Results 51 to 60 of about 1,540,853 (195)
Triple2Vec: Learning Triple Embeddings from Knowledge Graphs [PDF]
arXiv, 2019 Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes in a (knowledge) graph. To the best of our knowledge, none of them has tackled the problem of embedding
arxiv
Aging and Cancer, EarlyView.ABSTRACT Purpose To assess the long‐term effects of a community cancer exercise program on quality of life, fatigue, weight, waist circumference, physical activity levels, lower extremity strength, body mass index (BMI), heart rate, and blood pressure, across non‐metastatic and metastatic patients.
Isaac Oppong, Roozbeh Naemi
wiley +1 more source
Open-World Knowledge Graph Completion
, 2017 Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace.
Shi, Baoxu, Weninger, Tim
core +1 more source
Predicting the Co-Evolution of Event and Knowledge Graphs [PDF]
arXiv, 2015 Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities.
arxiv
RDF Knowledge Graph Visualization From a Knowledge Extraction System
, 2015 In this paper, we present a system to visualize RDF knowledge graphs. These graphs are obtained from a knowledge extraction system designed by GEOLSemantics. This extraction is performed using natural language processing and trigger detection.
Curé, Olivier, Kerdjoudj, Fadhela
core
25 years development of knowledge graph theory: the results and the challenge [PDF]
, 2008 The project on knowledge graph theory was begun in 1982. At the initial stage, the goal was to use graphs to represent knowledge in the form of an expert system.
Hoede, Cornelis, Nurdiati, Sri
core +2 more sources
Graph Pattern Entity Ranking Model for Knowledge Graph Completion [PDF]
arXiv, 2019 Knowledge graphs have evolved rapidly in recent years and their usefulness has been demonstrated in many artificial intelligence tasks. However, knowledge graphs often have lots of missing facts. To solve this problem, many knowledge graph embedding models have been developed to populate knowledge graphs and these have shown outstanding performance ...
arxiv
Knowledge Graphs on the Web -- an Overview [PDF]
arXiv, 2020 Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowledge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes ...
arxiv
Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs [PDF]
arXiv, 2020 Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques.
arxiv
A review of artificial intelligence in brachytherapy
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen +4 more
wiley +1 more source