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

By Mandar Joshi, Uma Sawant, Soumen Chakrabarti. 2014

43 citations recorded by the source corpus.

Much recent work focuses on formal interpretation 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 applied to open-domain, entity-oriented Web queries. First, Web queries are rarely wellformed questions. They are “telegraphic”, with missing verbs, prepositions, clauses, case and phrase clues. Second, the KG is always incomplete, unable to directly answer 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 relation type r, a target entity type t2, and contextual words s. The query seeks entity e2 2 t2 where r(e1,e2) holds, further 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, evidence in favor of candidate e2s are aggregated across several segmentations. Extensive experiments on the ClueWeb corpus and parts of Freebase as our KG, using over a thousand telegraphic queries adapted from TREC, INEX, and WebQuestions, show the efficacy of our approach. For one benchmark, MAP improves from 0.2‐0.29 (competitive baselines) to 0.42 (our system). NDCG@10 improves from 0.29‐0.36 to 0.54.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension (2017, background): “Other recent approaches attempt to combine structured high precision KBs with semi-structured information sources like OpenIE triples (Fader et al., 2014), HTML tables (Pasupat and Liang, 2015), and large (and noisy) corpora (Sawant and Chakrabarti, 2013; Joshi et al., 2014; Xu et al., 2015).”
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (2018, background): “• Telegraphic (Joshi et al., 2014): These are short questions formulated in an informal manner similar to keyword queries ( “First president India?””
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks (2017, methodology): “ence on the KB using text based features (Krishnamurthy and Mitchell, 2012; Reddy et al., 2014; Joshi et al., 2014; Yao and Van Durme, 2014; Yih et al., 2015; Neelakantan et al., 2015b; Guu et al., 2015; Xu et al., 2016b; Choi et al., 2015; Savenkov and Agichtein, 2016) or improves the inference on ”
Question Answering on Freebase via Relation Extraction and Textual Evidence (2016, background): “Our work is closely related to Joshi et al. (2014) who aim to answer noisy telegraphic queries using both structured and unstructured data.”
Scalable Semantic Parsing with Partial Ontologies (2015, background): “Joshi et al. (2014) describes an approach for mapping short search queries to a single Freebase relation, that beneﬁts from modeling schema incompleteness.”