Enhancing Search Performance on Gnutella-Like P2P Systems

The big challenges facing the search techniques on Gnutella-like peer-to-peer networks are search efficiency and quality of search results. In this paper, leveraging information retrieval (IR) algorithms such as Vector Space Model (VSM) and relevance ranking algorithms, we present GES (Gnutella with Efficient Search) to improve search performance. The key idea is that GES uses a distributed topology adaptation algorithm to organize semantically relevant nodes into same semantic groups by using the notion of node vector. Given a query, GES employs an efficient search protocol to direct the query to the most relevant semantic groups for answers, thereby achieving high recall with probing only a small fraction of nodes. To the best of our knowledge, GES is the first to identify node vector size as an important role in impacting search performance and to show that the node vector size offers a good trade-off between search performance and bandwidth cost. Moreover, GES adopts automatic query expansion and local data clustering to improve search performance. We show that GES is efficient and even outperforms the centralized node clustering system SETS. For example, in the scenario where node capacity is heterogeneous, GES can achieve 73 percent recall when probing only 20 percent nodes, outperforming SETS by about 18 percent.     

SemSearchPro – Using semantics throughout the search process

Semantic search attempts to go beyond the current state of the art in information access by addressing information needs on the semantic level, i.e. considering the meaning of users’ queries and the available resources. In recent years, there have been significant advances in developing and applying semantic technologies to the problem of semantic search. To collate these various approaches and to better understand what the concept of semantic search entails, we study semantic search under a general model. Extending this model, we introduce the notion of process-based semantic search, where semantics is exploited not only for query processing, but might be involved in all steps of the search process. We propose a particular approach that instantiates this process-based model. The usefulness of using semantics throughout the search process is finally assessed via a task-based evaluation performed in a real world scenario.     

CODISC: Collaborative and distributed semantic caching for maximizing cache effectiveness in wireless networks

In wireless mobile ad hoc networks (MANETs), a mobile node would normally acquire data from a data server through an access point by sending the server a request each time it needs data. To reduce the high costs normally associated with accessing remote servers (i.e., outside the MANET), data caching by the mobile nodes can be employed. Several caching techniques for MANETs have been proposed and implemented, including a cooperative scheme that we recently introduced. It employs a directory-based approach in which submitted queries are cached in the MANET to be used subsequently as indexes to corresponding data items (results). When a request is issued, nodes cooperate to find its answer (if it exists) and send it to the requesting node. In this paper, we extend this scheme by semantically comparing each submitted request with all cached queries. The semantic analysis process includes trimming the request into fragments and joining the answers of these fragments to produce the answer of the request. We study the performance of the proposed system both analytically and experimentally, and prove the advantageous features of the system relative to others in terms of query response time, generated traffic, and hit ratio.     

SSON：一种基于结构化P2P网络路由的语义覆盖网络结构

本文基于结构化P2P网络路由机制,采用基于主题划分的方法,提出了基于结构化P2P网络路由的语义覆盖网络SSON。SSON通过结构化P2P网络的标识符映射机制,根据资源类别将结点组织成层次化的覆盖网络,该覆盖网络结构确保搜索限制在与查询主题相关的局部结点子集中。该结构充分利用了结构化P2P网络的优点,解决了基于非结构化P2P网络建立的语义覆盖网络的对主题群的搜索低效问题,同时克服了结构化P2P网络仅支持精确匹配查找的缺点,为结构化P2P网络提供了可靠、高效的语义查询机制,极大地提高了查全率。     

Semantic-distance based evaluation of ranking queries over relational databases

Traditional database search uses pattern match in the comparison process. For a query with some search words, tuples are selected only if the words of the tuples exactly match the query words. In this paper, we propose a new method for evaluating relational ranking queries (or top-N queries) with text attributes. This method defines semantic distance functions and utilizes semantic match between words in database search. The attempt is that tuples, not only exactly matching, but also close to the query according to semantic distances, can both be fetched. The basic idea of the method is to create an index based on WordNet to expand the tuple words semantically. The candidate results for a query are retrieved by the index and a simple SQL selection statement, and then top-N answers are obtained. Extensive experiments are carried out to measure the performance of this new strategy for the evaluation of ranking queries over relational databases.     

基于MPEG-7的视频语义检索系统

设计和实现一个支持语义的分布式视频检索系统:"语寻"。该系统利用一个改进的视频语义处理工具(该工具基于IBM VideoAnnEx标注工具,并增加镜头语义图标注和自然语言处理的功能)对视频进行语义分析和标注,生成包含语义信息的MPEG-7描述文件,然后对视频的MPEG-7描述文件建立分布式索引,并同时分布式存储视频文件;系统提供丰富的Web查询接口,包括关键字语义扩展查询,语义图查询以及自然语句查询,当用户提交语义查询意图后,便能够迅速地检索到感兴趣的视频和片段,并且可以浏览点播;整个系统采用分布式架构,具备良好的可扩展性,并能够支持海量视频信息的索引和检索。     

基于语义的Web信息检索系统的研究

提出了对含有自由文本和丰富语义标记的网络文档资源的一种检索方法.通过对现有的三种语义检索系统原型的分析,提出了一个改进后的实现框架,在此框架中文档资源和查询都可用Web本体语言描述.这些描述提供了关于文档和其内容结构化或半结构化的信息.当这些文档被索引后执行语义查询时或者查询结果处理时,它可以对这些信息进行语义推理,从而将极大地提高检索效果.     

Rewiring strategies for semantic overlay networks

Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups, by means of a rewiring procedure that is periodically executed by each peer. This procedure establishes new connections to similar peers and disregards connections to peers that are dissimilar. Retrieval effectiveness is then improved by exploiting this information at query time (as queries may address clusters of similar peers). Although all systems based on semantic overlay networks apply some rewiring technique, there is no comprehensive study showing the effect of rewiring on system’s performance. In this work, a framework for studying the attribution of rewiring strategies in semantic overlay networks is proposed. A generic approach to rewiring is presented and several variants of this approach are reviewed and evaluated. We show how peer organisation is affected by the different design choices of the rewiring mechanism and how these choices affect the performance of the system overall (both in terms of communication overhead and retrieval effectiveness). Our experimental evaluation with real-word data and queries confirms the dependence between rewiring strategies and retrieval performance, and gives insights on the trade-offs involved in the selection of a rewiring strategy.     

Personalized Concept-Based Clustering of Search Engine Queries

The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.     

以访问频率为路由方向感的无结构P2P搜索

在全分布无结构P2P中,节点通常组织成为覆盖网络,通过查询消息在网络中广泛转发实现盲目搜索。由于数据存放位置独立于数据内容,一个节点并不清楚哪些节点更容易命中查询,因此发现路由方向感,提高查询消息转发有效性,对全分布无结构P2P搜索具有重要意义。在相关工作中,主要从用户兴趣、本体论等语义角度聚类用户,减小搜索范围。但当前语义获取和语义描述等工作还不甚成熟,因此这些方法并没有得到广泛采用。提出了一种以访问频率为路由方向感的新型搜索方法QRRO。在QRRO中,每个节点被分配一权重标识;节点仅仅为访问频率与节点权重接近的数据建立索引;基于访问频率建立存储内容和存储位置之间的藕合关系,形成路由方向感。模拟实验表明,QRRO在提高搜索成功率、降低搜索路径长度方面是有效的。而且,由于访问频率是每个文件都具有的非语义属性,因此QRRO具有通用性。     

SSW: A Small-World-Based Overlay for Peer-to-Peer Search

Peer-to-peer (P2P) systems have become a popular platform for sharing and exchanging voluminous information among thousands or even millions of users. The massive amount of information shared in such systems mandates efficient semantic-based search instead of key-based search. The majority of existing proposals can only support simple key-based search rather than semantic-based search. This paper presents the design of an overlay network, namely, semantic small world (SSW), that facilitates efficient semantic-based search in P2P systems. SSW achieves the efficiency based on four ideas: 1) semantic clustering, where peers with similar semantics organize into peer clusters, 2) dimension reduction, where to address the high maintenance overhead associated with capturing high-dimensional data semantics in the overlay, peer clusters are adaptively mapped to a one-dimensional naming space, 3) small world network, where peer clusters form into a one-dimensional small world network, which is search efficient with low maintenance overhead, and 4) efficient search algorithms, where peers perform efficient semantic-based search, including approximate point query and range query in the proposed overlay. Extensive experiments using both synthetic data and real data demonstrate that SSW is superior to the state of the art on various aspects, including scalability, maintenance overhead, adaptivity to distribution of data and locality of interest, resilience to peer failures, load balancing, and efficiency in support of various types of queries on data objects with high dimensions.     

Automatic content based image retrieval using semantic analysis

We present a new text-to-image re-ranking approach for improving the relevancy rate in searches. In particular, we focus on the fundamental semantic gap that exists between the low-level visual features of the image and high-level textual queries by dynamically maintaining a connected hierarchy in the form of a concept database. For each textual query, we take the results from popular search engines as an initial retrieval, followed by a semantic analysis to map the textual query to higher level concepts. In order to do this, we design a two-layer scoring system which can identify the relationship between the query and the concepts automatically. We then calculate the image feature vectors and compare them with the classifier for each related concept. An image is relevant only when it is related to the query both semantically and content-wise. The second feature of this work is that we loosen the requirement for query accuracy from the user, which makes it possible to perform well on users’ queries containing less relevant information. Thirdly, the concept database can be dynamically maintained to satisfy the variations in user queries, which eliminates the need for human labor in building a sophisticated initial concept database. We designed our experiment using complex queries (based on five scenarios) to demonstrate how our retrieval results are a significant improvement over those obtained from current state-of-the-art image search engines.     

基于语义相似度的Web数据库不精确查询方法

为了解决普通用户对于Web数据库的不精确查询问题,提出了一种基于语义相似度的Web数据库不精确查询方法。对于一个给定查询,该方法首先在查询历史中找出一个(或若干)与其相似度高于给定放松阈值的查询,然后从数据库中找出与这些查询相匹配的元组作为当前查询的不精确查询的结果,最后将这些查询结果按其对初始查询的满足程度进行排序。实验结果表明,提出的不同查询之间的语义相似度评估方法性能稳定、评估结果合理,不精确查询方法具有较高的查全率和排序准确性。     

融合蚁群算法和路由侦听的移动P2P搜索

提出融合蚁群算法和节约带宽的路由侦听技术的移动P2P搜索算法,它计算响应和节点语义相似度以更新节点路由表的信息素,依据表中的信息素来决定节点查询转发的方向;通过缓存路由经过节点的查询消息,侦听路径节点的响应消息,并据此顺带应答缓存的查询消息.实验结果表明,与其他同类算法相比,本文的移动P2P搜索算法在较低的带宽消耗下获得较高搜索成功率,有效地提高了搜索性能.     

Patterns of semantic relations to improve image content search

This paper reports on a study to explore how semantic relations can be used to expand a query for objects in an image. The study is part of a project with the overall objective to provide semantic annotation and search facilities for a virtual collection of art resources. In this study we used semantic relations from WordNet for 15 image content queries. The results show that, next to the hyponym/hypernym relation, the meronym/holonym (part-of) relation is particularly useful in query expansion. We identified a number of relation patterns that improve recall without jeopardising precision.     

SemreX: Efficient search in a semantic overlay for literature retrieval

The World Wide Web is growing at such a pace that even the biggest centralized search engines are able to index only a small part of the available documents on the Internet. The decentralized structure, together with the features of self-organization and fault-tolerance, makes peer-to-peer networking an effective information-sharing model; however, content searching still remains a serious challenge of large scale peer-to-peer networks. In this paper we present SemreX, a semantic overlay for desktop literature/ document retrieval in peer-to-peer networks. We present a semantic overlay algorithm by which semantically similar peers are locally clustered together, and long-range connections are rewired for a short-cut in peer-to-peer networks. Based on the semantic overlay, a heuristic query routing algorithm is proposed for efficient content searching. We conduct a comprehensive simulation to evaluate the search performance of our algorithms. Results show that search in our SemreX semantic overlay greatly improves search efficiency.     

QUERY ROUTING IN A PEER-TO-PEER SEMANTIC LINK NETWORK

A semantic link peer-to-peer (P2P) network specifies and manages semantic relationships between peers' data schemas and can be used as the semantic layer of a scalable Knowledge Grid. The proposed approach consists of an automatic semantic link discovery method, a tool for building and maintaining P2P semantic link networks (P2PSLNs), a semantic-based peer similarity measurement for efficient query routing, and the schema mapping algorithms for query reformulation and heterogeneous data integration. The proposed approach has three important aspects. First, it uses semantic links to enrich the relationships between peers' data schemas. Second, it considers not only nodes but also the XML structure in measuring the similarity between schemas to efficiently and accurately forward queries to relevant peers. Third, it copes with semantic and structural heterogeneity and data inconsistency so that peers can exchange and translate heterogeneous information within a uniform view.     

Searching and ranking method of relevant resources by user intention on the Semantic Web

As the information on the Internet dramatically increases, more and more limitations in information searching are revealed, because web pages are designed for human use by mixing content with presentation. In order to overcome these limitations, the Semantic Web, based on ontology, was introduced by W3C to bring about significant advancement in web searching. To accomplish this, the Semantic Web must provide search methods based on the different relationships between resources.In this paper, we propose a semantic association search methodology that consists of the evaluation of resources and relationships between resources, as well as the identification of relevant information based on ontology, a semantic network of resources and properties. The proposed semantic search method is based on an extended spreading activation technique. In order to evaluate the importance of a query result, we propose weighting methods for measuring properties and resources based on their specificity and generality. From this work, users can search semantically associated resources for their query, confident that the information is valuable and important. The experimental results show that our method is valid and efficient for searching and ranking semantic search results.     

A novel XML keyword query approach using entity subtree

Keyword query is an important means to find object information in XML document. Most of the existing keyword query approaches adopt the subtrees rooted at the smallest lowest common ancestors of the keyword matching nodes as the basic result units. The structural relationships among XML nodes are excessively emphasized but the semantic relevance is not fully exploited.To change this situation, we propose the concept of entity subtree and emphasis the semantic relevance among different nodes as querying information from XML. In our approach, keyword query cases are improved to a new keyword-based query language, Grouping and Categorization Keyword Expression (GCKE) and the core query algorithm, finding entity subtrees (FEST) is proposed to return high quality results by fully using the keyword semantic meanings exposed by GCKE. We demonstrate the effectiveness and the efficiency of our approach through extensive experiments.     

一种基于本体的知识库语义扩展搜索方法

为使知识库的信息搜索突破传统基于关键字查询的局限,提出一种基于本体的知识库语义扩展搜索方法。将本体和语义扩展引入知识库,对用户查询条件进行扩展搜索,通过相关度分析对搜索结果进行排序,使搜索效果得到优化。实验结果表明,该方法能提高搜索查全率和查准率。