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
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...
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph - Leitura de Artigo
ABSTRACT ... However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss. To address these challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph) framework. This framework establishes a cross-view hypergraph self-supervised learning mechanism for KG enhancement. Specifically, we model hyper-relational facts in KGs to capture interdependencies between entities under complete semantic conditions. With the refined representation, a hypergraph is dynamically constructed to preserve features in the deep vector space, thereby alleviating the over-smoothing ...
@article{PG-Schemas2023, author = {Angles, Renzo and Bonifati, Angela and Dumbrava, Stefania and Fletcher, George and Green, Alastair and Hidders, Jan and Li, Bei and Libkin, Leonid and Marsault, Victor and Martens, Wim and Murlak, Filip and Plantikow, Stefan and Savkovic, Ognjen and Schmidt, Michael and Sequeda, Juan and Staworko, Slawek and Tomaszuk, Dominik and Voigt, Hannes and Vrgoc, Domagoj and Wu, Mingxi and Zivkovic, Dusan}, title = {PG-Schema: Schemas for Property Graphs}, year = {2023}, issue_date = {June 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {1}, number = {2}, url = {https://doi.org/10.1145/3589778}, doi = {10.1145/3589778}, journal = {Proc. ACM Manag. Data}, month = {jun}, articleno = {198}, numpages = {25}, keywords = {property graphs, schemas, graph databases} } ABSTRACT Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO stan...