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.     

Query processing over incomplete autonomous databases: query rewriting using learned data dependencies

Incompleteness due to missing attribute values (aka “null values”) is very common in autonomous web databases, on which user accesses are usually supported through mediators. Traditional query processing techniques that focus on the strict soundness of answer tuples often ignore tuples with critical missing attributes, even if they wind up being relevant to a user query. Ideally we would like the mediator to retrieve such possibleanswers and gauge their relevance by accessing their likelihood of being pertinent answers to the query. The autonomous nature of web databases poses several challenges in realizing this objective. Such challenges include the restricted access privileges imposed on the data, the limited support for query patterns, and the bounded pool of database and network resources in the web environment. We introduce a novel query rewriting and optimization framework QPIAD that tackles these challenges. Our technique involves reformulating the user query based on mined correlations among the database attributes. The reformulated queries are aimed at retrieving the relevant possibleanswers in addition to the certain answers. QPIAD is able to gauge the relevance of such queries allowing tradeoffs in reducing the costs of database query processing and answer transmission. To support this framework, we develop methods for mining attribute correlations (in terms of Approximate Functional Dependencies), value distributions (in the form of Naïve Bayes Classifiers), and selectivity estimates. We present empirical studies to demonstrate that our approach is able to effectively retrieve relevant possibleanswers with high precision, high recall, and manageable cost.     

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。     

Form-based proxy caching for database-backed web sites: keywords and functions

Web caching proxy servers are essential for improving web performance and scalability, and recent research has focused on making proxy caching work for database-backed web sites. In this paper, we explore a new proxy caching framework that exploits the query semantics of HTML forms. We identify two common classes of form-based queries from real-world database-backed web sites, namely, keyword-based queries and function-embedded queries. Using typical examples of these queries, we study two representative caching schemes within our framework: (i) traditional passive query caching, and (ii) active query caching, in which the proxy cache can service a request by evaluating a query over the contents of the cache. Results from our experimental implementation show that our form-based proxy is a general and flexible approach that efficiently enables active caching schemes for database-backed web sites. Furthermore, handling query containment at the proxy yields significant performance advantages over passive query caching, but extending the power of the active cache to do full semantic caching appears to be less generally effective.     

Semantic segment extraction and matching for Internet FAQ retrieval

This investigation presents a novel approach to semantic segment extraction and matching for retrieving information from Internet FAQs with natural language queries. Two semantic segments, the question category segment (QS) and the keyword segment (KS), are extracted from the input queries and the FAQ questions with a semiautomatically derived question-semantic grammar. A semantic matching method is presented to estimate the similarity between the semantic segments of the query and the questions in the FAQ collection. Additionally, the vector space model (VSM) is adopted to measure the similarity between the query and the answers of the QA pairs. Finally, a multistage ranking strategy is adopted to determine the optimally performing combination of similarity metrics. The experimental results illustrate that the proposed method achieves an average rank of 4.52 and a top-10 recall rate of 90.89 percent. Compared with the query-expansion method, this method improves the performance by 4.82 places in the average rank of correct answers, 25.34 percent in the top-5 recall rate, and 5.21 percent in the top-10 recall rate.     

Querying linked data graphs using semantic relatedness: A vocabulary independent approach

Linked Data brings inherent challenges in the way users and applications consume the available data. Users consuming Linked Data on the Web, should be able to search and query data spread over potentially large numbers of heterogeneous, complex and distributed datasets. Ideally, a query mechanism for Linked Data should abstract users from the representation of data. This work focuses on the investigation of a vocabulary independent natural language query mechanism for Linked Data, using an approach based on the combination of entity search, a Wikipedia-based semantic relatedness measure and spreading activation. Wikipedia-based semantic relatedness measures address existing limitations of existing works which are based on similarity measures/term expansion based on WordNet. Experimental results using the query mechanism to answer 50 natural language queries over DBpedia achieved a mean reciprocal rank of 61.4%, an average precision of 48.7% and average recall of 57.2%.     

Comparing data summaries for processing live queries over Linked Data

A growing amount of Linked Data??graph-structured data accessible at sources distributed across the Web??enables advanced data integration and decision-making applications. Typical systems operating on Linked Data collect (crawl) and pre-process (index) large amounts of data, and evaluate queries against a centralised repository. Given that crawling and indexing are time-consuming operations, the data in the centralised index may be out of date at query execution time. An ideal query answering system for querying Linked Data live should return current answers in a reasonable amount of time, even on corpora as large as the Web. In such a live query system source selection??determining which sources contribute answers to a query??is a crucial step. In this article we propose to use lightweight data summaries for determining relevant sources during query evaluation. We compare several data structures and hash functions with respect to their suitability for building such summaries, stressing benefits for queries that contain joins and require ranking of results and sources. We elaborate on join variants, join ordering and ranking. We analyse the different approaches theoretically and provide results of an extensive experimental evaluation.     

Compressed Data Cube for Approximate OLAP Query Processing

Approximate query processing has emerged as an approach to dealing with the huge data volume and complex queries in the environment of data warehouse.In this paper,we present a novel method that provides approximate answers to OLAP queries.Our method is based on building a compressed (approximate) data cube by a clustering technique and using this compressed data cube to provide answers to queries directly,so it improves the performance of the queries.We also provide the algorithm of the OLAP queries and the confidence intervals of query results.An extensive experimental study with the OLAP council benchmark shows the effectiveness and scalability of our cluster-based approach compared to sampling.     

Web search results caching service for structured P2P networks

This paper proposes a two-level P2P caching strategy for Web search queries. The design is suitable for a fully distributed service platform based on managed peer boxes (set-top-box or DSL/cable modem) located at the edge of the network, where both boxes and access bandwidth to those boxes are controlled and managed by an ISP provider. Our solution significantly reduces user query traffic going outside of the ISP provider to get query results from the respective Web search engine. Web users are usually very reactive to worldwide events which cause highly dynamic query traffic patterns leading to load imbalance across peers. Our solution contains a strategy to quickly ease imbalance on peers and spread communication flow among participating peers. Each peer maintains a local result cache used to keep the answers for queries originated in the peer itself and queries for which the peer is responsible for by contacting the Web search engine on-demand. When query traffic is predominantly routed to a few responsible peers our strategy replicates the role of “being responsible for” to neighboring peers so that they can absorb query traffic. This is a fairly slow and adaptive process that we call mid-term load balancing. To achieve a short-term fair distribution of queries we introduce a location cache in each peer which keeps pointers to peers that have already requested the same queries in the recent past. This lets these peers share their query answers with newly requesting peers. This process is fast as these popular queries are usually cached in the first DHT hop of a requesting peer which quickly tends to redistribute load among more and more peers.     

DF还是IDF?主特征模型在Web信息检索中的使用

Web信息检索的难点之一就是简短、模糊的用户查询与存在大量冗余和噪声的文档之间的不匹配.对Web文档信息特征进行分析,提出Web文档主特征词、主特征域和主特征空间的概念,在该空间上使用文档频度DF(document frequency)信息而非传统意义上的IDF(inverse document frequency)信息进行权值计算,并给出一个改进的相似度计算模型.使用该模型在10G和19G的两个大规模Web文档集合上进行了3组标准测试.比较实验表明,与传统IDF思想相比,在各项评价指标上,DF相关的主特征权值计算方法都能始终较大幅度地提高系统性能,最大达到18.6%的性能改善.     

深层网查询接口的自动集成

深层网数据库的访问方式主要是通过查询接口,所以查询接口是外部访问深层网数据库的门户.为了能够同时访问同一领域多个Web数据库,需要对多个Web数据库的查询接口进行集成.因此,提出基于本体的深层网查询接口集成方法.首先构建领域核心本体,在模式匹配过程中,不断完善核心本体;然后,以本体作为媒介,在不同查询接口模式间建立属性映射关系,发现属性间的语义关联;最后,根据本体概念出现的频数生成集成接口.实验表明提出的深层网查询接口自动集成方法是可行的和高效的.     

A form-based query interface for complex queries

It has been now well over thirty years since the advent of visual query systems (VQSs). Following a very active research period spanning from the late 1980s to the early 1990s, key research areas still remain, such as how to express complex queries in user-friendly fashion using a form-based query interface. A query is considered potentially complex from the user׳s point of view when it contains several entity sets and involves an aggregate operator and/or a many-to-many relationship set. This work examines such complex queries in the light of a form-based VQS, called OVI-2, and developed to handle the sophisticated query needs of users of the student records system at Aalto University, Otaniemi campus.Special emphasis is given to complex queries which involve existential quantification or its negation, such as finding students who have completed one given set of courses but who have not yet completed another given set of courses. User-friendliness was a key priority and was achieved mainly by using a two-phased approach for querying. The first stage focuses on retrieving all tuples and attributes that may be of interest, while in the second stage, users narrow down the set of tuples and select only the attributes that are actually needed.Although the presented VQS has been specifically developed for use with a student database, the key ideas are described in a generic way which should allow them to be used to query almost any database schema that has a many-to-many relationship set. Because OVI-2 was in active use for more than five years (over forty users had access to it), user feedback played a big role in this work. Many of the conclusions presented herein are based on observing users actually using OVI-2 to perform their timely query needs.     

基于UML的构件检索

基于构件的软件开发（CBD）是当前大型软件系统开发方法的主流，而CBD的基础是构件库及其检索方法。目前主要采用从领域梃型中获得特定领域知识辅助用户进行构件检索，但缺乏较好的领域模型表示方法。本文对使用UML表示领域模型进行了研究，提出了一个利用UML和领域词典中的领域知识辅助用户刻画领域、扩充和求精初始查询、形成用户的构件需求并指导构件库检索，通过行为相似性确定构件的构件检索方法。该方法增强了用户对领域知识的了解，在检索过程中充分考虑了与构件相关的领域知识、检索上下文以及用户的意图，可对结果集进行有效筛选评优，极大地提高了查全率、查准率及用户的满意度。为了验证该方法的可行性和有效性，设计并实现了一个高效的构件检索环境。     

Enabling concept-based relevance feedback for information retrievalon the WWW

The World Wide Web is a world of great richness, but finding information on the Web is also a great challenge. Keyword-based querying has been an immediate and efficient way to specify and retrieve related information that the user inquires. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given, as in most cases. In order to clarify the ambiguity of the short queries given by users, we propose the idea of concept-based relevance feedback for Web information retrieval. The idea is to have users give two to three times more feedback in the same amount of time that would be required to give feedback for conventional feedback mechanisms. Under this design principle, we apply clustering techniques to the initial search results to provide concept-based browsing. We show the performance of various feedback interface designs and compare their pros and cons. We measure precision and relative recall to show how clustering improves performance over conventional similarity ranking and, most importantly, we show how the assistance of concept-based presentation reduces browsing labor     

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     

Precision and recall of ranking information-filtering systems

An information-filtering system collects incoming data of specific interests described in a server profile. These systems can export their collections by submitting their profiles to a directory server, where users can query for relevant systems to answer their requests. We develop a new similarity measure to rank information-filtering systems for Boolean queries. Users can send queries to the top-ranked systems and obtain most of the relevant information. In contrast to an existing method developed by Radecki, our method requires less time and space complexity and has better recall and precision for higher-ranked systems.     

基于属性相关度的Web数据库大小估算方法

提出了一种基于词频统计的方法以估算Web数据库的规模.通过分析Web数据库查询接口中属性之间的相关度来获取某个属性上的一组随机样本;并对该属性分别提交由前k位高频词形成的试探查询以估算Web数据库中记录的总数.通过在几个真实的Web数据库上进行实验验证,说明该方法可以准确地估算出Web数据库的大小.     

Searching Musical Audio Using Symbolic Queries

Finding a piece of music based on its content is a key problem in music in for music information retrieval . For example, a user may be interested in finding music based on knowledge of only a small fragment of the overall tune. In this paper, we consider the searching of musical audio using symbolic queries. We first propose a relative pitch approach for representing queries and pieces. Experiments show that this technique, while effective, works best when the whole tune is used as a query. We then present an algorithm for matching based on a pitch classes approach, using the longest common subsequence between a query and target. Experimental evaluation shows that our technique is highly effective, with a mean average precision of 0.77 on a collection of 1808 recordings. Significantly, our technique is robust for truncated queries, being able to maintain effectiveness and to retrieve correct answers whether the query fragment is taken from the beginning, middle, or end of a piece. This represents a significant reduction in the burden placed on users when formulating queries.