Learning Joint Query Interpretation and Response Ranking

TL;DR

This paper introduces joint models for interpreting free-form queries and ranking responses by leveraging structured annotations and unstructured text, improving entity search accuracy without requiring complete knowledge base schemas.

Contribution

It proposes two novel formulations that jointly interpret queries and rank responses, exploiting bidirectional information flow between unstructured text and structured knowledge.

Findings

Bridges the accuracy gap in typed entity search.

Outperforms two-stage query type prediction and search methods.

Utilizes probabilistic and max-margin models for joint interpretation and ranking.

Abstract

Thanks to information extraction and semantic Web efforts, search on unstructured text is increasingly refined using semantic annotations and structured knowledge bases. However, most users cannot become familiar with the schema of knowledge bases and ask structured queries. Interpreting free-format queries into a more structured representation is of much current interest. The dominant paradigm is to segment or partition query tokens by purpose (references to types, entities, attribute names, attribute values, relations) and then launch the interpreted query on structured knowledge bases. Given that structured knowledge extraction is never complete, here we use a data representation that retains the unstructured text corpus, along with structured annotations (mentions of entities and relationships) on it. We propose two new, natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus.One, inspired by probabilistic language models, computes expected response scores over the uncertainties of query interpretation. The other is based on max-margin discriminative learning, with latent variables representing those uncertainties. In the context of typed entity search, both formulations bridge a considerable part of the accuracy gap between a generic query that does not constrain the type at all, and the upper bound where the "perfect" target entity type of each query is provided by humans. Our formulations are also superior to a two-stage approach of first choosing a target type using recent query type prediction techniques, and then launching a type-restricted entity search query.