Exploiting Wikipedia-Based Information-Rich Taxonomy for Extracting Location, Creator and Membership Related Information for Conceptnet Expansion
Authors | Marek Krawczyk Rafal Rzepka Kenji Araki |
|---|---|
Publication date | 2015 |
DOI | 10.1007/978-3-319-93782-3_19 |
Links | Original |
Exploiting Wikipedia-Based Information-Rich Taxonomy for Extracting Location, Creator and Membership Related Information for Conceptnet Expansion - scientific work related to Wikipedia quality published in 2015, written by Marek Krawczyk, Rafal Rzepka and Kenji Araki.
Overview
In this paper authors present a method for extracting IsA assertions (hyponymy relations), AtLocation assertions (informing of the location of an object or place), LocatedNear assertions (informing of neighboring locations), CreatedBy assertions (informing of the creator of an object) and MemberOf assertions (informing of group membership) automatically from Japanese Wikipedia XML dump files. Authors use the Hyponymy extraction tool v1.0, which analyses definition, category and hierarchy structures of Wikipedia articles to extract IsA assertions and produce information-rich taxonomy. From this taxonomy authors extract additional information, in this case AtLocation, LocatedNear, CreatedBy and MemberOf types of assertions, using original method. The presented experiments prove that both methods produce satisfactory results: authors were able to acquire 5,866,680 IsA assertions with 96.0% reliability, 131,760 AtLocation assertion pairs with 93.5% reliability, 6,217 LocatedNear assertion pairs with 98.5% reliability, 270,230 CreatedBy assertion pairs with 78.5% reliability and 21,053 MemberOf assertions with 87.0% reliability. Authors method surpassed the baseline system in terms of both precision and the number of acquired assertions.
