A Cooperative Approach for Composite Ontology Matching

Ontologies have proven to be an essential element in a range of applications in which knowl-edge plays a key role. Resolving the semantic heterogeneity problem is crucial to allow the interoperability between ontology-based systems. This makes automatic ontology matching, as an anticipated solution to semantic heterogeneity, an important, research issue. Many dif-ferent approaches to the matching problem have emerged from the literature. An important issue of ontology matching is to find effective ways of choosing among many techniques and their variations, and then combining their results. An innovative and promising option is to formalize the combination of matching techniques using agent-based approaches, such as cooperative negotiation and argumentation. In this thesis, the formalization of the on-tology matching problem following an agent-based approach is proposed. Such proposal is evaluated using state-of-the-art data sets. The results show that the consensus obtained by negotiation and argumentation represent intermediary values which are closer to the best matcher. As the best matcher may vary depending on specific differences of multiple data sets, cooperative approaches are an advantage. *** RESUMO - Ontologias são elementos essenciais em sistemas baseados em conhecimento. Resolver o problema de heterogeneidade semântica é fundamental para permitira interoperabilidade entre sistemas baseados em ontologias. Mapeamento automático de ontologias pode ser visto como uma solução para esse problema. Diferentes e complementares abordagens para o problema são propostas na literatura. Um aspecto importante em mapeamento consiste em selecionar o conjunto adequado de abordagens e suas variações, e então combinar seus resultados. Uma opção promissora envolve formalizara combinação de técnicas de ma-peamento usando abordagens baseadas em agentes cooperativos, tais como negociação e argumentação. Nesta tese, a formalização do problema de combinação de técnicas de ma-peamento usando tais abordagens é proposta e avaliada. A avaliação, que envolve conjuntos de testes sugeridos pela comunidade científica, permite concluir que o consenso obtido pela negociação e pela argumentação não é exatamente a melhoria de todos os resultados individuais, mas representa os valores intermediários que são próximo da melhor técnica. Considerando que a melhor técnica pode variar dependendo de diferencas específicas de múltiplas bases de dados, abordagens cooperativas são uma vantagem

Toward Shared Understanding : An Argumentation Based Approach for Communication in Open Multi-Agent Systems

Open distributed computing applications are becoming increasingly commonplace nowadays. In many cases, these applications are composed of multiple autonomous agents, each with its own aims and objectives. In such complex systems, communication between these agents is usually essential for them to perform their task, to coordinate their actions and share their knowledge. However, successful and meaningful communication can only be achieved by a shared understanding of each other's messages. Therefore efficient mechanisms are needed to reach a mutual understanding when exchanging expressions from each other's world model and background knowledge. We believe the de facto mechanisms for achieving this are ontologies, and this is the area explored in this thesis [88]. However, supporting shared understanding mechanisms for open distributed applications is a major research challenge. Specifically, one consequence of a system being open is the heterogeneity of the agents. Agents may have conflicting goals, or may be heterogeneous with respect to their beliefs or their knowledge. Forcing all agents to use a common vocabulary defined in one or more shared ontologies is, thus, an oversimplified solution, particularly when these agents are designed and deployed independently of each other. This thesis proposes a novel approach to overcome vocabulary heterogeneity, where the agents dynamically negotiate the meaning of the terms they use to communicate. While many proposals for aligning two agent ontologies have been presented in the literature as the current standard approaches to resolve heterogeneity, they are lacking when dealing with important features of agents and their environment. Motivated by the hypothesis that ontology alignment approaches should reflect the characteristics of autonomy and rationality that are typical of agents, and should also be tailored to the requirements of an open environment, such as dynamism, we propose a way for agents to define and agree upon the semantics of the terms used at run-time, according to their interests and preferences. Since agents are autonomous and represent different stakeholders, the process by which they come to an agreement will necessarily only come through negotiation. By using argumentation theory, agents generate and exchange different arguments, that support or reject possible mappings between vocabularies, according to their own preferences. Thus, this work provides a concrete instantiation of the meaning negotiation process that we would like agents to achieve, and that may lead to shared understanding. Moreover, in contrast to current ontology alignment approaches, the choice of a mapping is based on two clearly identified elements: (i) the argumentation framework, which is common to all agents, and (ii) the preference relations, which are private to each agent. Despite the large body of work in the area of semantic interoperabiJity, we are not aware of any research in this area that has directly addressed these important requirements for open Multi-Agent Systems as we have done in this thesis. Supplied by The British Library - 'The world's knowledge

False textual information detection, a deep learning approach

Many approaches exist for analysing fact checking for fake news identification, which is the focus of this thesis. Current approaches still perform badly on a large scale due to a lack of authority, or insufficient evidence, or in certain cases reliance on a single piece of evidence. To address the lack of evidence and the inability of models to generalise across domains, we propose a style-aware model for detecting false information and improving existing performance. We discovered that our model was effective at detecting false information when we evaluated its generalisation ability using news articles and Twitter corpora. We then propose to improve fact checking performance by incorporating warrants. We developed a highly efficient prediction model based on the results and demonstrated that incorporating is beneficial for fact checking. Due to a lack of external warrant data, we develop a novel model for generating warrants that aid in determining the credibility of a claim. The results indicate that when a pre-trained language model is combined with a multi-agent model, high-quality, diverse warrants are generated that contribute to task performance improvement. To resolve a biased opinion and making rational judgments, we propose a model that can generate multiple perspectives on the claim. Experiments confirm that our Perspectives Generation model allows for the generation of diverse perspectives with a higher degree of quality and diversity than any other baseline model. Additionally, we propose to improve the model's detection capability by generating an explainable alternative factual claim assisting the reader in identifying subtle issues that result in factual errors. The examination demonstrates that it does indeed increase the veracity of the claim. Finally, current research has focused on stance detection and fact checking separately, we propose a unified model that integrates both tasks. Classification results demonstrate that our proposed model outperforms state-of-the-art methods