FTS: Faceted Taxonomy Construction and Search for Scientific Publications

Scalable keyword-based information retrieval has dominated the search industry for decades. When performing a sophisticated intelligence search and analysis task, a user is challenged to pose a right query, read multiple retrieved articles, understand their major contents, discover more relevant terms, and iterate. This process is often ad hoc and in many cases, very challenging especially when researchers start to explore a field they are not familiar with. For tasks like summarizing research efforts in one area, an analyst needs to interact with a keyword-based search engine for a long time before a reasonable, comprehensive technical report can be written. In this work, we developed a network-based, unified search and navigation platform, called FTS (Faceted Taxonomy Construction and Search), to ease query development and facilitate intelligence exploration in a large text repository, focused on scientific publications. It leverages the newest phrase mining, concept embedding and deep learning techniques to automatically extract concept terms and link them in a taxonomy structure, which could facilitate many interesting downstream applications including summarization, trend analysis, document categorization and recommendation.