Knowledge Graphs
Copyright policy )Knowledge Graphs
In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.
- University of Milan Italy
- University of Milano-Bicocca Italy
- University of Southampton United Kingdom
- Vrije Universiteit Brussel Belgium
- Sapienza University of Rome Italy
ddc:004, FOS: Computer and information sciences, Computer Science - Machine Learning, 505002 Data protection, Computer Science - Artificial Intelligence, Ontologie, 102001 Artificial Intelligence, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), 502050 Wirtschaftsinformatik, semantic web, Computer Science - Databases, 102001 Artificial intelligence, Knowledge graphs, graph databases, graph query languages, shapes, ontologies, graph algorithms, embeddings, graph neural networks, rule mining, 505002 Datenschutz, Graph query language, [INFO]Computer Science [cs], Knowledge graphs; graph databases; graph query languages; shapes; ontologies; graph algorithms; embeddings; graph neural networks; rule mining, 102015 Information systems, 102015 Informationssysteme, Knowledge graph, 020, Computer Sciences, Institut für Informatik und Computational Science, Graph algorithm, Databases (cs.DB), Rule mining, artificial intelligence, 502050 Business informatics, Graph neural network, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Graph database, 102, 004, Embeddings; Graph algorithms; Graph databases; Graph neural networks; Graph query languages; Knowledge graphs; Ontologies; Rule mining; Shapes;, knowledge graphs, machine learning, Datavetenskap (datalogi), Artificial Intelligence (cs.AI), Embeddings; Graph algorithms; Graph databases; Graph neural networks; Graph query languages; Knowledge graphs; Ontologies; Rule mining; Shapes, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Shapes, Embedding
ddc:004, FOS: Computer and information sciences, Computer Science - Machine Learning, 505002 Data protection, Computer Science - Artificial Intelligence, Ontologie, 102001 Artificial Intelligence, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), 502050 Wirtschaftsinformatik, semantic web, Computer Science - Databases, 102001 Artificial intelligence, Knowledge graphs, graph databases, graph query languages, shapes, ontologies, graph algorithms, embeddings, graph neural networks, rule mining, 505002 Datenschutz, Graph query language, [INFO]Computer Science [cs], Knowledge graphs; graph databases; graph query languages; shapes; ontologies; graph algorithms; embeddings; graph neural networks; rule mining, 102015 Information systems, 102015 Informationssysteme, Knowledge graph, 020, Computer Sciences, Institut für Informatik und Computational Science, Graph algorithm, Databases (cs.DB), Rule mining, artificial intelligence, 502050 Business informatics, Graph neural network, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Graph database, 102, 004, Embeddings; Graph algorithms; Graph databases; Graph neural networks; Graph query languages; Knowledge graphs; Ontologies; Rule mining; Shapes;, knowledge graphs, machine learning, Datavetenskap (datalogi), Artificial Intelligence (cs.AI), Embeddings; Graph algorithms; Graph databases; Graph neural networks; Graph query languages; Knowledge graphs; Ontologies; Rule mining; Shapes, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Shapes, Embedding
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