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https://ceur-ws.org/Vol-2563/aics_1.pdf
Beyond DBpedia and YAGO
The New Kids on the Knowledge Graph Block
Heiko Paulheim1[0000−0003−4386−8195]
University of Mannheim, Germany
Data and Web Science Group
heiko@informatik.uni-mannheim.de
Abstract. Starting with Cyc in the 1980s [6], the collection of general
knowledge in machine interpretable form has been considered a valuable
ingredient in intelligent and knowledge intensive applications. Notable
contributions in the field include the Wikipedia-based datasets DBpe-
dia [5] and YAGO [10], as well as the collaborative knowledge base
Wikidata [11]. Since Google has coined the term in 2012, they are most
often referred to as knowledge graphs [1, 8]. Besides such open knowledge
graphs, many companies have started using corporate knowledge graphs
as a means of information representation [7].
In this talk, I will look at two ongoing projects related to the extraction
of knowledge graphs from Wikipedia and other Wikis. The first new
dataset, CaLiGraph 1 , aims at the generation of explicit formal definitions
from categories [2], and the extraction of new instances from list pages [9].
In its current release, CaLiGraph contains 200k axioms defining classes,
and more than 7M typed instances.
In the second part, I will look at the transfer of the DBpedia approach
to a multitude of arbitrary Wikis. The first such prototype, DBkWik 2 ,
extracts data from Fandom, a Wiki farm hosting more than 400k dif-
ferent Wikis on various topics. Unlike DBpedia, which relies on a larger
user base for crowdsourcing an explicit schema and extraction rules, and
the “one-page-per-entity” assumption, DBkWik has to address various
challenges in the fields of schema learning and data integration [3, 4]. In
its current release, DBkWik contains more than 11M entities, and has
been found to be highly complementary to DBpedia.
References
1. Ehrlinger, L., Wöß, W.: Towards a definition of knowledge graphs. In: SEMAN-
TiCS (2016)
2. Heist, N., Paulheim, H.: Uncovering the semantics of wikipedia categories. In:
International semantic web conference. pp. 219–236. Springer (2019)
3. Hertling, S., Paulheim, H.: Dbkwik: A consolidated knowledge graph from thou-
sands of wikis. In: 2018 IEEE International Conference on Big Knowledge (ICBK).
pp. 17–24. IEEE (2018)
1
http://caligraph.org/
2
http://dbkwik.org/
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2 Heiko Paulheim
4. Hofmann, A., Perchani, S., Portisch, J., Hertling, S., Paulheim, H.: Dbkwik: To-
wards knowledge graph creation from thousands of wikis. In: International Seman-
tic Web Conference (Posters, Demos & Industry Tracks) (2017)
5. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N.,
Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia – A Large-
scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web
Journal 6(2) (2013)
6. Lenat, D.B.: CYC: A large-scale investment in knowledge infrastructure. Commu-
nications of the ACM 38(11), 33–38 (1995)
7. Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., Taylor, J.: Industry-scale
knowledge graphs: Lessons and challenges. Communications of the ACM 62(8),
36–43 (2019). https://doi.org/10.1145/3331166
8. Paulheim, H.: Knowledge graph refinement: A survey of approaches and evaluation
methods. Semantic Web 8(3), 489–508 (2017)
9. Paulheim, H., Ponzetto, S.P.: Extending dbpedia with wikipedia list pages. NLP-
DBPEDIA@ ISWC 13 (2013)
10. Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: A Core of Semantic Knowledge
Unifying WordNet and Wikipedia. In: 16th international conference on World Wide
Web. pp. 697–706 (2007)
11. Vrandečić, D., Krötzsch, M.: Wikidata: a Free Collaborative Knowledge Base.
Communications of the ACM 57(10), 78–85 (2014)