Temporal Faceted Learning of Concepts Using Web Search Engines

In this paper, we propose the problem of generating temporal faceted learning of concepts. The goal of the proposed problemisto annotate a concept with semantic, temporal, faceted, concise, andstructured information, which can release the cognitive burden of learning new concepts for users. The temporal faceted annotations can help users to learn and understand the unfamiliar or new emerged concepts. We propose a general method togenerate temporal faceted annotationof a concept by constructing its learning words, learning sentences, learning graph, and learning communities. Empirical experiments on LinkedIn dataset show that the proposed algorithm is effective and accurate. Different from the manually generated annotation repository such as LinkedIn and Wikipedia, the proposed method can automatically generated the annotations and does not need any prior knowledge such as ontology or the hierarchical knowledge base such as WordNet. The proposed method usesWeb search engines as a temporal faceted learning platform, which can add the new meaning and update the old meaning of concepts.