Structured Query

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Xifeng Yan - One of the best experts on this subject based on the ideXlab platform.

  • DialSQL: Dialogue Based Structured Query Generation
    Proceedings of ACL, 2018
    Co-Authors: Izzeddin Gur, Semih Yavuz, Yu Su, Xifeng Yan
    Abstract:

    The recent advance in deep learning and semantic parsing has significantly im-proved the translation accuracy of natural language questions to Structured queries. However, further improvement of the ex-isting approaches turns out to be quite challenging. Rather than solely rely-ing on algorithmic innovations, in this work, we introduce DialSQL, a dialogue-based Structured Query generation frame-work that leverages human intelligence to boost the performance of existing algo-rithms via user interaction. DialSQL is capable of identifying potential errors in a generated SQL Query and asking users for validation via simple multi-choice ques-tions. User feedback is then leveraged to revise the Query. We design a generic sim-ulator to bootstrap synthetic training di-alogues and evaluate the performance of DialSQL on the WikiSQL dataset. Using SQLNet as a black box Query generation tool, DialSQL improves its performance from 61.3% to 69.0% using only 2.4 vali-dation questions per dialogue.

  • ACL (1) - DialSQL: Dialogue Based Structured Query Generation
    Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018
    Co-Authors: Izzeddin Gur, Semih Yavuz, Xifeng Yan
    Abstract:

    The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to Structured queries. However, further improvement of the existing approaches turns out to be quite challenging. Rather than solely relying on algorithmic innovations, in this work, we introduce DialSQL, a dialogue-based Structured Query generation framework that leverages human intelligence to boost the performance of existing algorithms via user interaction. DialSQL is capable of identifying potential errors in a generated SQL Query and asking users for validation via simple multi-choice questions. User feedback is then leveraged to revise the Query. We design a generic simulator to bootstrap synthetic training dialogues and evaluate the performance of DialSQL on the WikiSQL dataset. Using SQLNet as a black box Query generation tool, DialSQL improves its performance from 61.3% to 69.0% using only 2.4 validation questions per dialogue.

Izzeddin Gur - One of the best experts on this subject based on the ideXlab platform.

  • DialSQL: Dialogue Based Structured Query Generation
    Proceedings of ACL, 2018
    Co-Authors: Izzeddin Gur, Semih Yavuz, Yu Su, Xifeng Yan
    Abstract:

    The recent advance in deep learning and semantic parsing has significantly im-proved the translation accuracy of natural language questions to Structured queries. However, further improvement of the ex-isting approaches turns out to be quite challenging. Rather than solely rely-ing on algorithmic innovations, in this work, we introduce DialSQL, a dialogue-based Structured Query generation frame-work that leverages human intelligence to boost the performance of existing algo-rithms via user interaction. DialSQL is capable of identifying potential errors in a generated SQL Query and asking users for validation via simple multi-choice ques-tions. User feedback is then leveraged to revise the Query. We design a generic sim-ulator to bootstrap synthetic training di-alogues and evaluate the performance of DialSQL on the WikiSQL dataset. Using SQLNet as a black box Query generation tool, DialSQL improves its performance from 61.3% to 69.0% using only 2.4 vali-dation questions per dialogue.

  • ACL (1) - DialSQL: Dialogue Based Structured Query Generation
    Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018
    Co-Authors: Izzeddin Gur, Semih Yavuz, Xifeng Yan
    Abstract:

    The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to Structured queries. However, further improvement of the existing approaches turns out to be quite challenging. Rather than solely relying on algorithmic innovations, in this work, we introduce DialSQL, a dialogue-based Structured Query generation framework that leverages human intelligence to boost the performance of existing algorithms via user interaction. DialSQL is capable of identifying potential errors in a generated SQL Query and asking users for validation via simple multi-choice questions. User feedback is then leveraged to revise the Query. We design a generic simulator to bootstrap synthetic training dialogues and evaluate the performance of DialSQL on the WikiSQL dataset. Using SQLNet as a black box Query generation tool, DialSQL improves its performance from 61.3% to 69.0% using only 2.4 validation questions per dialogue.

Wooju Kim - One of the best experts on this subject based on the ideXlab platform.

  • KBQA: constructing Structured Query graph from keyword Query for semantic search
    Proceedings of the International Conference on Electronic Commerce, 2017
    Co-Authors: Heewon Jang, Seunghee Jin, Haemin Jung, Hyesoo Kong, Dokyung Lee, Dongkyu Jeon, Wooju Kim
    Abstract:

    It is often very difficult to locate information on the Web because of its large and rapidly increasing amount of data. One key reason for this is traditional keyword-based search engines focus only on the resources whose title or content exactly matches the Query keywords. People usually want to find the best matching resource itself to their Query, not the documents which contain the resource. Recently, one promising way to meet this kind of requirement must be ontology-based approach for semantic search. However, it is also obvious there is still non-negligible gap between average users and ontological approach. To overcome this limitation of ontological approach such as Semantic Web, it is essential to provide an efficient method to fill the gap while taking full advantage of semantic technologies. To this end, we devise a method to generate alternative SPARQL queries from the typical natural language based Query to the conventional search engines and evaluate the most matched SPARQL Query among the alternatives by considering the characteristics of the target knowledge bases. We then implement a prototype system to evaluate the proposed method and validate its empirical performance and accuracy.

D. Curtis Jamison - One of the best experts on this subject based on the ideXlab platform.

  • Current Protocols in Bioinformatics - Structured Query Language (SQL) fundamentals.
    Current protocols in bioinformatics, 2003
    Co-Authors: D. Curtis Jamison
    Abstract:

    Relational databases provide the most common platform for storing data. The Structured Query Language (SQL) is a powerful tool for interacting with relational database systems. SQL enables the user to concoct complex and powerful queries in a straightforward manner, allowing sophisticated data analysis using simple syntax and structure. This unit demonstrates how to use the MySQL package to build and interact with a relational database.

Grant E Weddell - One of the best experts on this subject based on the ideXlab platform.

  • expressive and flexible access to web extracted data a keyword based Structured Query language
    International Conference on Management of Data, 2010
    Co-Authors: Jeffrey Pound, Ihab F Ilyas, Grant E Weddell
    Abstract:

    Automated extraction of Structured data from Web sources often leads to large heterogeneous knowledge bases (KB), with data and schema items numbering in the hundreds of thousands or millions. Formulating information needs with conventional Structured Query languages is difficult due to the sheer size of schema information available to the user. We address this challenge by proposing a new Query language that blends keyword search with Structured Query processing over large information graphs with rich semantics. Our formalism for Structured queries based on keywords combines the flexibility of keyword search with the expressiveness of structures queries. We propose a solution to the resulting disambiguation problem caused by introducing keywords as primitives in a Structured Query language. We show how expressions in our proposed language can be rewritten using the vocabulary of the web-extracted KB, and how different possible rewritings can be ranked based on their syntactic relationship to the keywords in the Query as well as their semantic coherence in the underlying KB. An extensive experimental study demonstrates the efficiency and effectiveness of our approach. Additionally, we show how our Query language fits into QUICK, an end-to-end information system that integrates web-extracted data graphs with full-text search. In this system, the rewritten Query describes an arbitrary topic of interest for which corresponding entities, and documents relevant to the entities, are efficiently retrieved.

  • SIGMOD Conference - Expressive and flexible access to web-extracted data: a keyword-based Structured Query language
    Proceedings of the 2010 international conference on Management of data - SIGMOD '10, 2010
    Co-Authors: Jeffrey Pound, Ihab F Ilyas, Grant E Weddell
    Abstract:

    Automated extraction of Structured data from Web sources often leads to large heterogeneous knowledge bases (KB), with data and schema items numbering in the hundreds of thousands or millions. Formulating information needs with conventional Structured Query languages is difficult due to the sheer size of schema information available to the user. We address this challenge by proposing a new Query language that blends keyword search with Structured Query processing over large information graphs with rich semantics. Our formalism for Structured queries based on keywords combines the flexibility of keyword search with the expressiveness of structures queries. We propose a solution to the resulting disambiguation problem caused by introducing keywords as primitives in a Structured Query language. We show how expressions in our proposed language can be rewritten using the vocabulary of the web-extracted KB, and how different possible rewritings can be ranked based on their syntactic relationship to the keywords in the Query as well as their semantic coherence in the underlying KB. An extensive experimental study demonstrates the efficiency and effectiveness of our approach. Additionally, we show how our Query language fits into QUICK, an end-to-end information system that integrates web-extracted data graphs with full-text search. In this system, the rewritten Query describes an arbitrary topic of interest for which corresponding entities, and documents relevant to the entities, are efficiently retrieved.