NL2CM: A natural language interface to crowd mining

Yael Amsterdamer, Anna Kukliansky, Tova Milo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations


The joint processing of general data, which can refer to objective data such as geographical locations, with individual data, which is related to the habits and opinions of individuals, is required in many real-life scenarios. For this purpose, crowd mining platforms combine searching knowledge bases for general data, with mining the crowd for individual, unrecorded data. Existing such platforms require queries to be stated in a formal language. To bridge the gap between naïve users, who are not familiar with formal query languages, and crowd mining platforms, we develop NL2CM, a prototype system which translates natural language (NL) questions into well-formed crowd mining queries. The mix of general and individual information needs raises unique challenges. In particular, the different types of needs must be identified and translated into separate query parts. To account for these challenges, we develop new, dedicated modules and embed them within the modular and easily extensible architecture of NL2CM. Some of the modules interact with the user during the translation process to resolve uncertainties and complete missing data. We demonstrate NL2CM by translating questions of the audience, in different domains, into OASSIS-QL, a crowd mining query language which is based on SPARQL.

Original languageEnglish
Title of host publicationSIGMOD 2015 - Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450327589
StatePublished - 27 May 2015
Externally publishedYes
EventACM SIGMOD International Conference on Management of Data, SIGMOD 2015 - Melbourne, Australia
Duration: 31 May 20154 Jun 2015

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


ConferenceACM SIGMOD International Conference on Management of Data, SIGMOD 2015

Bibliographical note

Publisher Copyright:
Copyright © 2015 ACM.


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