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...
Postagens
Valeria Fionda and Giuseppe Pirrò. 2017. Explaining and querying knowledge graphs by relatedness. Proc. VLDB Endow . 10, 12 (August 2017), 1913–1916. DOI:https://doi.org/10.14778/3137765.3137807 ABSTRACT We demonstrate RECAP, a tool that explains relatedness between entities in Knowledge Graphs (KGs) and imple ments a query by relatedness paradigm that allows to re trieve entities related to those in input. One of the peculiar ities of RECAP is that it does not require any data prepro cessing and can combine knowledge from multiple KGs. The underlying algorithmic techniques are reduced to the execu tion of SPARQL queries plus some local refinement. This makes the tool readily available on a large variety of KGs accessible via SPARQL endpoints. To show the general ap plicability of the tool, we will cover a set of use cases drawn from a variety of knowledge domains (e.g., biology, movies, co-authorship networks) and report on the concrete usage of RECAP in t...