Extracting location and creator-related information from Wikipedia-based information-rich taxonomy for ConceptNet expansion

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• 作者：Marek Krawczyk ; ^{marek@ist.hokudai.ac.jp" class="auth_mail" title="E-mail the corresponding author} ; Rafal Rzepka ^{rzepka@ist.hokudai.ac.jp" class="auth_mail" title="E-mail the corresponding author} ; Kenji Araki ^{araki@ist.hokudai.ac.jp" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Common sense knowledge ; Knowledge extraction ; Conceptet
• 刊名：Knowledge-Based Systems
• 出版年：2016
• 出版时间：15 September 2016
• 年：2016
• 卷：108
• 期：Complete
• 页码：125-131
• 全文大小：818 K

文摘

Our research goal is to generate new assertions suitable for introduction to the Japanese part of the ConceptNet common sense knowledge ontology. In this paper we 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) and CreatedBy assertions (informing of the creator of an object) automatically from Japanese Wikipedia XML dump files. We use the Hyponymy extraction tool v1.0, which analyzes definition, category and hierarchy structures of Wikipedia articles to extract IsA assertions and produce an information-rich taxonomy. From this taxonomy we extract additional information, in this case AtLocation, LocatedNear and CreatedBy types of assertions, using our original method. The presented experiments prove that we achieved our research goal on a large scale: both methods produce satisfactory results, and we were able to acquire 5,866,680 IsA assertions with 96.0% reliability, 131,760 AtLocation assertion pairs with 93.5% reliability, 6217 LocatedNear assertion pairs with 98.5% reliability and 270,230 CreatedBy assertion pairs with 78.5% reliability. Our method surpassed the baseline system in terms of both precision and the number of acquired assertions.