Knowledge graphs: Venturing out into the wild

Gerard De Melo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

While we now have vast collections of knowledge at our disposal, it appears that our systems often need further kinds of knowledge that are still missing in most knowledge graphs. This paper argues that we need keep moving further beyond simple collections of encyclopedic facts. Three key directions are (1) aiming at more tightly integrated knowledge, (2) distilling knowledge from text and other unstructured data, and (3) moving towards cognitive and neural approaches to better exploit the available knowledge in intelligent applications.

Original languageAmerican English
Title of host publicationKnowledge Graphs and Language Technology - ISWC 2016 International Workshops
Subtitle of host publicationKEKI and NLP and DBpedia, Revised Selected Papers
EditorsHeiko Paulheim, Sebastian Hellmann, John P. McCrae, Christian Chiarcos, Pablo Mendes, Hideaki Takeda, Key-Sun Choi, Jorge Gracia, Yoshihiko Hayashi, Seiji Koide, Marieke van Erp
PublisherSpringer Verlag
Pages1-9
Number of pages9
ISBN (Print)9783319687223
DOIs
StatePublished - 2017
Event15th International Semantic Web Conference, ISWC 2016 held in conjuction with the 1st Workshop on Knowledge Extraction and Knowledge Integration, KEKI 2016 and 4th NLP and DBpedia Workshop, NLP-DBpedia 2016 - Kobe, Japan
Duration: Oct 17 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10579 LNCS

Other

Other15th International Semantic Web Conference, ISWC 2016 held in conjuction with the 1st Workshop on Knowledge Extraction and Knowledge Integration, KEKI 2016 and 4th NLP and DBpedia Workshop, NLP-DBpedia 2016
Country/TerritoryJapan
CityKobe
Period10/17/1610/21/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Information extraction
  • Knowledge graphs
  • Neural methods

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