Mining hypernym-hyponym relations from social tags via tag embedding

Abstract

With the rapid development of Internet of Thing, mobile Internet, cloud computing and other technologies, network data increases dramatically and Folksonomy plays an important role in web systems. How to obtain valuable knowledge, especially hypernym-hyponym relations, becomes a popular research topic in the field of artificial intelligence. For Folksonomy, hypernym-hyponym relation identification aims to recognize the"is-a" relation between two social tags. Most existing works about identifying hypernym-hyponym relations are based on statistical and heuristic approaches, but their performance still needs to be improved. In this paper, we propose a novel supervised learning approach to identify hypernym-hyponym relations from social tags using tag embeddings. First, we use a neural network model to learn tag embeddings. This model relies on not only the hypernym and hyponym tags, but also the contextual information between them. We then apply such embeddings as features to identify hypernym-hyponym relations using a supervised learning method. Our experimental results demonstrate that the proposed approach significantly outperforms other state-of-the-art approaches over a labeled dataset. The accuracy and F1-score of our approach achieve 0.91 and 0.86 respectively.