Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries

Much recent work focuses on formal in-terpretation of natural question utterances, with the goal of executing the resulting structured queries on knowledge graphs (KGs) such as Freebase. Here we address two limitations of this approach when ap-plied to open-domain, entity-oriented Web queries. First, Web queries are rarely well-formed questions. They are “telegraphic”, with missing verbs, prepositions, clauses, case and phrase clues. Second, the KG is always incomplete, unable to directly an-swer many queries. We propose a novel technique to segment a telegraphic query and assign a coarse-grained purpose to each segment: a base entity e1, a rela-tion type r, a target entity type t2, and contextual words s. The query seeks en-tity e2 ∈ t2 where r(e1, e2) holds, fur-ther evidenced by schema-agnostic words s. Query segmentation is integrated with the KG and an unstructured corpus where mentions of entities have been linked to the KG. We do not trust the best or any specific query segmentation. Instead, evi-dence in favor of candidate e2s are aggre-gated across several segmentations. Ex-tensive experiments on the ClueWeb cor-pus and parts of Freebase as our KG, us-ing over a thousand telegraphic queries adapted from TREC, INEX, and Web-Questions, show the efficacy of our ap-proach. For one benchmark, MAP im-proves from 0.2–0.29 (competitive base-lines) to 0.42 (our system).