Results 41 to 50 of about 7,092,434 (358)
MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
Future Internet, 2023 Temporal knowledge graphs play an increasingly prominent role in scenarios such as social networks, finance, and smart cities. As such, research on temporal knowledge graphs continues to deepen.
Yong Yu +5 more
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A Dynamic Convolutional Network-Based Model for Knowledge Graph Completion
Information, 2022 Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion.
Haoliang Peng, Yue Wu
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Wikibase as an Infrastructure for Knowledge Graphs: The EU Knowledge Graph [PDF]
, 2021 Knowledge graphs are being deployed in many enterprises and institutions. An easy-to-use, well-designed infrastructure for such knowledge graphs is not obvious. After the success of Wikidata, many institutions are looking at the software infrastructure behind it, namely Wikibase.
Dennis Diefenbach +2 more
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Learning Entity and Relation Embeddings for Knowledge Graph Completion
AAAI Conference on Artificial Intelligence, 2015 Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings.
Yankai Lin +4 more
semanticscholar +1 more source
ACM SIGMOD Record, 2023 Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two ...
openaire +3 more sources
Knowledge Graph Embedding by Translating on Hyperplanes
AAAI Conference on Artificial Intelligence, 2014 We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance ...
Zhen Wang +3 more
semanticscholar +1 more source
Knowledge Graph Embeddings [PDF]
, 2012 With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al.
Rosso, Paolo +2 more
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KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion [PDF]
Conference on Empirical Methods in Natural Language ProcessingKnowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC.
Yanbin Wei +3 more
semanticscholar +1 more source
Journal of Big Data, 2023 The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions.
Bilal Abu-Salih +5 more
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Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020 Knowledge Graphs can be considered as fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. Stemming from scientific advancements in research areas of Semantic Web, Databases, Knowledge representation, NLP, Machine Learning, among others, Knowledge Graphs have rapidly gained ...
Claudio Gutierrez, Juan F. Sequeda
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