Knowledge Inquiry for Information Foraging

EasyChair Preprint 541

Abstract

Human analysts have a vital role in the task of sensemaking, the process of extracting information to reach conclusions and make decisions. Question-Answering (QA) is an existing natural language processing application that would appear to be relevant to the analyst’s task, given information needs to address in a structured knowledge source. Standard QA systems, however, assume an input question can be interpreted in isolation, meaning that there is a single translation of language to a structured query, and that there is a unique correct answer. We assume that a more appropriate tool for an analyst would support open-ended exploration for relevant information from structured data sources, and would not commit too early to a single interpretation of the analyst’s question. We provide the capability to pose natural language questions to knowledge graphs in RDF format where information that is relevant to the question can be visualized, making the knowledge source more transparent to the user. This paper presents InK, an inquiry system for knowledge graphs where the input is a NL natural language (NL) question and the output consists of knowledge assumed to be relevant to a general information need that motivates the question.

Keyphrases: Knowledge Graphs, Natural Language Processing, Question Answering

@booklet{EasyChair:541,
  author    = {Kyriaki Zafeiroudi and Rebecca Passonneau},
  title     = {Knowledge Inquiry for Information Foraging},
  howpublished = {EasyChair Preprint 541},
  year      = {EasyChair, 2018}}