A natural language interface for querying general and individual knowledge

Yael Amsterdamer, Anna Kukliansky, Tova Milo

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Many real-life scenarios require the joint analysis of general knowledge, which includes facts about the world, with individual knowledge, which relates to the opinions or habits of individuals. Recently developed crowd mining platforms, which were designed for such tasks, are a major step towards the solution. However, these platforms require users to specify their information needs in a formal, declarative language, which may be too complicated for naïve users. To make the joint analysis of general and individual knowledge accessible to the public, it is desirable to provide an interface that translates the user questions, posed in natural language (NL), into the formal query languages that crowd mining platforms support. While the translation of NL questions to queries over conventional databases has been studied in previous work, a setting with mixed individual and general knowledge raises unique challenges. In particular, to support the distinct query constructs associated with these two types of knowledge, the NL question must be partitioned and translated using different means; yet eventually all the translated parts should be seamlessly combined to a well-formed query. To account for these challenges, we design and implement a modular translation framework that employs new solutions along with state-of-the art NL parsing tools. The results of our experimental study, involving real user questions on various topics, demonstrate that our framework provides a high-quality translation for many questions that are not handled by previous translation tools.

Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
EditorsSimonas Saltenis, Christophe Claramunt, Ki-Joune Li
Pages1430-1441
Number of pages12
Volume8
Edition12
DOIs
StatePublished - 2015
Event3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of
Duration: 11 Sep 200611 Sep 2006

Publication series

NameProceedings of the VLDB Endowment
Number12
Volume8

Conference

Conference3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
Country/TerritoryKorea, Republic of
CitySeoul
Period11/09/0611/09/06

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science

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