Wikipedia-centric Knowledge Bases (KBs) such as YAGO and DBpedia store the hyperlinks between articles in Wikipedia using wikilink relations. While wikilinks are signals of semantic connection between entities, the meaning of such connection is most of the times unknown to KBs, e.g., for 89% of wikilinks in DBpedia no other relation between the entities is known. The task of discovering the exact relations that hold between the endpoints of a wikilink is called wikilink semantification. In this paper, we apply rule mining techniques on the already semantified wikilinks to propose relations for the unsemantified wikilinks in a subset of DBpedia. By mining highly supported and confident logical rules from KBs, we can semantify wikilinks with very high precision.
- Training dataset extracted from DBpedia 3.8, consisting of 4 million triples of different relations about people, places and organizations, 8 million rdf:type statements and 10 million wikilinks about the entities participating in the relations.
- Semantification rules mined by AMIE on the training dataset. They have the form:
linksTo(x, y) ^ ... ^ rdf:type(x, C) ^ rdf:type(y, C') => r(x, y)
- Sample of wikilinks and evaluation of their semantification candidates, used to estimate the precision of the semantification for a set of 181K unsemantified wikilinks found in the training set.