Conceptual query formulation and retrieval

In this paper, we advance a technique to develop a user profile for information retrieval through knowledge acquisition techniques. The profile bridges the discrepancy between user-expressed keywords and system-recognizable index terms. The approach presented in this paper is based on the application of personal construct theory to determine a user's vocabulary and his/her view of different documents in a training set. The elicited knowledge is used to develop a model for each phrase/concept given by the user by employing machine learning techniques.Our model correlates the concepts in a user's vocabulary to the index terms present in the documents in the training set. Computation of dependence between the user phrases also contributes in the development of the user profile and in creating a classification of documents. The resulting system is capable of automatically identifying the user concepts and query translation to index terms computed by the conventional indexing process. The system is evaluated by using the standard measures of precision and recall by comparing its performance against the performance of the smart system for different queries.This research is supported by the NSF grant IRI-8805875.     

中文搜索引擎查询与反馈词语特征研究

查询式是网络用户搜索时表达其信息需求的主要方式,系统提示的相关词则是用户改善查询的有效工具,该文以这二者为研究对象,从用户的使用行为入手对这二者的特征进行刻画和分析。首先使用日志挖掘的方法,对查询式进行总体的定量描述;进而通过定性分类将查询式中的高频词分为主体词和辅助词两大类,并比照问卷调查的研究结果,发现网络用户在搜索时大量地使用辅助词,主体词的内容相对集中,查询式的长度较短,结构相对简单。在对相关词的研究中,综合问卷调查和对比实验研究结果,发现被试者对搜索引擎提示的相关词认同程度高而应用程度低。该文为理解网络用户搜索时的语言使用提供了实证研究结果,并对搜索引擎索引的改善有一定的参考意义。     

面向查询意图的信息检索技术

用户兴趣和行为的多样性使得为不同用户提供更符合其查询意图的搜索结果成为一个具有挑战性的任务.Web 2.0下的社会标签是用户为他们感兴趣的网页等对象进行标注行为的结果,用户用标签来描述自己感兴趣的话题.这些标签不但代表着用户的兴趣,而且是对网页承载信息的最好揭示.提出了面向用户查询意图的标签推荐方法,旨在把能够体现用户真正查询意图的标签选择出来.标签作为对查询关键词的补充,不仅可以弥补用户短查询的缺陷,而且可以根据标签与网页上曾被标注过的标签间的关系,更准确地判断用户查询意图与网页内容之间的相关度,从而把更符合用户查询兴趣的结果排在靠前的位置上.实验结果表明,该方法比现有的其他方法更有效,这也说明社会标注对更准确地捕捉用户真实查询意图确实有重要作用.     

基于粗糙集的个性化Web搜索系统

本文提出了一种基于粗糙集理论的个性化web搜索系统。用户偏好文件中对关键字进行分组以表示用户兴趣类别。利用粗糙集理论处理自然语言的内在含糊性,根据用户偏好文件对查询条件进行扩展。搜索组件使用扩展后的查询条件搜索相关信息。为了进一步排除不相关信息,排序组件计算查询条件和搜索结果之间的相似程度,根据计算值对搜索结果进行排序。与传统搜索引擎进行了比较,实验结果表明,该系统有效地提高了搜索结果的精度,满足了用户的个性化需求。     

Look where you're going [robotic wheelchair]

We propose a robotic wheelchair that observes the user and the environment. It can understand the user's intentions from his/her behaviors and the environmental information. It also observes the user when he/she is off the wheelchair, recognizing the user's commands indicated by hand gestures. Experimental results show our approach to be promising. Although the current system uses face direction, for people who find it difficult to move their faces, it can be modified to use the movements of the mouth, eyes, or any other body parts that they can move. Since such movements are generally noisy, the integration of observing the user and the environment will be effective in understanding the real intentions of the user and will be a useful technique for better human interfaces.     

GPE:一种基于图模型的NFS有意义结果确定模型

XML非完全结构查询(NFS)允许用户利用部分XML结构信息,甚至仅仅是关键字来描述查询要求,是在缺乏完整的XML文档结构信息情况下的重要查询手段.针对图模型下的NFS有意义结果判断问题,在PE模型基础上提出一种基于图的有意义结果判断模型GPE,包括结果粒度、模式实体定义、等价模式定义和判断规则;针对标签歧义性和复杂的结构语义,GPE提出一种结合基于领域字典的语境受限的标签语义相似性和模式结构相似性的等价模式计算方法.通过在实际数据集和XML实验数据上的实验表明,GPE模型在查准率和查全率上均有较大提高.     

Personalized Web search for improving retrieval effectiveness

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.     

Incremental mining of information interest for personalized web scanning

Businesses and people often organize their information of interest (IOI) into a hierarchy of folders (or categories). The personalized folder hierarchy provides a natural way for each of the users to manage and utilize his/her IOI (a folder corresponds to an interest type). Since the interest is relatively long-term, continuous web scanning is essential. It should be directed by precise and comprehensible specifications of the interest. A precise specification may direct the scanner to those spaces that deserve scanning, while a specification comprehensible to the user may facilitate manual refinement, and a specification comprehensible to information providers (e.g. Internet search engines) may facilitate the identification of proper seed sites to start scanning. However, expressing such specifications is quite difficult (and even implausible) for the user, since each interest type is often implicitly and collectively defined by the content (i.e. documents) of the corresponding folder, which may even evolve over time. In this paper, we present an incremental text mining technique to efficiently identify the user's current interest by mining the user's information folders. The specification mined for each interest type specifies the context of the interest type in conjunctive normal form, which is comprehensible to general users and information providers. The specification is also shown to be more precise in directing the scanner to those sites that are more likely to provide IOI. The user may thus maintain his/her folders and then constantly get IOI, without paying much attention to the difficult tasks of interest specification and seed identification.     

OLAP系统基于查询结构的用户浏览引导

联机分析处理（OLAP）系统是数据仓库主要的前端支持工具,在OLAP系统中用户以浏览的方式进行数据访问。通常,OLAP系统用户一般会有相对稳定的信息需求,而OLAP系统中查询的结构一定程度上反映了用户所关心的信息内容,因此,用户执行查询的结构也会保持一定的稳定性。以查询结构为基础,对OLAP系统用户的查询行为进行了分析,提出了一种建立OLAP系统用户轮廓文件的方法,并对如何根据轮廓文件对用户的行为进行预测,并进一步对用户的浏览进行引导的方法进行了探讨。以此为基础,当OLAP系统用户进行信息浏览时,可以在OLAP系统前端,对用户可能感兴趣的地方做出一定的标识,引导用户将要进行的浏览动作,使用户能轻松的完成信息搜索的工作。     

Enhancing Concept-Based Retrieval Based on Minimal Term Sets

There is considerable interest in bridging the terminological gap that exists between the way users prefer to specify their information needs and the way queries are expressed in terms of keywords or text expressions that occur in documents. One of the approaches proposed for bridging this gap is based on technologies for expert systems. The central idea of such an approach was introduced in the context of a system called Rule Based Information Retrieval by Computer (RUBRIC). In RUBRIC, user query topics (or concepts) are captured in a rule base represented by an AND/OR tree. The evaluation of AND/OR tree is essentially based on minimum and maximum weights of query terms for conjunctions and disjunctions, respectively. The time to generate the retrieval output of AND/OR tree for a given query topic is exponential in number of conjunctions in the DNF expression associated with the query topic. In this paper, we propose a new approach for computing the retrieval output. The proposed approach involves preprocessing of the rule base to generate Minimal Term Sets (MTSs) that speed up the retrieval process. The computational complexity of the on-line query evaluation following the preprocessing is polynomial in m. We show that the computation and use of MTSs allows a user to choose query topics that best suit their needs and to use retrieval functions that yield a more refined and controlled retrieval output than is possible with the AND/OR tree when document terms are binary. We incorporate p-Norm model into the process of evaluating MTSs to handle the case where weights of both documents and query terms are non-binary.     

Beyond Single-Page Web Search Results

Given a user keyword query, current Web search engines return a list of individual Web pages ranked by their "goodness" with respect to the query. Thus, the basic unit for search and retrieval is an individual page, even though information on a topic is often spread across multiple pages. This degrades the quality of search results, especially for long or uncorrelated (multitopic) queries (in which individual keywords rarely occur together in the same document), where a single page is unlikely to satisfy the user's information need. We propose a technique that, given a keyword query, on the fly generates new pages, called composed pages, which contain all query keywords. The composed pages are generated by extracting and stitching together relevant pieces from hyperlinked Web pages and retaining links to the original Web pages. To rank the composed pages, we consider both the hyperlink structure of the original pages and the associations between the keywords within each page. Furthermore, we present and experimentally evaluate heuristic algorithms to efficiently generate the top composed pages. The quality of our method is compared to current approaches by using user surveys. Finally, we also show how our techniques can be used to perform query-specific summarization of Web pages.     

基于改进决策树算法的Web数据库查询结果自动分类方法

为了解决Web数据库多查询结果问题,提出了一种基于改进决策树算法的Web数据库查询结果自动分类方法.该方法在离线阶段分析系统中所有用户的查询历史并聚合语义上相似的查询,根据聚合的查询将原始数据划分成多个元组聚类,每个元组聚类对应一种类型的用户偏好.当查询到来时,基于离线阶段划分的元组聚类,利用改进的决策树算法在查询结果集上自动构建一个带标签的分层分类树,使得用户能够通过检查标签的方式快速选择和定位其所需信息.实验结果表明,提出的分类方法具有较低的搜索代价和较好的分类效果,能够有效地满足不同类型用户的个性化查询需求.     

Keyword proximity search in XML trees

Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare's plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword, proximity queries to return the (possibly heterogeneous) set of minimum connecting trees (MCTs) of the matches to the individual keywords in the query. We consider efficiently executing keyword proximity queries on labeled trees (XML) in various settings: 1) when the XML database has been preprocessed and 2) when no indices are available on the XML database. We perform a detailed experimental evaluation to study the benefits of our approach and show that our algorithms considerably outperform prior algorithms and other applicable approaches.     

Learning similarity matching in multimedia content-based retrieval

Many multimedia content-based retrieval systems allow query formulation with the user setting the relative importance of features (e.g., color, texture, shape, etc.) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. We present a neural network-based learning algorithm for adapting the similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results     

Exploiting Background Information in Knowledge Discovery from Text

This paper describes the FACT system for knowledge discovery fromtext. It discovers associations—patterns ofco-occurrence—amongst keywords labeling the items in a collection oftextual documents. In addition, when background knowledge is available aboutthe keywords labeling the documents FACT is able to use this information inits discovery process. FACT takes a query-centered view of knowledgediscovery, in which a discovery request is viewed as a query over theimplicit set of possible results supported by a collection of documents, andwhere background knowledge is used to specify constraints on the desiredresults of this query process. Execution of a knowledge-discovery query isstructured so that these background-knowledge constraints can be exploitedin the search for possible results. Finally, rather than requiring a user tospecify an explicit query expression in the knowledge-discovery querylanguage, FACT presents the user with a simple-to-use graphical interface tothe query language, with the language providing a well-defined semantics forthe discovery actions performed by a user through the interface.     

Hyperlinking reality via camera phones

A novel user interface concept for camera phones, called “Hyperlinking Reality via Camera Phones”, that we present in this article, provides a solution to one of the main challenges facing mobile user interfaces, that is, the problem of selection and visualization of actions that are relevant to the user in her current context. Instead of typing keywords on a small and inconvenient keypad of a mobile device, a user of our system just snaps a photo of her surroundings and objects in the image become hyperlinks to information. Our method commences by matching a query image to reference panoramas depicting the same scene that were collected and annotated with information beforehand. Once the query image is related to the reference panoramas, we transfer the relevant information from the reference panoramas to the query image. By visualizing the information on the query image and displaying it on the camera phone’s (multi-)touch screen, the query image augmented with hyperlinks allows the user intuitive access to information.     

A Graphical Model for Context-Aware Visual Content Recommendation

Existing recommender systems provide an elegant solution to the information overload in current digital libraries such as the Internet archive. Nowadays, the sensors that capture the user's contextual information such as the location and time are become available and have raised a need to personalize recommendations for each user according to his/her changing needs in different contexts. In addition, visual documents have richer textual and visual information that was not exploited by existing recommender systems. In this paper, we propose a new framework for context-aware recommendation of visual documents by modeling the user needs, the context and also the visual document collection together in a unified model. We address also the user's need for diversified recommendations. Our pilot study showed the merits of our approach in content based image retrieval.     

Recommendation from access logs with ensemble learning

Many recommendation systems find similar users based on a profile of a target user and recommend products that he/she may be interested in. The profile is constructed with his/her purchase histories. However, histories of new customers are not stored and it is difficult to recommend products to them in the same fashion. The problem is called a cold start problem. We propose a recommendation method using access logs instead of purchase histories, because the access logs are gathered more easily than purchase histories and the access logs include much information on their interests. In this study, we construct user’s profiles using product categories browsed by them from their access logs and predict products with Gradient Boosting Decision Tree. In addition, we carry out evaluation experiments using access logs in a real online shop and discuss performance of our proposed method comparing with conventional machine learning and Support Vector Machine (SVM). We confirmed that the proposed method achieved higher precision than SVM over 10 data sets. Especially, under unbalanced data sets, the proposed method is superior to SVM.     

个性化信息检索系统中用户兴趣模型的建立

个性化检索系统通过收集和分析用户信息来学习用户的兴趣和行为,从而实现对用户的个性化的信息推荐服务。而用户兴趣模型正是用户和兴趣的信息模型,用户兴趣模型直接影响到个性化的信息服务。     

Finding interesting patterns using user expectations

One of the major problems in the field of knowledge discovery (or data mining) is the interestingness problem. Past research and applications have found that, in practice, it is all too easy to discover a huge number of patterns in a database. Most of these patterns are actually useless or uninteresting to the user. But due to the huge number of patterns, it is difficult for the user to comprehend them and to identify those interesting to him/her. To prevent the user from being overwhelmed by the large number of patterns, techniques are needed to rank them according to their interestingness. In this paper, we propose such a technique, called the user-expectation method. In this technique, the user is first asked to provide his/her expected patterns according to his/her past knowledge or intuitive feelings. Given these expectations, the system uses a fuzzy matching technique to match the discovered patterns against the user's expectations, and then rank the discovered patterns according to the matching results. A variety of rankings can be performed for different purposes, such as to confirm the user's knowledge and to identify unexpected patterns, which are by definition interesting. The proposed technique is general and interactive