactions. A recent work by Oliveira and Pesquita, (2018) reports that current ontology matching techniques and systems are mostly devoted to finding links between two equivalent entities from two distinct ontologies. However, different domains may be involved that requires the implementation of matching techniques that allow linking more than two ontologies through more complex relations.…”

Section: Discussionmentioning

confidence: 99%

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties

Bousquet¹,

Souvignet²,

Sadou³

et al. 2019

Front. Pharmacol.

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Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic). Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.

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“…When the class pair’s similarity value is bigger than upper threshold, its correspondence’s relationship is “equivalent”; when the class pair’s similarity value is between upper threshold and lower threshold, its correspondence’s relationship is “subsumption”; when the class pair’s similarity value is lower than lower threshold, this correspondence will be removed. Next, for each object class, we utilize the approach proposed by D. Oliveira et al [ 8 ] to further determine the relationships of “union” and “intersection” among its subsumed target classes.…”

Section: Compact Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Optimizing Hydrography Ontology Alignment Through Compact Particle Swarm Optimization Algorithm

Wang

Yao

Wan

et al. 2020

Lecture Notes in Computer Science

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With the explosive growth in generating data in the hydrographical domain, many hydrography ontologies have been developed and maintained to describe hydrographical features and the relationships between them. However, the existing hydrography ontologies are developed with varying project perspectives and objectives, which inevitably results in the differences in terms of knowledge representation. Determining various relationships between two entities in different ontologies offers the opportunity to link hydrographical data for multiple purposes, though the research on this topic is in its infancy. Different from the traditional ontology alignment whose cardinality is 1:1, i.e. one source ontology entity is mapping with one target ontology entity and vice versa, and the relationship is the equivalence, matching hydrography ontologies is a more complex task, whose cardinality could be 1:1, 1:n or m:n and the relationships could be equivalence or subsumption. To efficiently optimize the ontology alignment, in this paper, a discrete optimal model is first constructed for the ontology matching problem, and then a Compact Particle Swarm Optimization algorithm (CPSO) based matching technique is proposed to efficiently solve it. CPSO utilizes the compact real-value encoding and decoding mechanism and the objective-decomposing strategy to approximate the PSO’s evolving process, which can dramatically reduce PSO’s memory consumption and runtime while at the same time ensure the solution’s quality. The experiment exploits the Hydrography dataset in Complex track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CPSO-based approach can effectively reduce PSO’s runtime and memory consumption, and determine high-quality hydrography ontology alignments.

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Improving the interoperability of biomedical ontologies with compound alignments

Cited by 32 publications

References 30 publications

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties

Optimizing Hydrography Ontology Alignment Through Compact Particle Swarm Optimization Algorithm

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