ATOM: Construction of Antitumor Biomaterial Knowledge Graph by Biomedicine Literature With the rapid development of antitumor biomaterials, biomedicine literature with respect to antitumor biomaterials has been leveraged for tumor treatment as it provides abundant and useful information. A large number of biomedicine literature contains unstructured data, making it difficult for researchers to obtain desired messages from it. Knowledge Graphs (KGs) provides structured relationships among entities and can be served as a solution. However, no existing tool can be found in constructing an antitumor biomaterial knowledge graph from biomedicine literature. To fill this gap, a novel approach, ATOM, was proposed to construct an antitumor biomaterial knowledge graph from biomedicine literature through a series of process including the recognition of antitumor entities, the simplification of sentences, the extraction of triples, and the predicate mapping. Experiments demonstrated that ATOM is able to effectively express the extracted antitumor entities and their relationships.

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.