Joint Semantic Relevance Learning with Text Data and Graph Knowledge

Abstract

Inferring semantic relevance among entities (e.g., entries of Wikipedia) is important and challenging. According to the information resources, the inference can be categorized into learning with either raw text data, or labeled text data (e.g., wiki page), or graph knowledge (e.g, Word-Net). Although graph knowledge tends to be more reliable, text data is much less costly and offers a better coverage.