Arijit Khan. 2023. Knowledge Graphs Querying. SIGMOD Rec. 52, 2 (June 2023), 18–29. https://doi.org/10.1145/3615952.3615956 ABSTRACT Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. [Sistemas / tarefas onde consulta aos KGs é usada] First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). [Diversas perspectivas sobre os problemas que as consultas em KG trazem] Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. [De quais comunidades estã...
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Arxiv - Pré print https://arxiv.org/abs/2308.06585 Abstract Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and evolves, making existing facts deprecated or introducing new ones. However, we would still want to be able to answer queries as if the graph were complete. ... [Na abordagem do CKG partimos da premissa que os KG são incompletos e assim consideramos um Mundo Aberto Dual onde os KGs são compostos por alegações e não fatos] 1.1. Introduction Due to the incomplete nature of knowledge graphs, a query answering system might not return a complete set of answers because the graph does not contain all information needed to perfectly match the conditions in a query. To address this setting, approximate query answering methods have been proposed, which attempt to provide likely answers to a query by learning from patterns in the graph and generalizi...
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...
Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, Jens Lehmann: Message Passing for Hyper-Relational Knowledge Graphs. EMNLP (1) 2020: 7346-7359 Abstract Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - STARE capable of modeling such hyper-relational KGs. Unlike existing approaches, STARE can encode an arbitrary number of additional information (qualifiers) along with the main triple while keeping the semantic roles of qualifiers and triples intact. We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K. [ Dataset KG hiper relacional: poderia ser usado nas tarefas de exploração de KG? ] Our experiments demonstrate that STARE bas...