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A metadata search approach to natural language database query.

机译:用于自然语言数据库查询的元数据搜索方法。

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摘要

The research develops a new solution approach using a metadata search to solve the problem of truly natural language database query. While previous results either restrict the syntax of the query (ineffectiveness) or require semantics (inefficiency), this new approach supports any style of articulation, including grammatically incorrect and incomplete ones. It also efficiently determines the answer to the query, with a feedback loop to handle any exceptions. The new approach features a new class of reference dictionary integrating four types of enterprise metadata: enterprise information models, database values, user-words, and cases. The reference dictionary accommodates any possible interpretations of a natural language query concerning enterprise databases and promises to reduce the growth of user-words through enterprise information models. The branch-and-bound search method makes it possible and efficient to search all possible (machine) interpretations of a natural language query and to determine the optimal solution. The approach also provides a case-based learning and a case-based reasoning to assure successful closure to a query and to improve performance. The development of the metadata-search natural language database query (MS-NLDBQ) supports the approach. The results include (1) a new reference dictionary and its graphical representation of natural language queries based on the Metatdatabase model, (2) the core method, the branch-and-bound search method and query generation, to translate natural language queries to simple SQL queries, and (3) the software implementing the core method. Testing results of the software with queries on the computer-integrated manufacturing (CIM) database show that the system is capable of processing truly natural language text inputs, even in the form of a short essay, under certain conditions (necessary and sufficient conditions). The necessary condition is that the text input contains at least one recognized keyword (an entry found in the reference dictionary). The sufficient condition is that the text input contains a complete set of keywords from which, and only from which, a single SQL statement can be constructed to answer the query correctly. The response time and the growth of user-words recorded during the test are all efficient relative to usual SQL query processing. The testing results confirm the practicality of the metadata search approach and provide a good basis for extensions toward developing the capability to answer PL-SQL class queries and queries against non-relational databases. Text-based natural language query capability also promises to be amenable to verbal queries when coupled with voice recognition and synthesis techniques. Future research will include an exploration of the new approach to solve some non-database, traditional natural language interface problems in particular application domains.
机译:该研究开发了一种使用元数据搜索的新解决方案,以解决真正自然语言数据库查询的问题。尽管以前的结果限制了查询的语法(无效)或需要语义(无效),但是这种新方法支持任何表达方式,包括语法错误和不完整的表达方式。它还使用反馈循环来有效地确定查询的答案,以处理任何异常。新方法采用了新的参考词典类,其中集成了四种类型的企业元数据:企业信息模型,数据库值,用户单词和案例。参考词典可容纳有关企业数据库的自然语言查询的任何可能解释,并有望通过企业信息模型来减少用户单词的增长。分支和边界搜索方法使得搜索自然语言查询的所有可能(机器)解释并确定最佳解决方案成为可能和高效。该方法还提供了基于案例的学习和基于案例的推理,以确保成功关闭查询并提高性能。元数据搜索自然语言数据库查询(MS-NLDBQ)的开发支持该方法。结果包括(1)基于Metatdatabase模型的新参考词典及其对自然语言查询的图形表示;(2)将自然语言查询转换为简单方法的核心方法,分支和边界搜索方法以及查询生成SQL查询,以及(3)实现核心方法的软件。通过对计算机集成制造(CIM)数据库进行查询的软件测试结果表明,该系统能够在某些条件(必要条件和充分条件)下处理真正的自然语言文本输入,即使是短文形式。必要条件是文本输入至少包含一个可识别的关键字(在参考词典中找到的一项)。充分的条件是,文本输入包含一整套关键字,仅可以从中构造单个SQL语句来正确回答查询。相对于通常的SQL查询处理,响应时间和测试期间记录的用户单词的增长都是有效的。测试结果证实了元数据搜索方法的实用性,并为扩展其开发能力提供了良好的基础,从而能够回答PL-SQL类查询和针对非关系数据库的查询。与语音识别和合成技术结合使用时,基于文本的自然语言查询功能也有望适用于口头查询。未来的研究将包括探索新方法以解决特定应用领域中的某些非数据库传统自然语言接口问题。

著录项

  • 作者

    Boonjing, Veera.;

  • 作者单位

    Rensselaer Polytechnic Institute.;

  • 授予单位 Rensselaer Polytechnic Institute.;
  • 学科 Computer Science.; Operations Research.
  • 学位 Ph.D.
  • 年度 2002
  • 页码 300 p.
  • 总页数 300
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;运筹学;
  • 关键词

  • 入库时间 2022-08-17 11:46:20

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