35 research outputs found
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Results of the ontology alignment evaluation initiative 2019
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2019 campaign offered 11 tracks with 29 test cases, and was attended by 20 participants. This paper is an overall presentation of that campaign
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Evaluating Ontology Matching Systems on Large, Multilingual and Real-world Test Cases
In the field of ontology matching, the most systematic evaluation of matching systems is established by the Ontology Alignment Evaluation Initiative (OAEI), which is an annual campaign for evaluating ontology matching systems organized by different groups of researchers. In this paper, we report on the results of an intermediary OAEI campaign called OAEI 2011.5. The evaluations of this campaign are divided in five tracks. Three of these tracks are new or have been improved compared to previous OAEI campaigns. Overall, we evaluated 18 matching systems. We discuss lessons learned, in terms of scalability, multilingual issues and the ability do deal with real world cases from different domains
Results of the second evaluation of matching tools
meilicke2012bThis deliverable reports on the results of the second SEALS evaluation campaign (for WP12 it is the third evaluation campaign), which has been carried out in coordination with the OAEI 2011.5 campaign. Opposed to OAEI 2010 and 2011 the full set of OAEI tracks has been executed with the help of SEALS technology. 19 systems have participated and five data sets have been used. Two of these data sets are new and have not been used in previous OAEI campaigns. In this deliverable we report on the data sets used in the campaign, the execution of the campaign, and we present and discuss the evaluation results
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Results of the Ontology Alignment Evaluation Initiative 2023
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2023 campaign offered 15 tracks and was attended by 16 participants. This paper is an overall presentation of that campaign
Investigating semantic similarity for biomedical ontology alignment
Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática) Universidade de Lisboa, Faculdade de CiĂŞncias, 2017A heterogeneidade dos dados biomĂ©dicos e o crescimento exponencial da informação dentro desse domĂnio tem levado Ă utilização de ontologias, que codificam o conhecimento de forma computacionalmente tratável. O desenvolvimento de uma ontologia decorre, em geral, com base nos requisitos da equipa que a desenvolve, podendo levar Ă criação de ontologias diferentes e potencialmente incompatĂveis por várias equipas de investigação. Isto implica que as várias ontologias existentes para codificar conhecimento biomĂ©dico possam, entre elas, sofrer de heterogeneidade: mesmo quando o domĂnio por elas codificado Ă© idĂŞntico, os conceitos podem ser representados de formas diferentes, com diferente especificidade e/ou granularidade. Para minimizar estas diferenças e criar representações mais standard e aceites pela comunidade, foram desenvolvidos algoritmos (matchers) que encontrassem pontes de conhecimento (mappings) entre as ontologias de forma a alinharem-nas. O tipo de algoritmos mais utilizados no Alinhamento de Ontologias (AO) sĂŁo os que utilizam a informação lĂ©xica (isto Ă©, os nomes, sinĂłnimos e descrições dos conceitos) para calcular as semelhanças entre os conceitos a serem mapeados. Uma abordagem complementar a esses algoritmos Ă© a utilização de Background Knowledge (BK) como forma de aumentar o nĂşmero de sinĂłnimos usados e assim aumentar a cobertura do alinhamento produzido. Uma alternativa aos algoritmos lĂ©xicos sĂŁo os algoritmos estruturais que partem do pressuposto que as ontologias foram desenvolvidas com pontos de vista semelhantes – realidade pouco comum. Surge entĂŁo o tema desta dissertação onde toma-se partido da Semelhança Semântica (SS) para o desenvolvimento de novos algoritmos de AO. É de salientar que atĂ© ao momento a utilização de SS no Alinhamento de Ontologias Ă© cingida Ă verificação de mappings e nĂŁo Ă sua procura. Esta dissertação apresenta o desenvolvimento, implementação e avaliação de dois algoritmos que utilizam SS, ambos usados como forma de estender alinhamentos produzidos previamente, um para encontrar mappings de equivalĂŞncias e o outro de subsunção (onde um conceito de uma ontologia Ă© mapeado como sendo descendente do conceito proveniente de outra ontologia). Os algoritmos propostos foram implementados no AML que Ă© um sistema topo de gama em Alinhamento de Ontologias. O algoritmo de equivalĂŞncia demonstrou uma melhoria de atĂ© 0.2% em termos de F-measure em comparação com o alinhamento âncora utilizado; e um aumento de atĂ© 11.3% quando comparado a outro sistema topo de gama (LogMapLt) que nĂŁo utiliza BK. É importante referir que, dentro do espaço de procura do algoritmo o Recall variou entre 66.7% e 100%. Já o algoritmo de subsunção apresentou precisĂŁo entre 75.9% e 95% (avaliado manualmente).The heterogeneity of biomedical data and the exponential growth of the information within this domain has led to the usage of ontologies, which encode knowledge in a computationally tractable way. Usually, the ontology’s development is based on the requirements of the research team, which means that ontologies of the same domain can be different and potentially incompatible among several research teams. This fact implies that the various existing ontologies encoding biomedical knowledge can, among them, suffer from heterogeneity: even when the encoded domain is identical, the concepts may be represented in different ways, with different specificity and/or granularity. To minimize these differences and to create representations that are more standard and accepted by the community, algorithms (known as matchers) were developed to search for bridges of knowledge (known as mappings) between the ontologies, in order to align them. The most commonly used type of matchers in Ontology Matching (OM) are the ones taking advantage of the lexical information (names, synonyms and textual description of the concepts) to calculate the similarities between the concepts to be mapped. A complementary approach to those algorithms is the usage of Background Knowledge (BK) as a way to increase the number of synonyms used, and further increase of the coverage of the produced alignment. An alternative to lexical algorithms are the structural ones which assume that the ontologies were developed with similar points of view - an unusual reality. The theme of this dissertation is to take advantage of Semantic Similarity (SS) for the development of new OM algorithms. It is important to emphasize that the use of SS in Ontology Alignment has, until now, been limited to the verification of mappings and not to its search. This dissertation presents the development, implementation, and evaluation of two algorithms that use SS. Both algorithms were used to extend previously produced alignments, one to search for equivalence and the other for subsumption mappings (where a concept of an ontology is mapped as descendant from a concept from another ontology). The proposed algorithms were implemented in AML, which is a top performing system in Ontology Matching. The equivalence algorithm showed an improvement in F-measure up to 0.2% when compared to the anchor alignment; and an increase of up to 11.3% when compared to another high-end system (LogMapLt) which lacks the usage of BK. It is important to note that, within the search space of the algorithm, the Recall ranged from 66.7% to 100%. On the other hand, the subsumption algorithm presented an accuracy between 75.9% and 95% (manually evaluated)
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Results of the ontology alignment evaluation initiative 2020
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign
Results of the Ontology Alignment Evaluation Initiative 2015
cheatham2016aInternational audienceOntology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation and consensus. OAEI 2015 offered 8 tracks with 15 test cases followed by 22 participants. Since 2011, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2015 campaign
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Matching disease and phenotype ontologies in the ontology alignment evaluation initiative
Background: The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease.
Results: Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results.
Conclusions: Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype
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Results of the Ontology Alignment Evaluation Initiative 2022
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall presentation of that campaign