USI Answers: Natural Language Question Answering Over (Semi-) Structured Industry Data
Abstract:
The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system’s interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.
Title: USI Answers: Natural Language Question Answering Over (Semi-) Structured Industry Data
Authors: Ulli Waltinger, Dan Tecuci, Mihaela Olteanu, Vlad Mocanu, Sean Sullivan
Pub/Conf: Proceedings of the Twenty-Fifth Innovative Applications of Artificial Intelligence Conference, IAAI 2013, July 14-18, 2013, Bellevue, Washington, USA
BibTeX:
@inproceedings{DBLP:conf/iaai/WaltingerTOMS13,
author = {Ulli Waltinger and
Dan Tecuci and
Mihaela Olteanu and
Vlad Mocanu and
Sean Sullivan},
title = {USI Answers: Natural Language Question Answering Over (Semi-)
Structured Industry Data},
booktitle = {Proceedings of the Twenty-Fifth Innovative Applications
of Artificial Intelligence Conference, IAAI 2013, July 14-18,
2013, Bellevue, Washington, USA},
year = {2013},
editor = {Hector Mu{\~n}oz-Avila and
David J. Stracuzzi},
ee = {http://www.aaai.org/ocs/index.php/IAAI/IAAI13/paper/view/6241},
}
