%0 Generic %1 hogan2020knowledge %A Hogan, Aidan %A Blomqvist, Eva %A Cochez, Michael %A d'Amato, Claudia %A de Melo, Gerard %A Gutierrez, Claudio %A Gayo, José Emilio Labra %A Kirrane, Sabrina %A Neumaier, Sebastian %A Polleres, Axel %A Navigli, Roberto %A Ngomo, Axel-Cyrille Ngonga %A Rashid, Sabbir M. %A Rula, Anisa %A Schmelzeisen, Lukas %A Sequeda, Juan %A Staab, Steffen %A Zimmermann, Antoine %D 2020 %K dfg-antrag-steckbriefe knowledgegraph ontologies %T Knowledge Graphs %U http://arxiv.org/abs/2003.02320 %X In this paper 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 and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

@misc{hogan2020knowledge, abstract = {In this paper 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 and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.}, added-at = {2021-04-13T09:43:01.000+0200}, author = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d'Amato, Claudia and de Melo, Gerard and Gutierrez, Claudio and Gayo, José Emilio Labra and Kirrane, Sabrina and Neumaier, Sebastian and Polleres, Axel and Navigli, Roberto and Ngomo, Axel-Cyrille Ngonga and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine}, biburl = {https://www.bibsonomy.org/bibtex/252967dc1ab4d66d3cc35a688450a1f69/albinzehe}, description = {Knowledge Graphs}, interhash = {937ebb9057ebdd6fa997a18741b79755}, intrahash = {52967dc1ab4d66d3cc35a688450a1f69}, keywords = {dfg-antrag-steckbriefe knowledgegraph ontologies}, note = {cite arxiv:2003.02320Comment: Revision from v4: Correcting minor typos and errata involving entailment discussion (former/latter), figure for query rewriting (swap city/venue for location), add more brittle nodes in connectivity; etc}, timestamp = {2021-04-13T09:43:01.000+0200}, title = {Knowledge Graphs}, url = {http://arxiv.org/abs/2003.02320}, year = 2020 }