IQ: The case for Iterative Querying for knowledge

Yosi Mass*, Maya Ramanath, Yehoshua Sagiv, Gerhard Weikum

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

17 Scopus citations

Abstract

Large knowledge bases, the Linked Data cloud, and Web 2.0 communities open up new opportunities for deep question answering to support the advanced information needs of knowledge workers like students, journalists, or business analysts. This calls for going beyond keyword search, towards more expressive ways of entity-relationship-oriented querying with graph constraints or even full-edged languages like SPARQL (over graph-structured, schema-less data). However, a neglected aspect of this active research direction is the need to support also query refinements, relaxations, and interactive exploration, as single-shot queries are often insufficient for the users' tasks. This paper addresses this issue by discussing the paradigm of Iterative Querying, IQ for short. We present two instantiations for IQ, one based on keyword search over labeled graphs combined with structural constraints, and another one based on extensions of the SPARQL language. We discuss the suitability of these approaches for knowledge-centric search tasks, and we identify open research problems that deserve greater attention.

Original languageEnglish
Pages38-44
Number of pages7
StatePublished - 2011
Event5th Biennial Conference on Innovative Data Systems Research, CIDR 2011 - Asilomar, CA, United States
Duration: 9 Jan 201112 Jan 2011

Conference

Conference5th Biennial Conference on Innovative Data Systems Research, CIDR 2011
Country/TerritoryUnited States
CityAsilomar, CA
Period9/01/1112/01/11

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