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DBkWik: extracting and integrating knowledge from thousands of Wikis

DOI:10.1007/s10115-019-01415-5
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Sven Hertling
Sven Hertling
Sven Hertling
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Heiko Paulheim at Universität Mannheim
Heiko Paulheim
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Abstract and Figures

Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from Wikipedia, and therefore similar in coverage. In contrast, Wikifarms like Fandom contain Wikis for specific topics, which are often complementary to the information contained in Wikipedia, and thus DBpedia and YAGO. Extracting these Wikis with the DBpedia extraction framework is possible, but results in many isolated knowledge graphs. In this paper, we show how to create one consolidated knowledge graph, called DBkWik, from thousands of Wikis. We perform entity resolution and schema matching, and show that the resulting large-scale knowledge graph is complementary to DBpedia. Furthermore, we discuss the potential use of DBkWik as a benchmark for knowledge graph matching.
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Knowledge and Information Systems (2020) 62:2169–2190
https://doi.org/10.1007/s10115-019-01415-5
REGULAR PAPER
DBkWik: extracting and integrating knowledge from
thousands of Wikis
Sven Hertling1·Heiko Paulheim1
Received: 2 January 2019 / Revised: 3 October 2019 / Accepted: 5 October 2019 /
Published online: 2 November 2019
© Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract
Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from
Wikipedia, and therefore similar in coverage. In contrast, Wikifarms like Fandom contain
Wikis for specific topics, which are often complementary to the information contained in
Wikipedia, and thus DBpedia and YAGO. Extracting these Wikis with the DBpedia extrac-
tion framework is possible, but results in many isolated knowledge graphs. In this paper,
we show how to create one consolidated knowledge graph, called DBkWik, from thousands
of Wikis. We perform entity resolution and schema matching, and show that the resulting
large-scale knowledge graph is complementary to DBpedia. Furthermore, we discuss the
potential use of DBkWik as a benchmark for knowledge graph matching.
Keywords Knowledge graph creation ·Information extraction ·Linked open data ·
Knowledge graph matching
1 Introduction
General purpose knowledge graphs, such as DBpedia, YAGO, and Wikidata, have become
a central part of the linked open data cloud [49] and are among the most frequently used
datasets within the Web of data [8]. Such knowledge graphs contain information on millions
of entities from multiple topical domains [37].
Many of the popular knowledge graphs are created from Wikipedia and hence have a
similar coverage [47]. Generally speaking, each real-world entity for which a dedicated
Wikipedia page exists becomes an entity in the knowledge graph. This is a fundamental
restriction for many applications—for example, for building content-based recommender
systems backed by knowledge graphs, Di Noia et al. showed that the coverage of entities in
popular recommender system datasets in DBpedia is no more than 85% for movies, 63% for
music artists, and 31% for books [35].
BSven Hertling
sven@informatik.uni-mannheim.de
Heiko Paulheim
heiko@informatik.uni-mannheim.de
1Data and Web Science Group, University of Mannheim, Mannheim, Germany
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... For example, Figure 2.6 shows a snapshot from the Wikia infobox of the entity Zeus in Greek mythology and the Wiki markup table extracted from the dump file of the Wiki page 1 . In the case of unstructured texts, the lexical and dependency features are usually used to construct the semi-structured texts document-level pattern-based deep-learning based sentence-level [Soderland et al., 1995] [ Kim and Moldovan, 1995] [ Mooney, 1999] [Suchanek et al., 2007] [ Auer et al., 2007] [ Hertling and Paulheim, 2020] [ Hearst, 1992] [Huffman, 1995] [Carlson et al., 2010a] [ Nakashole et al., 2012] [ Riedel et al., 2013] [ Lin et al., 2015] [ Kambhatla, 2004] [ Zhou et al., 2005] [ Wang, 2008] [Gormley et al., 2015] [ Zhang et al., 2017b] [ Cui et al., 2018] [ BOOK> indicates the relation hasAuthor between a book and a person. The drawback of pattern-based methods is requiring intervention from human experts, hence costly and not scalable. ...
... Wikia is also constructed similarly to Wikipedia, with each universe is organized as a Wiki, so that it also contains pages of entities in the universe, infoboxes and category networks. With tremendous contribution from fans on creating the content, Wikia has become a great source for knowledge extraction in fictional domains [Hertling and Paulheim, 2020]. ...
... The DBkWik project [Hertling and Paulheim, 2020] has leveraged structured infoboxes of fan communities at wikia (now renamed to fandom.com), to construct a large KB of fictional characters and their salient properties. However, this is strictly limited to relations and respective instances that are present in infoboxes. ...
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... The task of the track is to match pairs of knowledge graphs, whose schema and instances have to be matched simultaneously. The individual knowledge graphs are created by running the DBpedia extraction framework on eight different Wikis from the Fandom Wiki hosting platform 25 in the course of the DBkWik project [35,34]. They cover different topics (movies, games, comics and books) and three Knowledge Graph clusters sharing the same domain e.g. ...
... They are measured in terms of macro precision and recall. The results of non-specific systems are not reported here, as we could observe in the last campaigns that they can have intermediate results in tests of type ii) (same ontologies task) and poor performance in tests i) (different ontologies task).The detailed results can be investigated on the page of multifarm track results 35 . In terms of runtime, the results are not comparable to those from last year as the systems have been run in a different environment in terms of memory and number of processors. ...
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... For the evaluation and analyses of the components of the framework developed we use the data extracted for the DB-kWik KG [15]. This data was made available for the OAEI 2020 Knowledge Graph track 7 in which the main task was developing systems capable of matching both the instances and the schema of KGs. ...
... The value was parsed by the Is date feature methodology and compared with the ground truth to evaluate the precision and recall. Table 4 compares our approach (dateutil + rules) with the Named-Entity Recognition (NER) modules of SpaCy 15 and Facebook's Duckling 16 . Our approach achieves significantly higher performance when identifying dates in our use case. ...
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Knowledge Graphs have emerged as a core technology to aggregate and publish knowledge on the Web. However, integrating knowledge from different sources, not specifically designed to be interoperable, is not a trivial task. Finding the right ontologies to model a dataset is a challenge since several valid data models exist and there is no clear agreement between them. In this paper, we propose to facilitate the selection of a data model with the RICDaM (Recommending Interoperable and Consistent Data Models) framework. RICDaM generates and ranks candidates that match entity types and properties in an input dataset. These candidates are obtained by aggregating freely available domain RDF datasets in a knowledge graph and then enriching the relationships between the graph’s entities. The entity type and object property candidates are obtained by exploiting the instances and structure of this knowledge graph to compute a score that considers both the accuracy and interoperability of the candidates. Datatype properties are predicted with a random forest model, trained on the knowledge graph properties and their values, so to make predictions on candidate properties and rank them according to different measures. We present experiments using multiple datasets from the library domain as a use case and show that our methodology can produce meaningful candidate data models, adaptable to specific scenarios and needs.
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... While this demonstration has been based on DBpedia, it can be transferred to other approaches as well. With the same mechanism, it would be possible to use the extraction code of other Wikipedia-based knowledge graphs, such as YAGO [9] or the Linked Hypernym extraction [5], as well as to transfer the approach to other Wikis [4]. Also other refinement operators which are local to a Wikipedia page, such as extracting relations from text [2], would be applicable. ...
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... In the course of time, DIEF was internationalized [5] and extended with support for over 140 languages; the DBpedia Ontology was created as an integration layer over all the Infobox template parameters; Wikidata and Wikimedia Commons were added to the extraction as well as the text of the articles. While there is always a residual of extraction errors, DIEF can be considered the state-of-the-art tool to squeeze the most and the best information from a huge variety of Wikimedia projects (and also other MediaWiki deployments [3]). Recently, DBpedia automated its extraction using the MARVIN bot that produces 22 billion triples per release using an agile workflow [4] and a variety of novel tests to ensure quality. ...
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