Peter F. Patel-Schneider: Contextualization via Qualifiers. CKGSemStats@ISWC 2018 Sarven Capadisli, Franck Cotton, José M. Giménez-GarcÃa, Armin Haller, Evangelos Kalampokis, Vinh Nguyen, Amit P. Sheth, Raphaël Troncy: Joint Proceedings of the International Workshops on Contextualized Knowledge Graphs , and Semantic Statistics co-located with 17th International Semantic Web Conference (ISWC 2018). CEUR Workshop Proceedings 2317, CEUR-WS.org 2018 Abstract. A common method for contextualizing facts in knowledge graph formalisms is by adding property-value pairs, qualifiers, to the facts in the knowledge graph. [Afirmações contextualizadas e não fatos] Qualifiers work well for information that is additional to the base fact but pose an unwarranted burden on consumers of the information in knowledge graphs when the qualifier instead contextualizes the base fact, as in limiting the applicability of the fact to some time period or providing a confidence level for the fact. [Pq seria um fa
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ABSTRACT Pervasive systems present the need to interpret large quantities of data from many sources. [Integração de fontes de dados] Context models support developers working with such data by providing a shared representation of the environment on which to base this interpretation. This paper presents a set of requirements for a context model that addresses uncertainty, provenance, sensing and temporal properties of context. [Dimensões contextuais que estou tratando no CKG: proveniencia, temporal, localidade e identidade. Outras dimensões podem ser Incertezas e "Sensores" e isso depende do domÃnio do KG e/ou aplicação] 1. INTRODUCTION Pervasive computing is an evolution of the desktop computing paradigm, whereby almost any object, from home furnishings and appliances, to cars, to clothing, even to coffee mugs and credit cards can be embedded with sensing and processing capabilities [1]. [IoT] Through networks of these devices, information about people and their surrounding
Yueji Yang, Yuchen Li, Panagiotis Karras, and Anthony K. H. Tung. 2021. Context-aware Outstanding Fact Mining from Knowledge Graphs. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD '21). Association for Computing Machinery, New York, NY, USA, 2006–2016. https://doi.org/10.1145/3447548.3467272 ABSTRACT An Outstanding Fact (OF) is an attribute that makes a target entity stand out from its peers. [Diferenciar uma entidade das demais no sentido de identificar atributos e relações que são pouco comuns, que não correspondem ao padrão] However, existing approaches to OF mining: (i) disregard the context in which the target entity appears, hence may report facts irrelevant to that context; and (ii) require relational data, which are often unavailable or incomplete in many application domains. [Contexto é útil para esse tipo de tarefa mas o que é esse contexto?] In this paper, we introduce the novel problem of mining Context-aware Outstandi
Reasoning with contextual graphs Patrick Brezillon *, Laurent Pasquier, Jean-Charles Pomerol Received 15 December 1999; accepted 15 October 2000 Abstract ... model highly contextual reasoning ... introduce the notion of contextual graph to take into account temporal and context-based reasoning. This model relies on observed reasoning modes in which the context and its dynamics are essential. Introduction [SART e incidentes de controle de tráfego] ... three types of context, namely, the external context, the contextual knowledge and the proceduralized context. These three types of context allow to model the various information needed at each step of the incident resolution. [Divisão em três tipos: contexto do conhecimento (a ser usado para a tomada de decisão), contexto externo (parte não utilizada mas ainda assim necessária para contextualizar completamente a resposta) e contexto procedural (parte do contexto do conhecimento que está sendo usado em um ponto especÃfico da tomada de deci
Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann (2021) Knowledge Graphs, Synthesis Lectures on Data, Semantics, and Knowledge, No. 22, 1–237, DOI: 10.2200/S01125ED1V01Y202109DSK022, Morgan & Claypool Chapter 3 - Schema, Identity, Context https://www.emse.fr/~zimmermann/KGBook/Multifile/schema-identity-context/ We refer to a knowledge graph as a data graph potentially enhanced with representations of schema, identity, context , ontologies and/or rules . These additional representations may be embedded in the data graph, or layered above . [Elementos adicionais aos vértices e arestas de um grafo de dados para formar um KG] [As dimensões contextuais podem ser identificadas na fase de Engenharia do KG após as etapas de sumar
Yu Liu, Wen Hua, Kexuan Xin, and Xiaofang Zhou. 2020. Context-Aware Temporal Knowledge Graph Embedding. In Web Information Systems Engineering – WISE 2019: 20th International Conference, Hong Kong, China, January 19–22, 2020, Proceedings. Springer-Verlag, Berlin, Heidelberg, 583–598. https://doi.org/10.1007/978-3-030-34223-4_37 Abstract Knowledge graph embedding (KGE) is an important technique used for knowledge graph completion (KGC). However, knowledge in practice is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of temporal knowledge graph embeddings. [Considerar o contexto temporal para a validade de relacionamentos] Currently, existing temporal KGE methods only focus on one aspect of facts, i.e., the factual plausibility, while ignoring the other aspect, i.e., the temporal consistency. Temporal consistency models the interactions between a fact and its contexts, and thus is able to capture fine-granularity te
Quan Wang, Pingping Huang, Haifeng Wang, Songtai Dai, Wenbin Jiang, Jing Liu, Yajuan Lyu, Yong Zhu, Hua Wu: CoKE: Contextualized Knowledge Graph Embedding . CoRR abs/1911.02168 (2019) Abstract Knowledge graph embedding, which projects symbolic entities and relations into continuous vector spaces, is gaining increasing attention. Previous methods allow a single static embedding for each entity or relation, ignoring their intrinsic contextual nature, i.e., entities and relations may appear in different graph contexts, and accordingly, exhibit different properties. [O contexto de uma entidade ou relacionamento depende do grafo onde ela aparece e isso afeta o embeddings gerado] This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings. Two types of graph contexts are studied: edges and paths, both formulated as sequences of entiti