C2K: Acquiring Knowledge from Categories Using Semantic Associations

There are several RDF (Resource Description Framework) knowledge bases that store community-generated categories of entities and conceptual or factual information about entities. These two types of information may have strong associations; for example, entities categorized in People from Korea (categorial information) have a high probability of being a person (conceptual information) and being born in Korea (factual information). This kind of associations can be used for extracting new conceptual or factual information about entities. In this paper, we propose a prediction system that predicts new conceptual or factual information from categories of entities. First, the proposed system uses a novel association rule mining (ARM) approach that effectively mines rules encoding associations between categories of entities and conceptual or factual information about entities contained in existing RDF knowledge bases. Our extensive experiments show that our novel ARM approach outperforms the state-of-the-art ARM approach in terms of the prediction quality and coverage of these kind of associations. Second, the proposed system ranks and groups the mined rules based on their predictability by our novel semantic confidence measure calculated with a semantic resource such as WordNet. The experiments show that our novel confidence measure outperforms the standard confidence measure frequently used in the traditional ARM field in terms of discriminating the predictability of mined rules. Last, the proposed prediction system selects only rules of predictability from ranked and grouped rules, and then uses them to predict accurate new information from categories of entities. The experiments show that the results of the proposed prediction system are fairly comparable to that of the state-of-the-art prediction system in terms of the accuracy of prediction while overwhelming the coverage of prediction.