集中式P2P环境下有效的Top—K查询

目前大多数的Peer-to-Peer(P2P)系统只支持基于文件标识的搜索,用户不能根据文件的内容进行搜索.Top-k查询被广泛地应用于搜索引擎中,获得了巨大的成功.可是,由于P2P系统是一个动态的、分散的系统,在P2P环境下进行top-k查询是具有挑战性的.提出了一种在集中式P2P系统中的基于中心文档的层次化的top-k查询算法.首先,采用层次化的方法实现分布式的top-k查询,将结果的合并和排序分散到P2P网络中的各个节点上,充分利用了网络中的资源.其次,将节点返回的结果录入到中心文档中,然后确定其分数上限,对节点进行选择,提高了查询效率.     

P2P环境下数据管理系统上的Top-k查询

目前大多数P2P系统只提供文件的共享,缺乏数据管理能力.基于关系数据库上的关键搜索,本文提出了一种在P2P环境下共享数据库的新框架,其中每个节点上的数据库被看成是一个文档集,用户不用考虑数据库的模式结构信念,简化了不同节点数据库模式间的映射过程,能更好地适应P2P的分散和动态特性.将基于直方图的分层Top-k查询算法扩展到P2P环境下的数据库管理系统上,文档集和数据库的查询被统一起来,一致对待.在查询处理期间,直方图可以自动更新,同时根据查询结果,邻居节点可以自调整,具有自适应性.实验结果表明,基于关键词的数据库共享突破了传统的数据库共享模式,简化了数据访问方式,而基于直方图的Top-k查询算法提高了查询效率.     

语义查询扩展中词语-概念相关度的计算

在基于语义的查询扩展中,为了找到描述查询需求语义的相关概念,词语.概念相关度的计算是语义查询扩展中的关键一步.针对词语.概念相关度的计算,提出一种K2CM(keyword to concept method)方法.K2CM方法从词语.文档.概念所属程度和词语.概念共现程度两个方面来计算词语.概念相关度问语.文档.概念所属程度来源于标注的文档集中词语对概念的所属关系,即词语出现在若干文档中而文档被标注了若干概念.词语.概念共现程度是在词语概念对的共现性基础上增加了词语概念对的文本距离和文档分布特征的考虑.3种不同类型数据集上的语义检索实验结果表明,与传统方法相比,基于K2CM的语义查询扩展可以提高查询效果.     

P2P系统中基于Web的索引信息扩展算法的研究

基于环球网(Web)的特点和用户在点对点(P2P)系统中搜索的习惯,提出了一个在P2P系统中对媒体文件自动生成索引的方法。该方法有效地解决了媒体文件描述符不足所带来的查询精度低的问题。同时,提出了一个在P2P系统中节点信息的更新策略。实验表明,描述符扩展后,媒体文件查询结果的准确率得到了显著的提高。     

基于用户日志的查询扩展统计模型

信息检索长期存在着用词歧义性问题,在Web搜索上的表现更加突出.提出了一种基于用户查询日志的查询扩展统计模型,将用户查询中使用的词或短语与文档中出现的相应词或短语以条件概率的形式连接,利用贝叶斯公式挑选出文档中与该查询关联最紧密的词加入原查询,以达到扩展优化的目的.实验结果表明,该方法更适宜改进Web上的信息检索,相对传统的查询扩展算法可以大幅度提高查询精度.     

自适应P2P网络搜索算法

提出了一个基于查询代理的无结构P2P网络盲搜索算法，该算法在查询过程中感知并分析P2P网络的相关信息，根据查询满足情况自适应地控制子查询规模。与已有的盲搜索算法相比，查询代理算法实现了更细致的冗余开销控制，并避免了已有算法存在的优化难题。与已有盲搜索算法的对比实验的结果证实该算法可以更有效地降低冗余开销。     

基于关联规则与聚类算法的查询扩展算法

针对信息检索中查询关键词与文档用词不匹配的问题,提出一种基于关联规则与聚类算法的查询扩展算法。该算法在第1阶段对初始查询结果的前N篇文档进行关联规则挖掘,提取含有初始查询项的关联规则构建规则库,并从中选取与查询用词关联度最大的置个词作为扩展词,与初始查询组成新查询后再次查询,在第2阶段将新查询结果进行聚类分析并计算结果中每篇文档的最终相关度,按最终相关度大小重新排序。实验结果表明,该算法比单独使用关联规则算法或是单独使用聚类算法均有更优的检索性能。     

P2P网络下的KNN查询

K最近邻(KNN)查询是相似性查询的一种,已有大部分KNN查询算法都是针对集中式计算环境的,因此很容易形成性能瓶颈。P2P这种新的分布式计算技术能够有效克服集中式计算环境中的性能瓶颈问题。提出了一种分组式P2P网络结构下基于iDisdance索引的KNN查询方法,其主要思想是通过分布式簇索引裁剪搜索空间,降低网络通信开销,从而在P2P环境下执行KNN查询。最后通过仿真测试了该方法的有效性以及分组数量与数据分布对查询开销的影响。     

混合式P2P系统中基于缓存的多项查询方法研究

首先从混合式P2P网络拓扑结构出发,结合DHT思想,提出了基于DHT的层次化P2P网络模型.其次根据在文档集巨大的情况下,用户提交的查询不可能"面面俱到",实际用来回答查询的文档仅仅是文档集中很小的一部分这一思想,在层次化P2P模型的超级节点中建立了分布式缓存,运用分布式索引与缓存技术,提出一种新的方法来解决多项查询问题.即由多项查询中的某个关键字key,根据hash函数定位到负责该key的超级节点,查询该节点上的分布式索引得到缓存具体存储位置,最终将结果返回给用户,如若缓存中没有所要查询的内容,则广播该查询,同时根据系统中的历史广播查询信息来计算某个待选缓存项的利益值,利益最大的待选项加入缓存.一般针对多项查询的泛洪算法往往会造成巨大的网络信息量,提出的方法牺牲了超级节点上一小部分的存储力,缓解了多项查询造成的网络拥挤现象.同时,基于DHT的层次化P2P模型也具有很好的稳定性,不会因为大量节点的动态加入或者退出而无法进行多项查询.     

基于信任的P2P真实性查询及副本管理算法s

文档安全性对于信息共享Peer-to-Peer(或P2P)系统而言是一项重要的性能指标,以P2P系统的文档安全性优化为目标.P2P系统的文档安全性主要取决于两方面的因素:其载体的安全性和文档相关机制的构造,如副本管理等.对于P2P这样高度自主的分布式系统而言,文档安全性的提高无法依赖于结点安全性的提高,而应依靠对文档相关机制的控制来实现.首先设计了一个对文档安全性敏感的查询协议,以该查询协议为基础,与文档相关的机制就可以形式化地表述为函数,而系统文档安全性的提高就转化为函数空间上的数学分析.基于函数分析的结果,设计了一套旨在提高文档真实性的副本管理算法集合.理论分析的结果表明:在理想情况下,该算法集合可达到文档真实性的优化.对于实际系统,经过大量的模拟实验结果验证,该算法集可以获得良好的效果,接近优化水平.     

基于用户日志挖掘的查询扩展方法

查询扩展可以有效地消除查询歧义,提高信息检索的准确率和召回率.通过挖掘用户日志中查询词和相关文档的连接关系,构造关联查询,并在此基础上提出一种从关联查询中提取查询扩展词的查询扩展方法.同时,还提出一种查询歧义的判别方法,该方法可以对查询词所表达的检索意图的模糊程度进行有效度量,也可以对查询词的检索性能进行预先估计.通过对查询歧义的度量来动态调整扩展词的长度,提高查询扩展模型的灵活性和适应能力.     

Query expansion by mining user logs

Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.     

The effects of multiple query evidences on social image retrieval

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality—more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation.     

PHIRST: A distributed architecture for P2P information retrieval

Recent progress in peer to peer (P2P) search algorithms has presented viable structured and unstructured approaches for full-text search. We posit that these existing approaches are each best suited for different types of queries. We present PHIRST, the first system to facilitate effective full-text search within P2P databases. PHIRST works by effectively leveraging between the relative strengths of these approaches. Similar to structured approaches, agents first publish terms within their stored documents. However, frequent terms are quickly identified and not exhaustively stored, resulting in a significant reduction in the system's storage requirements. During query lookup, agents use unstructured search to compensate for the lack of fully published terms. Additionally, they explicitly weigh between the costs involved in structured and unstructured approaches, allowing for a significant reduction in query costs. Finally, we address how node failures can be effectively addressed through storing multiple copies of selected data. We evaluated the effectiveness of our approach using both real-world and artificial queries. We found that in most situations our approach yields near perfect recall. We discuss the limitations of our system, as well as possible compensatory strategies.     

基于迭代方法的多层Markov网络信息检索模型

查询扩展是提高检索效果的有效方法,传统的查询扩展方法大都以单个查询词的相关性来扩展查询词,没有充分考虑词项之间、文档之间以及查询之间的相关性,使得扩展效果不佳。针对此问题,该文首先通过分别构造词项子空间和文档子空间的Markov网络,用于提取出最大词团和最大文档团,然后根据词团与文档团的映射关系将词团分为文档依赖和非文档依赖词团,并构建基于文档团依赖的Markov网络检索模型做初次检索,从返回的检索结果集合中构造出查询子空间的Markov网络,用于提取出最大查询团,最后,采用迭代的方法计算文档与查询的相关概率,并构建出最终的基于迭代方法的多层Markov网络信息检索模型。实验结果表明 该文的模型能较好地提高检索效果。     

基于查询扩展的人名消歧

针对现有很多基于特征的人名消歧方法不适用于文档本身特征稀疏的问题,提出一种借助丰富的互联网资源,使用搜索引擎查询并扩展出更多与文档相关特征的方法。首先根据搜索引擎的特性构建了四类查询规则,然后通过这些查询规则进行搜索并返回前k个文档,最后对这些文档使用文档频率(DF)方法进行特征选择,并将选择的特征加入到原文档中。实验证明,该方法能显著提高人名消歧系统的性能,平均F值由76%增加到81%。     

A personalized query expansion approach for engineering document retrieval

Engineers create engineering documents with their own terminologies, and want to search existing engineering documents quickly and accurately during a product development process. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy has been often problematic because of the semantic ambiguity of terminologies in engineering documents and queries. The semantic ambiguity can be alleviated by using a domain ontology. Also, if queries are expanded to incorporate the engineer’s personalized information needs, the accuracy of the search result would be improved. Therefore, we propose a framework to search engineering documents with less semantic ambiguity and more focus on each engineer’s personalized information needs. The framework includes four processes: (1) developing a domain ontology, (2) indexing engineering documents, (3) learning user profiles, and (4) performing personalized query expansion and retrieval. A domain ontology is developed based on product structure information and engineering documents. Using the domain ontology, terminologies in documents are disambiguated and indexed. Also, a user profile is generated from the domain ontology. By user profile learning, user’s interests are captured from the relevant documents. During a personalized query expansion process, the learned user profile is used to reflect user’s interests. Simultaneously, user’s searching intent, which is implicitly inferred from the user’s task context, is also considered. To retrieve relevant documents, an expanded query in which both user’s interests and intents are reflected is then matched against the document collection. The experimental results show that the proposed approach can substantially outperform both the keyword-based approach and the existing query expansion method in retrieving engineering documents. Reflecting a user’s information needs precisely has been identified to be the most important factor underlying this notable improvement.     

Efficient Monte Carlo clustering in subspaces

One of the key difficulties for users in information retrieval is to formulate appropriate queries to submit to the search engine. In this paper, we propose an approach to enrich the user’s queries by additional context. We used the Language Model to build the query context, which is composed of the most similar queries to the query to expand and their top-ranked documents. Then, we applied a query expansion approach based on the query context and the Latent Semantic Analyses method. Using a web test collection, we tested our approach on short and long queries. We varied the number of recommended queries and the number of expansion terms to specify the appropriate parameters for the proposed approach. Experimental results show that the proposed approach improves the effectiveness of the information retrieval system by 19.23 % for short queries and 52.94 % for long queries according to the retrieval results using the original users’ queries.     

基于网页查询结果的广告查询扩展研究

在计算广告学中,为用户查询返回相关的广告一直是研究的热点。然而用户的查询一般比较简短,广告的表示也局限在简短的创意和一些竞价词上,返回符合用户查询意图的广告十分困难。为了解决这个问题,该文提出利用多特征融合的方法进行广告查询扩展,先将查询输入到搜索引擎中,获得Top-k网页查询结果,将它们作为获取扩展词的外部资源,由于采用一般的特征选取方法获取扩展词采用的特征比较单一,缺乏语义信息,容易产生主题漂移现象,该文通过计算扩展词和查询词在网页查询结果中的共现度,并融合传统的TF特征和词性信息,获得与原始查询语义相关的扩展词。在真实的广告语料上的实验结果显示,基于多特征融合的选择广告扩展词的方法能有效地提高返回广告的相关性。     

Using WordNet and lexical operators to improve Internet searches

A natural language interface system for an Internet search engine shows substantial increases in the precision of query results and the percentage of queries answered correctly. The system expands queries based on a word-sense-disambiguation method and postprocesses retrieved documents to extract only the parts relevant to a query