TY - CHAP
T1 - A natural language interface for querying general and individual knowledge
AU - Amsterdamer, Yael
AU - Kukliansky, Anna
AU - Milo, Tova
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84953847630&partnerID=8YFLogxK
U2 - https://doi.org/10.14778/2824032.2824042
DO - https://doi.org/10.14778/2824032.2824042
M3 - فصل
VL - 8
T3 - Proceedings of the VLDB Endowment
SP - 1430
EP - 1441
BT - Proceedings of the VLDB Endowment
A2 - Saltenis, Simonas
A2 - Claramunt, Christophe
A2 - Li, Ki-Joune
T2 - 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
Y2 - 11 September 2006 through 11 September 2006
ER -