OverlapLDA: A Generative Approach for LiteratureBased Discovery Literaturebased discovery, the process of choosing bridge terms to build plausible connections between xe2x80x9cunrelatedxe2x80x9d terms, helps the biomedical research by generating potentially useful hypotheses. The basic idea of previous work is conducting two or higher order association rules searching and ranking, either according to the semantics or the statistical measurements. However, the high order searching nature makes current approaches computationally complex. We address this problem by proposing a generative model assuming all the connection words as a rank of words. We further present an inference method for the generative model to learn the rank. Our model avoids the process of high order searching. Experiments show that our approach addresses the problem both effectively and efficiently.

CTGA: Graphbased Biomedical Literature Search Scientists are relying heavily on biomedical literature search (BLS) engines (e.g., PubMed) to acquire knowledge. Existing BLS systems adopt a xe2x80x9cCAxe2x80x9d paradigm that is to design querydocument similarity measurement based on wordsphrases in the unstructured Content and to develop search Algorithms. In this work, we argue that structures should be extracted and utilized to bridge the gap between text content and knowledgebased search. And graph is one of the most effective structured forms of knowledge and more informative than words or phrases. So we carry out a paradigm shift from xe2x80x9cCAxe2x80x9d to xe2x80x9cCTGAxe2x80x9d. Here xe2x80x9cTxe2x80x9d is for factual tuple of concepts and relations, and xe2x80x9cGxe2x80x9d is for knowledge graph. Our proposed graphbased BLS system has three parts: (1) it uses neural information extraction models to turn text into tuples; (2) it represents the tuples of a query or a document as a knowledge graph of linked concept nodes; (3) it has an efficient graphbased matching algorithm to return related documents at the level of structured knowledge. Experiments show that in both objective and subjective evaluation, our CTGA performs significantly better than traditional CAbased PubMed.