@InProceedings{ziegler-EtAl:2017:I17-2,
  author    = {Ziegler, David  and  Abujabal, Abdalghani  and  Saha Roy, Rishiraj  and  Weikum, Gerhard},
  title     = {Efficiency-aware Answering of Compositional Questions using Answer Type Prediction},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {222--227},
  abstract  = {This paper investigates the problem of answering compositional factoid
	questions over knowledge bases (KB) under efficiency constraints. The method,
	called TIPI, (i) decomposes compositional questions, (ii) predicts answer types
	for individual sub-questions, (iii) reasons over the compatibility of joint
	types, and finally, (iv) formulates compositional SPARQL queries respecting
	type constraints. TIPI's answer type predictor is trained using distant
	supervision, and exploits lexical, syntactic and embedding-based features to
	compute context- and hierarchy-aware candidate answer types for an input
	question. Experiments on a recent benchmark show that TIPI results in
	state-of-the-art performance under the real-world assumption that only a single
	SPARQL query can be executed over the KB, and substantial reduction in the
	number of queries in the more general case.},
  url       = {http://www.aclweb.org/anthology/I17-2038}
}