Relevance feedback revisited: dealing with content and structure in XML documents

Relevance feedback (RF) is a technique that allows to enrich an initial query according to the user feedback. The goal is to express more precisely the user’s needs. Some open issues arise when considering semi-structured documents like XML documents. They are mainly related to the form of XML documents which mix content and structure information and to the new granularity of information. Indeed, the main objective of XML retrieval is to select relevant elements in XML documents instead of whole documents. Most of the RF approaches proposed in XML retrieval are simple adaptation of traditional RF to the new granularity of information. They usually enrich queries by adding terms extracted from relevant elements instead of terms extracted from whole documents. In this article, we describe a new approach of RF that takes advantage of two sources of evidence: the content and the structure. We propose to use the query term proximity to select terms to be added to the initial query and to use generic structures to express structural constraints. Both sources of evidence are used in different combined forms. Experiments were carried out within the INEX evaluation campaign and results show the effectiveness of our approaches.     

RRSi: indexing XML data for proximity twig queries

Twig query pattern matching is a core operation in XML query processing. Indexing XML documents for twig query processing is of fundamental importance to supporting effective information retrieval. In practice, many XML documents on the web are heterogeneous and have their own formats; documents describing relevant information can possess different structures. Therefore some “user-interesting” documents having similar but non-exact structures against a user query are often missed out. In this paper, we propose the RRSi, a novel structural index designed for structure-based query lookup on heterogeneous sources of XML documents supporting proximate query answers. The index avoids the unnecessary processing of structurally irrelevant candidates that might show good content relevance. An optimized version of the index, oRRSi, is also developed to further reduce both space requirements and computational complexity. To our knowledge, these structural indexes are the first to support proximity twig queries on XML documents. The results of our preliminary experiments show that RRSi and oRRSi based query processing significantly outperform previously proposed techniques in XML repositories with structural heterogeneity.

XN-Store:一种原生XML数据库的存储方案

随着XML相关标准的推广与应用,Web上出现了大量的XML文档,为了进行有效的管理,有必要将XML文档存储到数据库中,存储方案已成为XML数据管理领域研究的一个重要课题,将XML文档映射为关系表,存储到传统的RDBMS中,会破坏XML数据的树形结构,造成查询效率的下降,提出了一种新的用于原生XML数据库的存储方案--XN-Store,该方案基于索引结构将XML节点作为记录直接存储到分页文件中,建立起持久化文档对象模型,从而保持了XML数据原有的树形结构.XN-Store不仅降低了XML文档的存储空间开销,而且实现了XML节点的快速串行化输出和访问操作.作为通用的原生XML存储方案,XN-Store支持各种二级索引的创建,以提高XML查询处理的效率,采用多种数据集,分别在XN-Store和先前的XML存储系统上进行实验,比较存储空间、存储时间、串行化时间和节点访问时间.实验结果表明,XN-Store是一种高性能的原生XML数据库存储方案.     

面向XML Repository搜索引擎的研究与实现

由于XML开发者可以随意定义自己的元素,就可能导致相同的元素表示不同的信息或相同的信息由不同的元素表示,这种现象使得人们交换XML文档相当困难。为了解决这一问题,许多团体组织开发了XMLRepository。目前主流的搜索机制并不适合XMLRepository,因此针对XMLRepository开发搜索引擎成为一个新的课题。本文通过分析XMLRepository的特点和主流搜索引擎的局限性,根据引入的“本体论”和“带有不完整信息的XML树”概念,为XML文档模式提出一种新的搜索引擎的模型XRDS,并通过实验验证。     

平衡I/O和CPU的XML关键词检索

随着XML在数据交换和数据存储中的普遍应用,基于XML文档的信息检索研究逐渐成为新的研究热点。XML文档本身含有的结构信息可以使其检索精度得到很大提高,但相应地,XML检索中使用的较复杂的评分模型(如组合语言模型和推理网络的结构化评分模型)和较细的返回结果粒度(由文档转变为元素或者段落),也使得传统的信息检索由I/O密集型应用转变为CPU密集型应用。针对上述应用特点的转变,提出了一种新的检索处理框架,即保存数据的两种索引形式,根据系统的状态动态地调整任务调度,平衡I/O和CPU的处理,以达到减少单个查询的平均响应时间的目的。     

Dynamically Updating XML Data: Numbering Scheme Revisited

Almost all existing approaches use certain numbering scheme to encode XML elements to facilitate query processing when XML data is stored in databases. For example, under the most popular region-based numbering scheme, the starting and ending positions of an element in a document are used as the code to identify the element so that the ancestor/descendant relationship between two elements can be determined by merely examining their codes. While such numbering scheme can greatly improve query performance, renumbering large amount of elements caused by updates becomes a performance bottleneck if XML documents are frequently updated. Unfortunately, no satisfactory work has been reported for efficient update of XML data. In this paper, we first formalize the XML data update problem by defining the basic operators to support most XML update queries. We then present a new numbering scheme that not only requires minimal code-length in comparison with existing numbering schema but also improves update performance when XML data is frequently updated at arbitrary positions. The fundamental difference between our new scheme and existing ones is that, instead of maintaining the explicit codes for elements, we only store the necessary information and generate the codes when they are needed in query processing. In addition to present the basic scheme, we also discuss some optimization techniques to further reduce the update cost. Results of a comprehensive performance study are provided to show the advantages of the new scheme.     

面向XML文档检索的索引技术

XML文档的查询索引是当前研究的热点.该文探讨XML文档的索引技术,包括索引结构的设计等问题,给出了一个高效的XML索引方法,采用独特的编码方法,对XML文档及其遵循的DTD同时建立索引,有效支持内容和结构的双重检索;该方法结合了区间编码、倒排表和路径索引的思想,利用DTD结构信息来提高查询的效率.实验结果表明,本文提出的方法可以有效地降低建立XML数据索引的代价,能够缩短查询的响应时间.     

SXBP：基于Pri—order编码的XML文档存储方法

随着XML技术的发展，如何利用现有的数据库技术存储和查询XML文档已成为XML数据管理领域研究的热点问题。本文介绍了一种新的文档编码方法，以及基于这种编码方式提出了一种新的XML文档存储方法。方法按照文档中结点类型将XML文档树型结构分解为结点，分别存储到对应的关系表中，这种方法能够将任意结构的文档存储到一个固定的关系模式中。同时为了便于实现数据的查询，将文档中出现的简单路径模式也存储为一个表。这种新的文档存储方法能够有效地支持文档的查询操作，并能根据结点的编码信息实现原XML文档的正确恢复。最后，对本文提出的存储方法和恢复算法进行了实验验证。     

基于贝叶斯网络的XML文档查询模型

目前XML查询语言及查询界面对Web用户过于复杂,该文描述了一种XML文档索引机制,在此基础上建立了一个通用的贝叶斯网络查询模型。用户只需在交互界面输入自然语言描述的查询,系统就能对其实现基于语义的构造,由它生成多个结构化查询;对这些查询建立贝叶斯网络,计算查询在给定文档下的概率,选择概率最大的前3个查询提交给系统执行。     

一种支持高效XML 路径查询的自适应结构索引

提出了一种新的自适应结构索引:AS-Index(adaptive structural index),能够克服现有静态索引和自适应索引的缺陷,具备高效的查询和调整性能.AS-Index 建立在F&B-Index 的基础之上,其索引结构包括F&B-Index,Query-Table 和Part-Table.Query-Table 能够记录频繁查询,避免了查询过程中的冗余操作.并且,在Query-Table 的基础上提出了自底向上的查询处理过程,能够充分利用现有的频繁查询高效地回答非频繁查询.Part-Table 用于优化包含祖先后裔边的查询,进一步提高了查询性能.现有的自适应结构索引的调整粒度是XML 元素节点,调整过程往往需要遍历整个文档.而AS-Index 是基于F&B-Index 节点的增量调整,其过程是局部的,高效的,并且能够支持复杂分支查询的调整.实验结果表明,AS-Index 在查询和调整性能上优于现有的XML 结构索引.同时,相比于现有的自适应结构索引,AS-Index 针对大规模文档具有更加优良的可扩展性.     

An adaptive path index for XML data using the query workload

Due to its flexibility, XML is becoming the de facto standard for exchanging and querying documents over the Web. Many XML query languages such as XQuery and XPath use label paths to traverse the irregularly structured XML data. Without a structural summary and efficient indexes, query processing can be quite inefficient due to an exhaustive traversal on XML data. To overcome the inefficiency, several path indexes have been proposed in the research community. Traditional indexes generally record all label paths from the root element in XML data and are constructed with the use of data only. Such path indexes may result in performance degradation due to large sizes and exhaustive navigations for partial matching path queries which start with the self-or-descendent axis(“//”). To improve the query performance, we propose an adaptive path index for XML data (termed APEX). APEX does not keep all paths starting from the root and utilizes frequently used paths on query workloads. APEX also has a nice property that it can be updated incrementally according to the changes of query workloads. Experimental results with synthetic and real-life data sets clearly confirm that APEX improves the query processing cost typically 2–69 times compared with the traditional indexes, with the performance gap increasing with the irregularity of XML data.     

Object-stack: An object-oriented approach for top-k keyword querying over fuzzy XML

Keyword search is the most popular technique of searching information from XML (eXtensible markup language) document. It enables users to easily access XML data without learning the structure query language or studying the complex data schemas. Existing traditional keyword query methods are mainly based on LCA (lowest common ancestor) semantics, in which the returned results match all keywords at the granularity of elements. In many practical applications, information is often uncertain and vague. As a result, how to identify useful information from fuzzy data is becoming an important research topic. In this paper, we focus on the issue of keyword querying on fuzzy XML data at the granularity of objects. By introducing the concept of “object tree”, we propose the query semantics for keyword query at object-level. We find the minimum whole matching result object trees which contain all keywords and the partial matching result object trees which contain partial keywords, and return the root nodes of these result object trees as query results. For effectively and accurately identifying the top-K answers with the highest scores, we propose a score mechanism with the consideration of tf*idf document relevance, users’ preference and possibilities of results. We propose a stack-based algorithm named object-stack to obtain the top-K answers with the highest scores. Experimental results show that the object-stack algorithm outperforms the traditional XML keyword query algorithms significantly, and it can get high quality of query results with high search efficiency on the fuzzy XML document.     

XML查询方法的设计与研究

提出了用Petri网建立XML语义结构模型的设计方法,根据XML的DTD结构建立描述XML语义结构的Petri网模型,DTD结构中的元素声明和属性声明对应Petri网模型中的一个或一组变迁,声明中的元素或属性对应Petri 网模型中的库所,把XML查询中的路径表达式定义为Petri网库所中的Token。根据Petri网模型的结构生成存储XML数据的关系数据库模型,将XML的查询问题最终转化为数据库中数据的查询操作。 XML;Petri网;路径表达式;查询     

一种新的XML文档的存储平台SDML的实现技术

目前,XML文档数据库(NXD—Native XML DBMS)的设计和存储正受到越来越多的关注,这是由于它可以灵活地表示各种数据,尤其是那些关系模式无法表达的复杂的数据。已经有一些NXD产品出现。而对XML文档的存储的好坏直接影响到它的查询效率,基于此我们自主提出了一种高效的XML文档存储平台SDML。详细讨论了它的存储结构和实现细节。特别提出了如何解决具有大量结构相同元素的存储方法,并给出了在其上进行查询、插入、删除和索引维护等操作的解决方案。给出了这种结构I／O费用代价,并进行了相关的实现,为NXD的存储优化提供一种新的途径。     

Topic-centric and semantic-aware retrieval system for internet of things

The Internet of things (IoT) has been considered as one of the promising paradigms that can allow people and objects to seamlessly interact. So far, numerous applications and services have been proposed, such as retrieval service. The retrieval, however, faces a big challenge in IoT because the data belongs to different domains and user interaction with the surrounding environment is constrained. This paper proposes Acrost, a retrieval system based on topic discovery and semantic awareness in IoT environment. The initial contents with interesting information is obtained through the combination of two topic centric collectors. The metadata is extracted by aggregating regular expression-based and conditional random fields-based approaches. Moreover, the semantic-aware retrieval is achieved by parsing the query and ranking the relevance of contents. In addition, we present a case study on academic conference retrieval to validate the proposed approaches. Experimental results show that the proposed system can significantly improve the response time and efficiency of topic self-adaptive retrieval manner.     

基于语义分析的查询扩展方法

查询扩展是优化信息检索的有效途径。为此,提出一种基于语义分析的查询扩展方法,利用基于互信息的共现模型分析初检文档,并将其作为部分扩展源,用模型的统计结果剪枝由语义词典WordNet生成的语义树,限制扩展范围。从初检文档和语义词典两方面选取扩展词对原查询进行扩展形成新的查询集。对返回结果进行重排序,调整前n篇文档的查准率。实验证明该方法是切实可行的。     

Learning element similarity matrix for semi-structured document analysis

Capturing latent structural and semantic properties in semi-structured documents (e.g., XML documents) is crucial for improving the performance of related document analysis tasks. Structured Link Vector Mode (SLVM) is a representation recently proposed for modeling semi-structured documents. It uses an element similarity matrix to capture the latent relationships between XML elements—the constructing components of an XML document. In this paper, instead of applying heuristics to define the element similarity matrix, we propose to compute the matrix using the machine learning approach. In addition, we incorporate term semantics into SLVM using latent semantic indexing to enhance the model accuracy, with the element similarity learnability property preserved. For performance evaluation, we applied the similarity learning to k-nearest neighbors search and similarity-based clustering, and tested the performance using two different XML document collections. The SLVM obtained via learning was found to outperform significantly the conventional Vector Space Model and the edit-distance-based methods. Also, the similarity matrix, obtained as a by-product, can provide higher-level knowledge on the semantic relationships between the XML elements.

Using semantic components to search for domain-specific documents: An evaluation from the system perspective and the user perspective

We seek to leverage an expert user's knowledge about how information is organized in a domain and how information is presented in typical documents within a particular domain-specific collection, to effectively and efficiently meet the expert's targeted information needs. We have developed the semantic components model to describe important semantic content within documents. The semantic components model for a given collection (based on a general understanding of the type of information needs expected) consists of a set of document classes, where each class has an associated set of semantic components. Each semantic component instance consists of segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. This paper describes how the semantic components model can be used to improve an information retrieval system. We present experimental evidence from a large interactive searching study that compared the use of semantic components in a system with full text and keyword indexing, where we extended the query language to allow users to search using semantic components, to a base system that did not have semantic components. We evaluate the systems from a system perspective, where semantic components were shown to improve document ranking for precision-oriented searches, and from a user perspective. We also evaluate the systems from a session-based perspective, evaluating not only the results of individual queries but also the results of multiple queries during a single interactive query session.     

XML 关键词检索的查询理解

与纯文本文档集相比,使用语义标签标注的半结构化的XML文档集,有助于信息检索系统更好地理解待检索文档.同样,结构化查询,比如SQL,XQuery和Xpath,相对于纯关键词查询更加清晰地表达了用户的查询意图.这二者都能够帮助信息检索系统获得更好的检索精度.但关键词查询因其简单和易用性,仍被广泛使用.提出了XNodeRelation算法,以自动推断关键词查询的结构化信息(条件/目标节点类型).与已有的推断算法相比,综合了XML文档集的模式和统计信息以及查询关键词出现的上下文及其关联关系等推断用户的查询意图.大量的实验验证了该算法的有效性.