Temporal Learning of Semantic Relations between Concepts Using Web Repository

In this paper, the study on generating temporal learning of relations between concepts is proposed. The purpose of the proposed study is to annotate a relation between concepts with semantic, temporal, concise, and structured information, which can release the cognitive burden of learning relations between concepts for users. The temporal annotations can help users to learn and understand the unfamiliar or new emerged relations between concepts. A general method is proposed to generate temporal learning of a relation between concepts by constructing its relation words, relation sentences, relation graph, and relation factor. Empirical experiments on movie star dataset show that the proposed algorithm is effective and accurate.