Kangqi Luo


2018

pdf
Knowledge Base Question Answering via Encoding of Complex Query Graphs
Kangqi Luo | Fengli Lin | Xusheng Luo | Kenny Zhu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question and the corresponding complex query structure. In this work, we encode such complex query structure into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. This approach consistently outperforms existing methods on complex questions while staying competitive on simple questions.

2015

pdf
Inferring Binary Relation Schemas for Open Information Extraction
Kangqi Luo | Xusheng Luo | Kenny Zhu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing