Visualization for biomedical ontologies alignment

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2016Desde o início do século, a investigação biomédica e a prática clínica levaram a uma acumulação de grandes quantidades de informação, por exemplo, os dados resultantes da sequenciação genómica ou os registos médicos. As ontologias fornecem um modelo estruturado com o intuito de representar o conhecimento e têm sido bem sucedidas no domínio biomédico na melhoria da interoperabilidade e partilha. O desenvolvimento desconectado das ontologias biomédicas levou à criação de modelos que apresentam domínios idênticos ou sobrepostos. As técnicas de emparelhamento de ontologias foram desenvolvidas afim de estabelecer ligações significativas entre as classes das ontologias, por outras palavras, para criar alinhamentos. Para alcançar um alinhamento ótimo é, não só importante melhorar as técnicas de emparelhamentos mas também criar as ferramentas necessárias para que possa existir intervenção humana, particularmente na visualização. Apesar da importância da intervenção de utilizadores e da visualização no emparelhamento de ontologias, poucos sistemas o suportam, sobretudo para grandes e complexas ontologias como as do domínio biomédico, concretamente no contexto da revisão de alinhamentos e interpretação de incoerências lógicas. O objetivo central desta tese consistiu na investigação dos principais paradigmas de visualização de ontologias, no contexto do alinhamento de ontologias biomédicas, e desenvolver abordagens de visualização e interação que vão de encontro a estes desafios. O trabalho desenvolvido levou, então, à criação de um novo módulo de visualização para um sistema de emparelhamento do state of the art que suporta a revisão de alinhamentos, e à construção de uma ferramenta online que visa ajudar o utilizador a compreender os conflitos encontrados nos alinhamentos, ambos baseados numa abordagem de visualização de subgrafos. Ambas as contribuições foram avaliadas em pequena escala, por testes a utilizadores que revelaram a relevância da visualização de subgrafos contra a visualização em árvore, mais comum no domínio biomédico.Since the begin of the century, biomedical research and clinical practice have resulted in the accumulation of very large amounts of information, e.g. data from genomic sequencing or medical records. Ontologies provide a structured model to represent knowledge and have been quite successful in the biomedical domain at improving interoperability and sharing. The disconnected development of biomedical ontologies has led to the creation of models that have overlapping or even equal domains. Ontology matching techniques were developed to establish meaningful connections between classes of the ontologies, in other words to create alignments. In order to achieve an optimal alignment, it is not only important to improve the matching techniques but also to create the necessary tools for human intervention, namely in visualization. Despite the importance of user intervention and visualization in ontology matching, few systems support these, especially for large and complex ontologies such as those in the biomedical domain, specifically in the context of the alignment revision and logical incoherence explanation. The central objective of this thesis was to investigate the main ontology visualization paradigms, in the context of biomedical ontology matching, and to develop visualization and interaction approaches addressing those challenges. The work developed lead to the creation of a new visualization module for a state of the art ontology matching system, that supports the alignment review, and to the construction of an online tool that aims to help the user understand the conflicts found in the alignments both based on a subgraph visualization approach. Both contributions were evaluated, in a small-scale, by user tests that revealed the relevance of subgraph visualization versus the more common tree visualization for the biomedical domain

Performance assessment of ontology matching systems for FAIR data

© The Author(s). 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application from the rare disease domain. Additionally, we present a method for analyzing top-level classes to improve precision. Results: We included three ontologies (NCIt, SNOMED CT, ORDO) and three matching systems (AgreementMakerLight 2.0, FCA-Map, LogMap 2.0). We evaluated the performance of the matching systems against reference alignments from BioPortal and the Unified Medical Language System Metathesaurus (UMLS). Then, we analyzed the top-level ancestors of matched classes, to detect incorrect mappings without consulting a reference alignment. To detect such incorrect mappings, we manually matched semantically equivalent top-level classes of ontology pairs. AgreementMakerLight 2.0, FCA-Map, and LogMap 2.0 had F1-scores of 0.55, 0.46, 0.55 for BioPortal and 0.66, 0.53, 0.58 for the UMLS respectively. Using vote-based consensus alignments increased performance across the board. Evaluation with manually created top-level hierarchy mappings revealed that on average 90% of the mappings’ classes belonged to top-level classes that matched. Conclusions: Our findings show that the included ontology matching systems automatically produced mappings that were modestly accurate according to our evaluation. The hierarchical analysis of mappings seems promising when no reference alignments are available. All in all, the systems show potential to be implemented as part of an ontology matching service for querying FAIR data. Future research should focus on developing methods for the evaluation of mappings used in such mapping services, leading to their implementation in a FAIR data ecosystem

Evaluating Pre-trained Word Embeddings in domain specific Ontology Matching

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2022The ontology matching process focuses on discovering mappings between two concepts from distinct ontologies, a source and a target. It is a fundamental step when trying to integrate heterogeneous data sources that are described in ontologies. This data represents an even more challenging problem since we are working with complex data as biomedical data. Thus, derived from the necessity of keeping on improving ontology matching techniques, this dissertation focused on implementing a new approach to the AML pipeline to calculate similarities between entities from two distinct ontologies. For the implementation of this dissertation, we used some of the OAEI tracks, such as Anatomy and LargeBio, to apply a new algorithm and evaluate if it improves AML’s results against a refer ence alignment. This new approach consisted of using pre-trained word embeddings of five different types, BioWordVec Extrinsic, BioWordVec Intrinsic, PubMed+PC, PubMed+PC+Wikipedia and English Wikipedia. These pre-trained word embeddings use a machine learning technique, Word2Vec, and were used in this work since it allows to carry the semantic meaning inherent to the words represented with the corresponding vector. Word embeddings allowed that each concept of each ontology was represented with a corresponding vector to see if, with that information, it was possible to improve how relations between concepts were determined in the AML system. The similarity between concepts was calculated through the cosine distance and the evaluation of the new alignment used the metrics precision recall and F-measure. Although we could not prove that word embeddings improve AML current results, this implementation could be refined, and the technique can be still an option to consider in future work if applied in some other way

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)

Matching Biomedical Knowledge Graphs with Neural Embeddings

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Os grafos de conhecimento são estruturas que se tornaram fundamentais para a organização dos dados biomédicos que têm sido produzidos a um ritmo exponencial nos últimos anos. A abrangente adoção desta forma de estruturar e descrever dados levou ao desenvolvimento de abordagens de prospeção de dados que tirassem partido desta informação com o intuito de auxiliar o progresso do conhecimento científico. Porém, devido à impossibilidade de isolamento de domínios de conhecimento e à idiossincrasia humana, grafos de conhecimento construídos por diferentes indivíduos contêm muitas vezes conceitos equivalentes descritos de forma diferente, dificultando uma análise integrada de dados de diferentes grafos de conhecimento. Vários sistemas de alinhamento de grafos de conhecimento têm focado a resolução deste desafio. Contudo, o desempenho destes sistemas no alinhamento de grafos de conhecimento biomédicos estagnou nos últimos quatro anos com algoritmos e recursos externos bastante trabalhados para aprimorar os resultados. Nesta dissertação, apresentamos duas novas abordagens de alinhamento de grafos de conhecimento empregando Neural Embeddings: uma utilizando semelhança simples entre embeddings à base de palavras e de entidades de grafos; outra treinando um modelo mais complexo que refinasse a informação proveniente de embeddings baseados em palavras. A metodologia proposta visa integrar estas abordagens no processo regular de alinhamento, utilizando como infraestrutura o sistema AgreementMakerLight. Estas novas componentes permitem extender os algoritmos de alinhamento do sistema, descobrindo novos mapeamentos, e criar uma abordagem de alinhamento mais generalizável e menos dependente de ontologias biomédicas externas. Esta nova metodologia foi avaliada em três casos de teste de alinhamento de ontologias biomédicas, provenientes da Ontology Alignment Evaluation Initiative. Os resultados demonstraram que apesar de ambas as abordagens não excederem o estado da arte, estas obtiveram um desempenho benéfico nas tarefas de alinhamento, superando a performance de todos os sistemas que não usam ontologias externas e inclusive alguns que tiram proveito das mesmas, o que demonstra o valor das técnicas de Neural Embeddings na tarefa de alinhamento de grafos do conhecimento biomédicos.Knowledge graphs are data structures which became essential to organize biomedical data produced at an exponential rate in the last few years. The broad adoption of this method of structuring and describing data resulted in the increased interest to develop data mining approaches which took advantage of these information structures in order to improve scientific knowledge. However, due to human idiosyncrasy and also the impossibility to isolate knowledge domains in separate pieces, knowledge graphs constructed by different individuals often contain equivalent concepts described differently. This obstructs the path to an integrated analysis of data described by multiple knowledge graphs. Multiple knowledge graph matching systems have been developed to address this challenge. Nevertheless, the performance of these systems has stagnated in the last four years, despite the fact that they were provided with highly tailored algorithms and external resources to tackle this task. In this dissertation, we present two novel knowledge graph matching approaches employing neural embeddings: one using plain embedding similarity based on word and graph models; the other one using a more complex word-based model which requires training data to refine embeddings. The proposed methodology aims to integrate these approaches in the regular matching process, using the AgreementMakerLight system as a foundation. These new components enable the extension of the system’s current matching algorithms, discovering new mappings, and developing a more generalizable and less dependent on external biomedical ontologies matching procedure. This new methodology was evaluated on three biomedical ontology matching test cases provided by the Ontology Alignment Evaluation Initiative. The results showed that despite both embedding approaches don’t exceed state of the art results, they still produce better results than any other matching systems which do not make use of external ontologies and also surpass some that do benefit from them. This shows that Neural Embeddings are a valuable technique to tackle the challenge of biomedical knowledge graph matching

Truveta Mapper: A Zero-shot Ontology Alignment Framework

In this paper, a new perspective is suggested for unsupervised Ontology Matching (OM) or Ontology Alignment (OA) by treating it as a translation task. Ontologies are represented as graphs, and the translation is performed from a node in the source ontology graph to a path in the target ontology graph. The proposed framework, Truveta Mapper (TM), leverages a multi-task sequence-to-sequence transformer model to perform alignment across multiple ontologies in a zero-shot, unified and end-to-end manner. Multi-tasking enables the model to implicitly learn the relationship between different ontologies via transfer-learning without requiring any explicit cross-ontology manually labeled data. This also enables the formulated framework to outperform existing solutions for both runtime latency and alignment quality. The model is pre-trained and fine-tuned only on publicly available text corpus and inner-ontologies data. The proposed solution outperforms state-of-the-art approaches, Edit-Similarity, LogMap, AML, BERTMap, and the recently presented new OM frameworks in Ontology Alignment Evaluation Initiative (OAEI22), offers log-linear complexity in contrast to quadratic in the existing end-to-end methods, and overall makes the OM task efficient and more straightforward without much post-processing involving mapping extension or mapping repair

Ontology Matching Techniques for Enterprise Architecture Models

Abstract. Current Enterprise Architecture (EA) approaches tend to be generic, based on broad meta-models that cross-cut distinct architectural domains. Integrating these models is necessary to an effective EA process, in order to support, for example, benchmarking of business processes or assessing compliance to structured requirements. However, the integration of EA models faces challenges stemming from structural and semantic heterogeneities that could be addressed by ontology matching techniques. For that, we used AgreementMakerLight, an ontology matching system, to evaluate a set of state of the art matching approaches that could adequately address some of the heterogeneity issues. We assessed the matching of EA models based on the ArchiMate and BPMN languages, which made possible to conclude about not only the potential but also of the limitations of these techniques to properly explore the more complex semantics present in these models. Enterprise Architecture (EA) is a practice to support the analysis, design and implementation of a business strategy in an organization, considering its relevant multiple domains. In recent years, a variety of Enterprise Architecture To support the matching tasks we have used AgreementMakerLight (AML