数据库的查询优化技术

数据库系统是管理信息系统的核心，从大多数系统的应用实例来看，查询操作在各种数据库操作中所占据的比重最大，而查询操作所基于的SELECT语句在SQL语句中又是代价最大的语句。由于SQL语言是面向结果而不是面向过程的查询语言，所以一般支持SQL语言的大型关系型数据库都需要使用一个基于成本的优化器，为即时查询提供一个最佳的执行策略。     

基于关键词的文档层次查询

文档查询是科学工作中的重要环节.从实现机理采看,文档查询是一种核心就是构建查询语句即设计查询界面及向数据库查询语句转化.提出了一种面向文档查询的查询树概念,将每个叶结点对应于一条SQL语句,而分支结点则表示子结点之间的并交差集合运算关系和其他运算关系,便于表达复杂文档查询要求.设计了查询树向SQL语句转化算法,将整个查询树合并为一条SQL语句,充分发挥DBMS 查询优化功能.     

基于多知识库和局部反馈的查询扩展研究

查询扩展是优化信息查询的一种重要手段。提出了一种基于多知识库和局部反馈的查询扩展方法,该方法首先融合了领域本体与同义词词林两种不同类型的知识库对查询关键词进行扩展,然后再用局部反馈方法对扩展结果进行筛选。实验表明,该方法可以有效提高查询性能。     

基于Ontology的政府信息检索技术

主要论述基于Ontology（本体）的政府知识库的信息检索技术。该技术有别于传统的全文检索技术,采用基于本体的技术来分析处理用户提交的查询请求,分析自然语言形式问题中的词法、语法、语义等信息,识别出问题的类别,得到一些关键词,并进行扩展。还可根据本体中领域词汇的关系对关键词进行扩展,并赋予不同的权值。然后将问题类别和带权值的关键词序列提交给系统的检索引擎进行后继的处理。     

应用RDF本体图扩充SPARQL查询

RDF是一种描述资源的语言。作为一种W3C组织推荐的描述标准,RDF已经被越来越多的个人和组织用于发布信息,促使RDF查询语言成为当前研究的热点。W3C提出的SPARQL RDF查询语言通过图形模式匹配实现对多个RDF图的查询,该方法简单、有效,但缺乏本体推理能力。因此,对于包含本体信息的RDF查询,必须显式地将本体信息用RDF图表示出来,并作为SPARQL查询中RDF数据集的一部分,以保证图形模式与查询要求之间的语义一致性。     

软件工程方法的新进展

面向Agent软件工程被视为是一种可有效支持复杂系统开发的新颖软件开发范型,面向Agent软件工程（AOSE,Agent-Oriented Software Engineering）将Agent作为基本的概念抽象和计算模型,并以此为基础提供了一系列思想、原则、方法、技术和工具来理解、规约、分析、设计、实现和部署软件系统。在介绍Agent相关理论的基础上,论述了软件工程方法的新进展——面向Agent软件工程研究的新进展。     

利用本体和主题词表的集成查询元数据

提出了通过集成现存的本体和主题词表构造元数据模式的一种新方法。这个集成基于主题词术语和本体概念之间的蕴含关系规范，并产生具体应用的元数据模式。同时给出了如何利用结果元数据模式进行元数据查询。在元数据查询中，利用术语关系的蕴含语义。提出了一种面向数据库的解决方法。     

基于本体的多Agent的自动协商模型研究

为解决电子商务中多Agent自动协商的难题,提出了基于本体的多Agent自动协商模型.首先介绍了该模型及其系统框架的结构,详细分析了多Agent自动协商过程,基于Agent自学习能力各协商状态能自动转换:提出了各类服务的本体描述,在语义层次上对各类服务活动进行描述、查询与自动匹配;为了支持多Agent之间的合作,综合考虑服务多属性,提出了一种多Agent间的柔性协商的方法,Agent通过对各类属性的协商,解决了供需双方间自动匹配问题.最后,从查全率、查准率两个指标验证了模型在判定供应商归属的有效性,通过对多属性的柔性协商验证了选定具体供应商的可行性.     

大数据环境下学术创新力自动测度知识库分析与构建

[目的/意义]构建大数据环境下学术创新力自动测度知识库,为学术创新力测度分析做支持。[方法/过程]以图书情报学相关期刊2008-2018年部分论文为数据源,使用本体建模的方法构建图书情报学知识本体,结合信息科学领域G3(科学、科学研究类)相关论文构建的本体,按照软件工程规范进行相关分析和设计,确定存储方案和存储结构,采用MySQL数据库来存储数据。[结果/结论]最终成功构建学术创新力测度知识库系统,并提供必要的知识库查询相关接口。[局限]由于时间关系和对图书情报学领域缺乏系统深入的了解,文章所构建的本体在属性关系方面进行了简化,导致实际使用效果会受一定影响。     

一种面向复杂多值属性的本体存储方法研究

随着本体种类和资源的增加,本体的结构越来越复杂,如何合理地存储各种结构类型的本体、支持高效的本体查询是一项很有意义且具有挑战性的课题.本文提出一种面向复杂多值属性的本体存储方法,采用与传统的本体分解存储模式不同的数据库模式分解方法,将单属性值资源与多属性值资源的存储分别处理来解决资源与属性值之间的复杂关系问题,并通过对资源和其属性值的统一存储来解决存储冗余问题.实验结果验证了该本体存储方法在获取复杂本体关系方面的优势,但同时也暴露出模式结构的易理解性与模式的查询效率之间的矛质,在此塞础上提出了修正存储模式的折中方案,实验表明查询资源的速度比原来方案基本快了一个数量级,但这是以冗余的多个表结构为代价的.     

Temporal and multi-versioned XML documents: A survey

Extensible Markup Language (XML) documents are associated with time in two ways: (1) XML documents evolve over time and (2) XML documents contain temporal information. The efficient management of the temporal and multi-versioned XML documents requires optimized use of storage and efficient processing of complex historical queries. This paper provides a comparative analysis of the various schemes available to efficiently store and query the temporal and multi-versioned XML documents based on temporal, change management, versioning, and querying support. Firstly, the paper studies the multi-versioning control schemes to detect, manage, and query change in dynamic XML documents. Secondly, it describes the storage structures used to efficiently store and retrieve XML documents. Thirdly, it provides a comparative analysis of the various commercial tools based on change management, versioning, collaborative editing, and validation support. Finally, the paper presents some future research and development directions for the multi-versioned XML documents.     

Learning from homologous queries and semantically related terms for query auto completion

Query auto completion (QAC) models recommend possible queries to web search users when they start typing a query prefix. Most of today’s QAC models rank candidate queries by popularity (i.e., frequency), and in doing so they tend to follow a strict query matching policy when counting the queries. That is, they ignore the contributions from so-called homologous queries, queries with the same terms but ordered differently or queries that expand the original query. Importantly, homologous queries often express a remarkably similar search intent. Moreover, today’s QAC approaches often ignore semantically related terms. We argue that users are prone to combine semantically related terms when generating queries.We propose a learning to rank-based QAC approach, where, for the first time, features derived from homologous queries and semantically related terms are introduced. In particular, we consider: (i) the observed and predicted popularity of homologous queries for a query candidate; and (ii) the semantic relatedness of pairs of terms inside a query and pairs of queries inside a session. We quantify the improvement of the proposed new features using two large-scale real-world query logs and show that the mean reciprocal rank and the success rate can be improved by up to 9% over state-of-the-art QAC models.     

Quantifying the impact of concept recognition on biomedical information retrieval

In ad hoc querying of document collections, current approaches to ranking primarily rely on identifying the documents that contain the query terms. Methods such as query expansion, based on thesaural information or automatic feedback, are used to add further terms, and can yield significant though usually small gains in effectiveness. Another approach to adding terms, which we investigate in this paper, is to use natural language technology to annotate - and thus disambiguate - key terms by the concept they represent. Using biomedical research documents, we quantify the potential benefits of tagging users’ targeted concepts in queries and documents in domain-specific information retrieval. Our experiments, based on the TREC Genomics track data, both on passage and full-text retrieval, found no evidence that automatic concept recognition in general is of significant value for this task. Moreover, the issues raised by these results suggest that it is difficult for such disambiguation to be effective.     

New query suggestion framework and algorithms: A case study for an educational search engine

Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as “comparison of queries”. We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66–90% statistically significant increase in relevance of query suggestions compared to a baseline method.     

基于黑板的多Agent系统的研究

结合黑板系统及Agent技术．设计了基于黑板的多Agent系统（MABS）模型，探讨了MABS的体系结构，定义了黑板和知识源地描述语言以及知识交换格式，以及Agent之间通讯语言，此外，提出了多知识源Agent协调决策的处理方法，有助于多个Agent协同求解大规模复杂问题。     

Ontology的自动构建方法

作为大规模的语义知识资源，Ontology在智能信息检索和其他智能化的信息资源管理中具有重要的作用。但是要使Ontology真正能够发挥作用，必须要使它具有一定的规模，而采用人工方法构建实用的Ontology需要花费漫长的时间和巨大的成本。本文探讨了一种Ontology的自动构建方法，该方法包括自然语言处理、知识获取、Ontology构建和知识库4个部分，并对每个部分的功能和应用的技术进行了具体介绍。该自动构建系统具有开放性和自我学习的特点，可以面向某领域进行领域Ontology的构建。     

Applying query structuring in cross-language retrieval

We will explore various ways to apply query structuring in cross-language information retrieval. In the first test, English queries were translated into Finnish using an electronic dictionary, and were run in a Finnish newspaper database of 55,000 articles. Queries were structured by combining the Finnish translation equivalents of the same English query key using the syn-operator of the InQuery retrieval system. Structured queries performed markedly better than unstructured queries. Second, the effects of compound-based structuring using a proximity operator for the translation equivalents of query language compound components were tested. The method was not useful in syn-based queries but resulted in decrease in retrieval effectiveness. Proper names are often non-identical spelling variants in different languages. This allows n-gram based translation of names not included in a dictionary. In the third test, a query structuring method where the Boolean and-operator was used to assign more weight to keys translated through n-gram matching gave good results.     

A microprocessor architecture for bibliographic retrieval system

In this paper the features of a microprocessor based architecture for bibliographic retrieval system are illustrated. The proposed system consists of the following three functional blocks: the “query processor”, the “simple query executers” and the “answer composer”. The query processor parses the queries and breaks the complex query into simple queries. Each simple query executer is able to perform the operations satisfying a simple query. Finally, the answer composer puts together the results of all simple query executers and produces the response to the query originally raised. This machine will allow the implementation of a very powerfull query language. The basic design goals are the system modularity and a whatever complex query's fulfilment. This is achieved through the proposed query language and by means of the system architecture allowing high parallelism in the performed operations.     

How doctors search: A study of query behaviour and the impact on search results

Professional, workplace searching is different from general searching, because it is typically limited to specific facets and targeted to a single answer. We have developed the semantic component (SC) model, which is a search feature that allows searchers to structure and specify the search to context-specific aspects of the main topic of the documents. We have tested the model in an interactive searching study with family doctors with the purpose to explore doctors’ querying behaviour, how they applied the means for specifying a search, and how these features contributed to the search outcome. In general, the doctors were capable of exploiting system features and search tactics during the searching. Most searchers produced well-structured queries that contained appropriate search facets. When searches failed it was not due to query structure or query length. Failures were mostly caused by the well-known vocabulary problem. The problem was exacerbated by using certain filters as Boolean filters. The best working queries were structured into 2–3 main facets out of 3–5 possible search facets, and expressed with terms reflecting the focal view of the search task. The findings at the same time support and extend previous results about query structure and exhaustivity showing the importance of selecting central search facets and express them from the perspective of search task. The SC model was applied in the highest performing queries except one. The findings suggest that the model might be a helpful feature to structure queries into central, appropriate facets, and in returning highly relevant documents.