Assisting web search using query suggestion based on word similarity measure and query modification patterns

One of the useful tools offered by existing web search engines is query suggestion (QS), which assists users in formulating keyword queries by suggesting keywords that are unfamiliar to users, offering alternative queries that deviate from the original ones, and even correcting spelling errors. The design goal of QS is to enrich the web search experience of users and avoid the frustrating process of choosing controlled keywords to specify their special information needs, which releases their burden on creating web queries. Unfortunately, the algorithms or design methodologies of the QS module developed by Google, the most popular web search engine these days, is not made publicly available, which means that they cannot be duplicated by software developers to build the tool for specifically-design software systems for enterprise search, desktop search, or vertical search, to name a few. Keyword suggested by Yahoo! and Bing, another two well-known web search engines, however, are mostly popular currently-searched words, which might not meet the specific information needs of the users. These problems can be solved by WebQS, our proposed web QS approach, which provides the same mechanism offered by Google, Yahoo!, and Bing to support users in formulating keyword queries that improve the precision and recall of search results. WebQS relies on frequency of occurrence, keyword similarity measures, and modification patterns of queries in user query logs, which capture information on millions of searches conducted by millions of users, to suggest useful queries/query keywords during the user query construction process and achieve the design goal of QS. Experimental results show that WebQS performs as well as Yahoo! and Bing in terms of effectiveness and efficiency and is comparable to Google in terms of query suggestion time.     

A statistical method for estimating the usefulness of text databases

Searching desired data on the Internet is one of the most common ways the Internet is used. No single search engine is capable of searching all data on the Internet. The approach that provides an interface for invoking multiple search engines for each user query has the potential to satisfy more users. When the number of search engines under the interface is large, invoking all search engines for each query is often not cost effective because it creates unnecessary network traffic by sending the query to a large number of useless search engines and searching these useless search engines wastes local resources. The problem can be overcome if the usefulness of every search engine with respect to each query can be predicted. We present a statistical method to estimate the usefulness of a search engine for any given query. For a given query, the usefulness of a search engine in this paper is defined to be a combination of the number of documents in the search engine that are sufficiently similar to the query and the average similarity of these documents. Experimental results indicate that our estimation method is much more accurate than existing methods.     

Precision Evaluation of Search Engines

In this paper, we present a general approach for statistically evaluating precision of search engines on the Web. Search engines are evaluated in two steps based on a large number of sample queries: (a) computing relevance scores of hits from each search engine, and (b) ranking the search engines based on statistical comparison of the relevance scores. In computing relevance scores of hits, we study four relevance scoring algorithms. Three of them are variations of algorithms widely used in the traditional information retrieval field. They are cover density ranking, Okapi similarity measurement, and vector space model algorithms. In addition, we develop a new three-level scoring algorithm to mimic commonly used manual approaches. In ranking the search engines in terms of precision, we apply a statistical metric called probability of win. In our experiments, six popular search engines, AltaVista, Fast, Google, Go, iWon, and NorthernLight, were evaluated based on queries from two domains of interest: parallel and distributed processing, and knowledge and data engineering. The first query set contains 1726 queries collected from the index terms of papers published in the IEEE Transactions on Knowledge and Data Engineering. The second set contains 1383 queries collected from the index terms of papers published in the IEEE Transactions on Parallel and Distributed Systems. Search engines were queried and compared in two different search modes: the default search mode and the exact phrase search mode. Our experimental results show that these six search engines performed differently under different search modes and scoring methods. Overall, Google was the best. NorthernLight was mostly second in the default search mode, whereas iWon was mostly second in the exact phrase search mode.     

A personalized search engine based on Web‐snippet hierarchical clustering

We propose a (meta‐)search engine, called SnakeT (SNippet Aggregation for Knowledge ExtracTion), which queries more than 18 commodity search engines and offers two complementary views on their returned results. One is the classical flat‐ranked list, the other consists of a hierarchical organization of these results into folders created on‐the‐fly at query time and labeled with intelligible sentences that capture the themes of the results contained in them. Users can browse this hierarchy with various goals: knowledge extraction, query refinement and personalization of search results. In this novel form of personalization, the user is requested to interact with the hierarchy by selecting the folders whose labels (themes) best fit her query needs. SnakeT then personalizes on‐the‐fly the original ranked list by filtering out those results that do not belong to the selected folders. Consequently, this form of personalization is carried out by the users themselves and thus results fully adaptive, privacy preserving, scalable and non‐intrusive for the underlying search engines. We have extensively tested SnakeT and compared it against the best available Web‐snippet clustering engines. SnakeT is efficient and effective, and shows that a mutual reinforcement relationship between ranking and Web‐snippet clustering does exist. In fact, the better the ranking of the underlying search engines, the more relevant the results from which SnakeT distills the hierarchy of labeled folders, and hence the more useful this hierarchy is to the user. Vice versa, the more intelligible the folder hierarchy, the more effective the personalization offered by SnakeT on the ranking of the query results. Copyright © 2007 John Wiley & Sons, Ltd.     

Using Local Popularity of Web Resources for Geo-Ranking of Search Engine Results

Search engines retrieve and rank Web pages which are not only relevant to a query but also important or popular for the users. This popularity has been studied by analysis of the links between Web resources. Link-based page ranking models such as PageRank and HITS assign a global weight to each page regardless of its location. This popularity measurement has shown successful on general search engines. However unlike general search engines, location-based search engines should retrieve and rank higher the pages which are more popular locally. The best results for a location-based query are those which are not only relevant to the topic but also popular with or cited by local users. Current ranking models are often less effective for these queries since they are unable to estimate the local popularity. We offer a model for calculating the local popularity of Web resources using back link locations. Our model automatically assigns correct locations to the links and content and uses them to calculate new geo-rank scores for each page. The experiments show more accurate geo-ranking of search engine results when this model is used for processing location-based queries.     

Effective rank aggregation for metasearching

Nowadays, mashup services and especially metasearch engines play an increasingly important role on the Web. Most of users use them directly or indirectly to access and aggregate information from more than one data sources. Similarly to the rest of the search systems, the effectiveness of a metasearch engine is mainly determined by the quality of the results it returns in response to user queries. Since these services do not maintain their own document index, they exploit multiple search engines using a rank aggregation method in order to classify the collected results. However, the rank aggregation methods which have been proposed until now, utilize a very limited set of parameters regarding these results, such as the total number of the exploited resources and the rankings they receive from each individual resource. In this paper we present QuadRank, a new rank aggregation method, which takes into consideration additional information regarding the query terms, the collected results and the data correlated to each of these results (title, textual snippet, URL, individual ranking and others). We have implemented and tested QuadRank in a real-world metasearch engine, QuadSearch, a system developed as a testbed for algorithms related to the wide problem of metasearching. The name QuadSearch is related to the current number of the exploited engines (four). We have exhaustively tested QuadRank for both effectiveness and efficiency in the real-world search environment of QuadSearch and also, using a task from the recent TREC-2009 conference. The results we present in our experiments reveal that in most cases QuadRank outperformed all component engines, another metasearch engine (Dogpile) and two successful rank aggregation methods, Borda Count and the Outranking Approach.     

基于后向链接的查询路由研究

尽管通用搜索引擎已经被广泛使用，但它们筛选用户查询结果中无关结果的功能一直不能任人满意，因此有些搜索需要使用专题搜索引擎，文章提出了一种基于中文专题搜索引擎的查询路由架构，该架构为用户的查询寻找合适的专题搜索引擎路由，并找出最佳搜索引擎，除了描述构架外，其中使用到的查询扩展和聚类算法文章也一并给出。     

QUBiC: An adaptive approach to query-based recommendation

Search engine users often encounter the difficulty of phrasing the precise query that could lead to satisfactory search results. Query recommendation is considered an effective assistant in enhancing keyword-based queries in search engines and Web search software. In this paper, we present a Query-URL Bipartite based query reCommendation approach, called QUBiC. It utilizes the connectivity of a query-URL bipartite graph to recommend related queries and can significantly improve the accuracy and effectiveness of personalized query recommendation systems comparing with the conventional pairwise similarity based approach. The main contribution of the QUBiC approach is its three-phase framework for personalized query recommendations. The first phase is the preparation of queries and their search results returned by a search engine, which generates a historical query-URL bipartite collection. The second phase is the discovery of similar queries by extracting a query affinity graph from the bipartite graph, instead of operating on the original bipartite graph directly using biclique-based approach or graph clustering. The query affinity graph consists of only queries as its vertices and its edges are weighted according to a query-URL vector based similarity (dissimilarity) measure. The third phase is the ranking of similar queries. We devise a novel rank mechanism for ordering the related queries based on the merging distances of a hierarchical agglomerative clustering (HAC). By utilizing the query affinity graph and the HAC-based ranking, we are able to capture the propagation of similarity from query to query by inducing an implicit topical relatedness between queries. Furthermore, the flexibility of the HAC strategy makes it possible for users to interactively participate in the query recommendation process, and helps to bridge the gap between the determinacy of actual similarity values and the indeterminacy of users’ information needs, allowing the lists of related queries to be changed from user to user and query to query, thus adaptively recommending related queries on demand. Our experimental evaluation results show that the QUBiC approach is highly efficient and more effective compared to the conventional query recommendation systems, yielding about 13.3 % as the most improvement in terms of precision.     

一种新的搜索引擎查询导向系统

网络上的专业搜索引擎数量众多，普通用户在选择时往往无所适从。文章提出了一个自动的查询导向系统，可以将用户查询自动导向到合适的专业搜索引擎，解决了这个矛盾。     

LESSON: A system for lecture notes searching and sharing over Internet

In this paper, we present a system LESSON for lecture notes searching and sharing, which is dedicated to both instructors and students for effectively supporting their Web-based teaching and learning activities. The LESSON system employs a metasearch engine for lecture notes searching from Web and a peer-to-peer (P2P) overlay network for lecture notes sharing among the users. A metasearch engine provides an unified access to multiple existing component search engines and has better performance than general-purpose search engines. With the help of a P2P overlay network, all computers used by instructors and students can be connected into a virtual society over the Internet and communicate directly with each other for lecture notes sharing, without any centralized server and manipulation. In order to merge results from multiple component search engines into a single ranked list, we design the RSF strategy that takes rank, similarity and features of lecture notes into account. To implement query routing decision for effectively supporting lecture notes sharing, we propose a novel query routing mechanism. Experimental results indicate that the LESSON system has better performance in lecture notes searching from Web than some popular general-purpose search engines and some existing metasearch schemes; while processing queries within the system, it outperforms some typical routing methods. Concretely, it can achieve relatively high query hit rate with low bandwidth consumption in different types of network topologies.     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

Towards a graph-based user profile modeling for a session-based personalized search

Most Web search engines use the content of the Web documents and their link structures to assess the relevance of the document to the user’s query. With the growth of the information available on the web, it becomes difficult for such Web search engines to satisfy the user information need expressed by few keywords. First, personalized information retrieval is a promising way to resolve this problem by modeling the user profile by his general interests and then integrating it in a personalized document ranking model. In this paper, we present a personalized search approach that involves a graph-based representation of the user profile. The user profile refers to the user interest in a specific search session defined as a sequence of related queries. It is built by means of score propagation that allows activating a set of semantically related concepts of reference ontology, namely the ODP. The user profile is maintained across related search activities using a graph-based merging strategy. For the purpose of detecting related search activities, we define a session boundary recognition mechanism based on the Kendall rank correlation measure that tracks changes in the dominant concepts held by the user profile relatively to a new submitted query. Personalization is performed by re-ranking the search results of related queries using the user profile. Our experimental evaluation is carried out using the HARD 2003 TREC collection and showed that our session boundary recognition mechanism based on the Kendall measure provides a significant precision comparatively to other non-ranking based measures like the cosine and the WebJaccard similarity measures. Moreover, results proved that the graph-based search personalization is effective for improving the search accuracy.     

Retrieving keyworded subgraphs with graph ranking score

Keyword queries have long been popular to search engines and to the information retrieval community and have recently gained momentum for its usage in the expert systems community. The conventional semantics for processing a user query is to find a set of top-k web pages such that each page contains all user keywords. Recently, this semantics has been extended to find a set of cohesively interconnected pages, each of which contains one of the query keywords scattered across these pages. The keyword query having the extended semantics (i.e., more than a list of keywords hyperlinked with each other) is referred to the graph query. In case of the graph query, all the query keywords may not be present on a single Web page. Thus, a set of Web pages with the corresponding hyperlinks need to be presented as the search result. The existing search systems reveal serious performance problem due to their failure to integrate information from multiple connected resources so that an efficient algorithm for keyword query over graph-structured data is proposed. It integrates information from multiple connected nodes of the graph and generates result trees with the occurrence of all the query keywords. We also investigate a ranking measure called graph ranking score (GRS) to evaluate the relevant graph results so that the score can generate a scalar value for keywords as well as for the topology.     

Neural network applications for automatic new topic identification of FAST and Excite search engine transaction logs

Abstract: Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms for more efficient search engines. Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. This study proposes an artificial neural network application in the area of search engine research to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals and query reformulation patterns. Sample data logs from the FAST and Excite search engines are selected to train the neural network and then the neural network is used to identify topic changes in the data log. As a result, almost all the performance measures yielded favourable results.     

集成搜索引擎的文本数据库选择

用户需要检索的信息往往分散存储在多个搜索多个搜索引擎各自的数据库里,对普通用户而言,访问多个搜索引擎并从返回的结果中分辨出确实有网页是一件费时费力的工作,集成搜索引擎则可以提供给用户一个同时记问多个搜索引擎人集成环境,集成搜索引擎能将其接收到的用户查询提交给底层的多个搜索引擎进行搜索,作为一种搜索工具,集成搜索引擎具有如WEB查询覆盖面比传统引擎更大,引警有更好的可扩展性等优点,讨论了解决集成搜索引擎的数据库选择问题的多种技术,针对用户提交的查询要求,通过数据库选择可以选定最有可能返回有用信息的底层搜索引擎。     

What users see - Structures in search engine results pages

This paper investigates the composition of search engine results pages. We define what elements the most popular web search engines use on their results pages (e.g., organic results, advertisements, shortcuts) and to which degree they are used for popular vs. rare queries. Therefore, we send 500 queries of both types to the major search engines Google, Yahoo, Live.com and Ask. We count how often the different elements are used by the individual engines. In total, our study is based on 42,758 elements. Findings include that search engines use quite different approaches to results pages composition and therefore, the user gets to see quite different results sets depending on the search engine and search query used. Organic results still play the major role in the results pages, but different shortcuts are of some importance, too. Regarding the frequency of certain host within the results sets, we find that all search engines show Wikipedia results quite often, while other hosts shown depend on the search engine used. Both Google and Yahoo prefer results from their own offerings (such as YouTube or Yahoo Answers). Since we used the .com interfaces of the search engines, results may not be valid for other country-specific interfaces.     

An evolutionary approach for combining different sources of evidence in search engines

Modern Web search engines use different strategies to improve the overall quality of their document rankings. Usually the strategy adopted involves the combination of multiple sources of relevance into a single ranking. This work proposes the use of evolutionary techniques to derive good evidence combination functions using three different sources of evidence of relevance: the textual content of documents, the reputation of documents extracted from the connectivity information available in the processed collection and the anchor text concatenation. The combination functions discovered by our evolutionary strategies were tested using a collection containing 368 queries extracted from a real nation-wide search engine query log with over 12 million documents. The experiments performed indicate that our proposal is an effective and practical alternative for combining sources of evidence into a single ranking. We also show that different types of queries submitted to a search engine can require different combination functions and that our proposal is useful for coping with such differences.     

一种基于用户标记的搜索结果排序算法

随着计算机网络的快速发展,网络上的信息量也日益纷繁复杂.如何准确、快速地帮助人们从海量网络数据中获取所需信息,这是目前搜索引擎首要解决的问题,为此,各种搜索排序算法应运而生.但是目前,网页信息的表达形式都十分简单,用户描述查询的形式更是十分简单,这就造成了在判断网页内容与用户查询相关性时十分困难.首先对现有的搜索引擎排序算法进行了分类总结,分析它们的优缺点.然后提出了一种基于用户反馈的语义标记的新方法,最后采用多种评估方法与Google搜索结果进行对比分析.实验结果表明,利用该方法所得到的排序结果比Google的排序结果更接近用户需求.     

A query refinement framework for xml keyword search

Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.     

基于知识的网页检索工具

随着因特网在全球范围的广泛使用,越来越多的人们借助于因特网从事科研和商务活动,而网页检索工具成了人们必不可少的软件工具．然而,目前流行的检索工具大多基于关键字查询,常常出现信息过载或有用信息丢失等现象．造成这一原因主要有两方面：用户提交的查询不能很好地表达他的目的;查询的结果没有建立有效的索引机制,引导人们快速找到有用信息。为此我们提出一种基于知识的网页检索工具（ＫＷＳＥ）,它是在已有的检索工具的