Query Understanding via Entity Attribute Identification
Abstract
Understanding searchers' queries is an essential component of semantic search systems. In many cases, search queries involve specific attributes of an entity in a knowledge base (KB), which can be further used to find query answers. In this study, we aim to move forward the understanding of queries by identifying their related entity attributes from a knowledge base. To this end, we introduce the task of entity attribute identification and propose two methods to address it: (i) a model based on Markov Random Field, and (ii) a learning to rank model. We develop a human annotated test collection and show that our proposed methods can bring significant improvements over the baseline methods.
Cite
@article{arxiv.1809.08566,
title = {Query Understanding via Entity Attribute Identification},
author = {Arash Dargahi Nobari and Arian Askari and Faegheh Hasibi and Mahmood Neshati},
journal= {arXiv preprint arXiv:1809.08566},
year = {2018}
}
Comments
Proceedings of the 27th International Conference on Information and Knowledge Management (CIKM '18), 2018