The ability to extract new knowledge from large datasets is one of the most significant challenges facing society. The problem spans across domains from intelligence analysis and scientific research to basic web search. Current information extraction and retrieval tools either lack the flexibility to adapt to evolving information needs or require users to sift through search results and piece together relevant information. With so much data compounded by the criticality of finding relevant information, new tools and methods are needed to discover and relate relevant pieces of information in ever expanding repositories of data. We posit that user-driven relational models are needed to collectively learn and discover fine-grained entities and relations that are relevant to a user's information need. To meet this need, we present a ranked retrieval and extraction framework for collectively learning and integrating evidence of entities and relational dependencies to predict at query time, a ranking of sentences containing the most relevant entities and relational dependencies. By using a relational model, evidence can be leveraged across entity and relation instances. By performing joint inference at query time, NLP pipeline errors are minimized, and more adaptive and discriminative models that meet the specific knowledge discovery needs of the user can be developed. Our goal is to develop user-driven relational models of entities and their relational dependencies, and a search system based on these models that allow users to search for known entities and relations, discover new relations from known entities, and discover new entities from known relations. Preliminary qualitative and quantitative evaluations demonstrate the efficacy and potential of the proposed relational modeling approach.
The work in REF presents a user driven relational model and targets entity and relation extraction.
User-driven relational models for entity-relation search and extraction
JIWES '12
Computer Science