Referências extraídas do artigo sobre controvérsias na WD indicado pelo professor Altigran na defesa de proposta D. Artz and Y. Gil. A survey of trust in computer science and the semantic web. Journal ofWeb Semantics, 5(2), 2010. Abstract Trust is an integral component in many kinds of human interaction, allowing people to act under uncertainty and with the risk of negative consequences. For example, exchanging money for a service, giving access to your property, and choosing between conflicting sources of information all may utilize some form of trust. In computer science, trust is a widely used term whose definition differs among researchers and application areas. Trust is an essential component of the vision for the Semantic Web, where both new problems and new applications of trust are being studied. This paper gives an overview of existing trust research in computer science and the Semantic Web [Confiar a ponto de agir, mesmo assumindo riscos] 1. Introduction Trust is a centr...
Postagens
Dando uma googada https://iep.utm.edu/truth/ There are two commonly accepted constraints on truth and falsehood: Every proposition is true or false. [Law of the Excluded Middle.] No proposition is both true and false. [Law of Non-contradiction.] What is the difference between a truth-maker and a truth bearer? Truth-bearers are either true or false; truth-makers are not since, not being representations, they cannot be said to be true, nor can they be said to be false . That's a second difference. Truth-bearers are 'bipolar,' either true or false; truth-makers are 'unipolar': all of them obtain. What are considered truth bearers? A variety of truth bearers are considered – statements, beliefs, claims, assumptions, hypotheses, propositions, sentences, and utterances . When I speak of a fact . . . I mean the kind of thing that makes a proposition true or false. (Russe...
Keynote on Youtube -> https://youtu.be/DZ6NlcW4YV8?si=4Z5zDA1Vx_D10GKz No Intelligence Without Knowledge Katja Hose TU Wien, Austria Abstract. Knowledge graphs and graph data in general are becoming more and more essential components of intelligent systems. This does not only include native graph data, such as social networks or Linked Data on the Web. The flexibility of the graph model and its ability to store data relationships explicitly enables the integration and exploitation of data from very diverse sources. However, to truly exploit their potential, it becomes crucial to provide intelligent systems with verifiable knowledge, reliable facts, patterns, and a deeper understanding of the underlying domains. This talk will therefore chart a number of challenges for exploiting graphs to manage and bring meaning to large amounts of heterogeneous data and discuss opportunities with, without, and for artificial intelligence emerging from research situated at the confluence of data m...
https://arxiv.org/pdf/2201.11903.pdf Chain-of-Thought (CoT) para elaborar perguntas aos LLMs mas as perguntas podem ser incompletas no que diz respeito ao contexto. A busca exploratório é formada por várias perguntas elaboradas ao longo do processo. Está relacionado com "Fine Tuning" na tarefa a ser executada pq está ensinando ao LLM como responder Introduction However, scaling up model size alone has not proved sufficient for achieving high performance on challenging tasks such as arithmetic, commonsense, and symbolic reasoning (Raeet al., 2021). This work explores how the reasoning ability of large language models can be unlocked by a simple method motivated by two ideas. First, techniques for arithmetic reasoning can benefit from generating natural language rationales that lead to the final answer. Prior work has given models the ability to generate natural language intermediate steps by training from scratch (Ling et al., 2017) or finetuning a pretrained model (Cobbe e...
Com Daniel Exemplos de Proveniência: Caso do IBGE e Redes Sociais Meta-Query sobre contexto de Entidades e Conceitos SHACL e SHEX para descrever o esquema de grafo pq as Ontologias não tem esta expressividade. A meta-query sobre Entidades e Conceitos deve se basear na respectiva Classe (tipo) que possui determinada propriedade ou relação correspondente ao contexto. Se o tipo não estiver presente no KG (incompleto) deve ser "sugerido" por regra. (?c1, ckg:Contextualizes, ?tipo. [id entidade], ?c1, ?valor . [id entidade], rdf:type, ?tipo. * ?C {label = ?C.label}, ckg:Represented By, ?c1 Se o ?tipo for unbounded em *, o tipo que está na camada de contexto pode ser atribuído como o tipo da entidade (regra que explica a dedução está baseada no contexto) e seria semelhante a explicação da regra default. Os qualificadores se aplicam a todos os relacionamentos, tanto entre nós como entre nós e literais. Fechamento do exemplo de Espaço-Temporal O condicional da data de referência ...
Das leituras que fiz durante a minha pesquisa bibliográfica, a única abordagem que percebi que poderia ser aplicada para relacionar KG e redes sociais seria nas tarefas de fact-checking. Os 3 artigos abaixo apresentam um modelo de Alegações Contextualizados usando a Ontologia Schema.org que separa a proposição (justificativa e representação formal), a declaração (expressão da alegação) e o contexto da declaração realizada. As declarações podem estar associadas a qualquer recurso online (blogs, redes sociais, sites da imprensa, etc ...). É o caso de análise de discurso para identificar "Quem disse o quê", "Quando disse", "Onde disse" e "Por que disse" bem como ter UMA justificativa na forma de proposição para o que é dito mas o modelo NÃO prevê o contexto das proposições para uma avaliação de sua veracidade (uso a revisão de sites de Fact-Checking). A separação entre fato e alegação está definida da seguinte forma: " the notion of a cla...
Fonte: https://plato.stanford.edu/entries/abduction/ In the philosophical literature, the term “ abduction ” is used in two related but different senses. In both senses, the term refers to some form of explanatory reasoning . However, in the historically first sense, it refers to the place of explanatory reasoning in generating hypotheses, while in the sense in which it is used most frequently in the modern literature it refers to the place of explanatory reasoning in justifying hypotheses. In the latter sense, abduction is also often called “ Inference to the Best Explanation. ” [Definição moderna: justificar hipóteses. Inferir a Melhor Explicação] Most philosophers agree that abduction (in the sense of Inference to the Best Explanation) is a type of inference that is frequently employed, in some form or other, both in everyday and in scientific reasoning. [Usada tanto no dia a dia quanto na própria ciência] The type of inference exhibited here is called abduction or, somewhat more c...