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 ...
The evolution of the Web of documents into a Web of services and data has resulted in an increased a...
The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes i...
The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes i...
Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from Wikipedia, and there...
Popular knowledge graphs such as DBpedia and YAGO are built from Wikipedia, and therefore similar in...
Large knowledge graphs like DBpedia and YAGO are always based on the same source - namely Wikipedia....
Knowledge graphs serve as the primary sources of structured data in many Semantic Web applications. ...
When it comes to factual knowledge about a wide range of domains, Wikipedia is often the prime sourc...
Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extrac...
Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extrac...
Knowledge about entities and their interrelations is a crucial factor of success for tasks like ques...
International audienceKnowledge graphs are being deployed in many enterprises and institutions. An e...
World knowledge may be available in different forms such as relational databases, triple stores, lin...
The internet is constantly expanding across millions of web pages. Using the internet effectively is...
The evolution of the Web of documents into a Web of services and data has resulted in an increased a...
The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes i...
The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes i...
Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from Wikipedia, and there...
Popular knowledge graphs such as DBpedia and YAGO are built from Wikipedia, and therefore similar in...
Large knowledge graphs like DBpedia and YAGO are always based on the same source - namely Wikipedia....
Knowledge graphs serve as the primary sources of structured data in many Semantic Web applications. ...
When it comes to factual knowledge about a wide range of domains, Wikipedia is often the prime sourc...
Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extrac...
Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extrac...
Knowledge about entities and their interrelations is a crucial factor of success for tasks like ques...
International audienceKnowledge graphs are being deployed in many enterprises and institutions. An e...
World knowledge may be available in different forms such as relational databases, triple stores, lin...
The internet is constantly expanding across millions of web pages. Using the internet effectively is...
The evolution of the Web of documents into a Web of services and data has resulted in an increased a...
The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes i...
The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes i...