Statistical analysis of the owl:sameAs network for aligning concepts in the linking open data cloud

The massively distributed publication of linked data has brought to the attention of scientific community the limitations of classic methods for achieving data integration and the opportunities of pushing the boundaries of the field by experimenting this collective enterprise that is the linking open data cloud. While reusing existing ontologies is the choice of preference, the exploitation of ontology alignments still is a required step for easing the burden of integrating heterogeneous data sets. Alignments, even between the most used vocabularies, is still poorly supported in systems nowadays whereas links between instances are the most widely used means for bridging the gap between different data sets. We provide in this paper an account of our statistical and qualitative analysis of the network of instance level equivalences in the Linking Open Data Cloud (i.e. the sameAs network) in order to automatically compute alignments at the conceptual level. Moreover, we explore the effect of ontological information when adopting classical Jaccard methods to the ontology alignment task. Automating such task will allow in fact to achieve a clearer conceptual description of the data at the cloud level, while improving the level of integration between datasets. <br/

Cross-lingual Linking on the Multilingual Web of Data (position statement)

Recently, the Semantic Web has experienced signi�cant advancements in standards and techniques, as well as in the amount of semantic information available online. Even so, mechanisms are still needed to automatically reconcile semantic information when it is expressed in di�erent natural languages, so that access to Web information across language barriers can be improved. That requires developing techniques for discovering and representing cross-lingual links on the Web of Data. In this paper we explore the different dimensions of such a problem and reflect on possible avenues of research on that topic

Is a Semantic Web Agent a Knowledge-Savvy Agent?

The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scale—possibly at the scale of the Internet. The very characteristics that define the Semantic Web—that is, dynamic, distributed, incomplete, and uncertain knowledge—suggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we can’t make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike

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

Will this work for Susan? Challenges for delivering usable and useful generic linked data browsers

While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools

Context-based ontology matching and data interlinking

euzenat2015cContext-based matching finds correspondences between entities from two ontologies by relating them to other resources. A general view of context-based matching is designed by analysing existing such matchers. This view is instantiated in a path-driven approach that (a) anchors the ontologies to external ontologies, (b) finds sequences of entities (path) that relate entities to match within and across these resources, and (c) uses algebras of relations for combining the relations obtained along these paths. Parameters governing such a system are identified and made explicit. We discuss the extension of this approach to data interlinking and its benefit to cross-lingual data interlinking. First, this extension would require an hybrid algebra of relation that combines relations between individual and classes. However, such an algebra may not be particularly useful in practice as only in a few restricted case it could conclude that two individuals are the same. But it can be used for finding mistakes in link sets

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