Results of the Ontology Alignment Evaluation Initiative 2009

euzenat2009cInternational audienceOntology matching consists of finding correspondences between on- tology entities. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. Test cases can use ontologies of different nature (from expressive OWL ontologies to simple directories) and use different modal- ities, e.g., blind evaluation, open evaluation, consensus. OAEI-2009 builds over previous campaigns by having 5 tracks with 11 test cases followed by 16 partici- pants. This paper is an overall presentation of the OAEI 2009 campaign

Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences Among Ontologies

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'sability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the networks. The output of one network in response to a stimulus to another network can be interpreted as an analogical mapping. In a similar fashion, the networks can be explicitly trained to mapspecific items in one domain to specific items in another domain. Representation layer helpsthe network learn relationship mapping with direct training method.The OMNN approach is tested on family tree test cases. Node mapping, relationshipmapping, unequal structure mapping, and scalability test are performed. Results showthat OMNN is able to learn and infer correspondences in tree-like structures. Furthermore, OMNN is applied to several OAEI benchmark test cases to test its performance on ontologymapping. Results show that OMNN approach is competitive to the top performing systems that participated in OAEI 2009

Ontology matching: state of the art and future challenges

shvaiko2013aInternational audienceAfter years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue some further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field

The Role of String Similarity Metrics in Ontology Alignment

Tim Berners-Lee originally envisioned a much different world wide web than the one we have today - one that computers as well as humans could search for the information they need [3]. There are currently a wide variety of research efforts towards achieving this goal, one of which is ontology alignment

Ontology mapping neural network: An approach to learning and inferring correspondences among ontologies

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the networks. The output of one network in response to a stimulus to another network can be interpreted as an analogical mapping. In a similar fashion, the networks can be explicitly trained to map specific items in one domain to specific items in another domain. Representation layer helps the network learn relationship mapping with direct training method. OMNN is applied to several OAEI benchmark test cases to test its performance on ontology mapping. Results show that OMNN approach is competitive to the top performing systems that participated in OAEI 2009

Dealing with uncertain entities in ontology alignment using rough sets

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision

Investigating semantic similarity for biomedical ontology alignment

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática) Universidade de Lisboa, Faculdade de Ciências, 2017A heterogeneidade dos dados biomédicos e o crescimento exponencial da informação dentro desse domínio tem levado à 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 à criação de ontologias diferentes e potencialmente incompatíveis por várias equipas de investigação. Isto implica que as várias ontologias existentes para codificar conhecimento biomédico possam, entre elas, sofrer de heterogeneidade: mesmo quando o domínio por elas codificado é idêntico, 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) são os que utilizam a informação léxica (isto é, os nomes, sinónimos e descrições dos conceitos) para calcular as semelhanças entre os conceitos a serem mapeados. Uma abordagem complementar a esses algoritmos é a utilização de Background Knowledge (BK) como forma de aumentar o número de sinónimos usados e assim aumentar a cobertura do alinhamento produzido. Uma alternativa aos algoritmos léxicos são os algoritmos estruturais que partem do pressuposto que as ontologias foram desenvolvidas com pontos de vista semelhantes – realidade pouco comum. Surge então o tema desta dissertação onde toma-se partido da Semelhança Semântica (SS) para o desenvolvimento de novos algoritmos de AO. É de salientar que até ao momento a utilização de SS no Alinhamento de Ontologias é cingida à verificação de mappings e não à 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 equivalências e o outro de subsunção (onde um conceito de uma ontologia é mapeado como sendo descendente do conceito proveniente de outra ontologia). Os algoritmos propostos foram implementados no AML que é um sistema topo de gama em Alinhamento de Ontologias. O algoritmo de equivalência demonstrou uma melhoria de até 0.2% em termos de F-measure em comparação com o alinhamento âncora utilizado; e um aumento de até 11.3% quando comparado a outro sistema topo de gama (LogMapLt) que não 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 precisão 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)

The state of semantic technology today - overview of the first SEALS evaluation campaigns

This paper describes the first five SEALS Evaluation Campaigns over the semantic technologies covered by the SEALS project (ontology engineering tools, ontology reasoning tools, ontology matching tools, semantic search tools, and semantic web service tools). It presents the evaluations and test data used in these campaigns and the tools that participated in them along with a comparative analysis of their results. It also presents some lessons learnt after the execution of the evaluation campaigns and draws some final conclusions