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We explore new ways of improving a search engine using data from Web 2.0 applications such as blogs and social bookmarks. This data contains entities such as documents, people and tags, and relationships between them. We propose a simple yet effective method, based on faceted search, that treats all entities in a unified manner: returning all of them (documents, people and tags) on every search, and allowing all of them to be used as search terms. We describe an implementation of such a social search engine on the intranet of a large enterprise, and present large-scale experiments which verify the validity of our approach.

We describe a system for automating call-center analysis and monitoring. Our system integrates transcription of incoming calls with analysis of their content; for the analysis, we introduce a novel method of estimating the domain-specific importance of conversation fragments, based on divergence of corpus statistics. Combining this method with Information Retrieval approaches, we provide knowledge-mining tools both for the call-center agents and for administrators of the center.

This paper describes an evaluation method based on Term Relevance Sets Trels that measures an IR system's quality by examining the content of the retrieved results rather than by looking for pre-specified relevant pages. Trels consist of a list of terms believed to be relevant for a particular query as well as a list of irrelevant terms. The proposed method does not involve any document relevance judgments, and as such is not adversely affected by changes to the underlying collection. Therefore, it can better scale to very large, dynamic collections such as the Web. Moreover, this method can evaluate a system's effectiveness on an updatable "live" collection, or on collections derived from different data sources. Our experiments show that the proposed method is very highly correlated with official TREC measures.

We describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name refers to. In addition, it assigns to each page a geographic focus --- a locality that the page discusses as a whole. The tagging process is simple and fast, aimed to be applied to large collections of Web pages and to facilitate a variety of location-based applications and data analyses.Geotagging involves arbitrating two types of ambiguities: geo/non-geo and geo/geo. A geo/non-geo ambiguity occurs when a place name also has a non-geographic meaning, such as a person name (e.g., Berlin) or a common word (Turkey). Geo/geo ambiguity arises when distinct places have the same name, as in London, England vs. London, Ontario.An implementation of the tagger within the framework of the WebFountain data mining system is described, and evaluated on several corpora of real Web pages. Precision of up to 82% on individual geotags is achieved. We also evaluate the relative contribution of various heuristics the tagger employs, and evaluate the focus-finding algorithm using a corpus pretagged with localities, showing that as many as 91% of the foci reported are correct up to the country level.

Abstract Although time has been recognized as an important dimension in the co‐citation literature, to date it has not been incorporated into the analogous process of link analysis on the Web. In this paper, we discuss several aspects and uses of the time dimension in the context of Web information retrieval. We describe the ideal case—where search engines track and store temporal data for each of the pages in their repository, assigning timestamps to the hyperlinks embedded within the pages. We introduce several applications which benefit from the availability of such timestamps. To demonstrate our claims, we use a somewhat simplistic approach, which dates links by approximating the age of the page's content. We show that by using this crude measure alone it is possible to detect and expose significant events and trends. We predict that by using more robust methods for tracking modifications in the content of pages, search engines will be able to provide results that are more timely and better reflect current real‐life trends than those they provide today.

We describe a system for extracting mentions of terms such as company and product names, in a large and noisy corpus of documents, such as the World Wide Web. Since natural language terms are highly ambiguous, a significant challenge in this task is disambiguating which occurrences of each term are truly related to the right meaning, and which are not. We describe our approach for disambiguation, and show that it achieves very high accuracy with only limited training. This serves as a necessary first step for applications that strive to do analytics on term mentions.

We present an efficient query evaluation method based on a two level approach: at the first level, our method iterates in parallel over query term postings and identifies candidate documents using an approximate evaluation taking into account only partial information on term occurrences and no query independent factors; at the second level, promising candidates are fully evaluated and their exact scores are computed. The efficiency of the evaluation process can be improved significantly using dynamic pruning techniques with very little cost in effectiveness. The amount of pruning can be controlled by the user as a function of time allocated for query evaluation. Experimentally, using the TREC Web Track data, we have determined that our algorithm significantly reduces the total number of full evaluations by more than 90%, almost without any loss in precision or recall. At the heart of our approach there is an efficient implementation of a new Boolean construct called WAND or Weak AND that might be of independent interest.

Most of the work on XML query and search has stemmed from the publishing and database communities, mostly for the needs of business applications. Recently, the Information Retrieval community began investigating the XML search issue to answer information discovery needs. Following this trend, we present here an approach where information needs can be expressed in an approximate manner as pieces of XML documents or "XML fragments" of the same nature as the documents that are being searched. We present an extension of the vector space model for searching XML collections via XML fragments and ranking results by relevance. We describe how we have extended a full-text search engine to comply with this model. The value of the proposed method is demonstrated by the relative high precision of our system, which was among the top performers in the recent INEX workshop. Our results indicate that certain queries are more appropriate than others for the extended vector space model. Specifically, queries with relatively specific contexts but vague information needs are best situated to reap the benefit of this model. Finally our results show that one method may not fit all types of queries and that it could be worthwhile to use different solutions for different applications.

This work describes an automatic query refinement technique, which focuses on improving precision of the top ranked documents. The terms used for refinement are lexical affinities (LAs), pairs of closely related words which contain exactly one of the original query terms. Adding these terms to the query is equivalent to re-ranking search results, thus, precision is improved while recall is preserved. We describe a novel method that selects the most informative LAs for refinement, namely, those LAs that best separate relevant documents from irrelevant documents in the set of results. The information gain of candidate LAs is determined using unsupervised estimation that is based on the scoring function of the search engine. This method is thus fully automatic and its quality depends on the quality of the scoring function. Experiments we conducted with TREC data clearly show a significant improvement in the precision of the top ranked documents.

In spite of the increase in the availability of mobile devices in the last few years, Web information is not yet as accessible from PDAs or WAP phones as it is from the desktop. In this paper, we propose a solution for supporting one of the most popular information discovery mechanisms, namely Web directory navigation, from mobile devices. Our proposed solution consists of caching enough information on the device itself in order to conduct most of the navigation actions locally (with subsecond response time) while intermittently communicating with the server to receive updates and additional data requested by the user. The cached information is captured in a "directory capsule". The directory capsule represents only the portion of the directory that is of interest to the user in a given context and is sufficiently rich and consistent to support the information needs of the user in disconnected mode. We define a novel subscription model specifically geared for Web directories and for the special needs of PDAs. This subscription model enables users to specify the parts of the directory that are of interest to them as well as the preferred granularity. We describe a mechanism for keeping the directory capsule in sync over time with the Web directory and user subscription requests. Finally, we present the Pocket Directory Browser for Palm powered computers that we have developed. The pocket directory can be used to define, view and manipulate the capsules that are stored on the Palm. We provide several usage examples of our system on the Open Directory Project, one of the largest and most popular Web directories.

Mobile knowledge seekers often need access to information on the Web during a meeting or on the road, while away from their desktop. A common practice today is to use pervasive devices such as Personal Digital Assistants or mobile phones. However, these devices have inherent constraints (e.g., slow communication, form factor) which often make information discovery tasks impractical.In this paper, we present a new focused-search approach specifically oriented for the mode of work and the constraints dictated by pervasive devices. It combines focused search within specific topics with encapsulation of topic-specific information in a persistent repository. One key characteristic of these persistent repositories is that their footprint is small enough to fit on local devices, and yet they are rich enough to support many information discovery tasks in disconnected mode. More specifically, we suggest a representation for topic-specific information based on "knowledge-agent bases" that comprise all the information necessary to access information about a topic (under the form of key concepts and key Web pages) and assist in the full search process from query formulation assistance to result scanning on the device itself. The key contribution of our work is the coupling of focused search with encapsulated knowledge representation making information discovery from pervasive devices practical as well as efficient. We describe our model in detail and demonstrate its aspects through sample scenarios.

We describe PicASHOW, a fully automated WWW image retrieval system that is based on several link-structure analyzing algorithms. Our basic premise is that a pagepdisplays (or links to) an image when the author ofpconsiders the image to be of value to the viewers of the page. We thus extend some well known link-based WWWpage retrievalschemes to the context of image retrieval.PicASHOW's analysis of the link structure enables it to retrieve relevant images even when those are stored in files with meaningless names. The same analysis also allows it to identifyimage containersandimage hubs. We define these as Web pages that are rich in relevant images, or from which many images are readily accessible.PicASHOW requires no image analysis whatsoever and no creation of taxonomies for preclassification of the Web's images. It can be implemented by standard WWW search engines with reasonable overhead, in terms of both computations and storage, and with no change to user query formats. It can thus be used to easily add image retrieving capabilities to standard search engines.Our results demonstrate that PicASHOW, while relying almost exclusively on link analysis, compares well with dedicated WWW image retrieval systems. We conclude that link analysis, a proven effective technique for Web page search, can improve the performance of Web image retrieval, as well as extend its definition to include the retrieval of image hubs and containers.

We introduce static index pruning methods that significantly reduce the index size in information retrieval systems.We investigate uniform and term-based methods that each remove selected entries from the index and yet have only a minor effect on retrieval results. In uniform pruning, there is a fixed cutoff threshold, and all index entries whose contribution to relevance scores is bounded above by a given threshold are removed from the index. In term-based pruning, the cutoff threshold is determined for each term, and thus may vary from term to term. We give experimental evidence that for each level of compression, term-based pruning outperforms uniform pruning, under various measures of precision. We present theoretical and experimental evidence that under our term-based pruning scheme, it is possible to prune the index greatly and still get retrieval results that are almost as good as those based on the full index.

Article Knowledge encapsulation for focused search from pervasive devices Share on Authors: Yariv Aridor IBM Research Lab in Haifa, Matam, Haifa 31905, Israel IBM Research Lab in Haifa, Matam, Haifa 31905, IsraelView Profile , David Carmel IBM Research Lab in Haifa, Matam, Haifa 31905, Israel IBM Research Lab in Haifa, Matam, Haifa 31905, IsraelView Profile , Yoëlle S. Maarek IBM Research Lab in Haifa, Matam, Haifa 31905, Israel IBM Research Lab in Haifa, Matam, Haifa 31905, IsraelView Profile , Aya Soffer IBM Research Lab in Haifa, Matam, Haifa 31905, Israel IBM Research Lab in Haifa, Matam, Haifa 31905, IsraelView Profile , Ronny Lempel Department of Computer Science, Technion, Haifa 32000, Israel Department of Computer Science, Technion, Haifa 32000, IsraelView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 754–764https://doi.org/10.1145/371920.372195Online:01 April 2001Publication History 8citation368DownloadsMetricsTotal Citations8Total Downloads368Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Article Share on PicASHOW: pictorial authority search by hyperlinks on the Web Authors: Ronny Lempel Department of Computer Science, The Technion, Haifa 32000, Israel Department of Computer Science, The Technion, Haifa 32000, IsraelView Profile , Aya Soffer IBM Research Lab in Haifa, Matam, Haifa 31905, Israel IBM Research Lab in Haifa, Matam, Haifa 31905, IsraelView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 438–448https://doi.org/10.1145/371920.372098Online:01 April 2001Publication History 37citation449DownloadsMetricsTotal Citations37Total Downloads449Last 12 Months15Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

An algorithm is given for processing pictorial query specifications that consist of a query image and a similarity level that must hold between the query image and database images. The similarity level specifies the contextual similarity (how well the content of one image matches that of another) as well as the spatial similarity (the relative locations of the matching symbols in the two images). The algorithm differs from previous approaches in its ability to handle multiple instances of each object in both the query and database images by searching for isomorphic subgraphs. The running time of the algorithm is O(m 2/sup m/) in the worst case where all symbols in both the query and database image are from the same class, but falls far below this bound in the presence of spatial constraints.

A method for representing and matching logos based on positive and negative shape features is presented. Negative shape features represent an object that consists of several components enclosed in a simple geometric structure (e.g., a square) based on its interior with the components considered as holes. The goal is to find logos in a database that are most similar to a given sample logo. A border is added around logos that are not enclosed in a simple shape. Logos are segmented. Local and global shape features are computed for each component. Two methods for comparing logos represented by positive and negative components are presented and evaluated.

A technique that enables specifying complex queries in image databases using pictorial query trees is presented. The leaves of a pictorial query tree correspond to individual pictorial queries that specify which objects should appear in the target images as well as how many occurrences of each object are required. In addition, the minimum required certainty of matching between query-image objects and database-image objects, as well as spatial constraints that specify bounds on the distance between objects and the relative direction between them are also specified. Internal nodes in the query tree represent logical operations (AND, OR, XOR) and their negations on the set of pictorial queries (or subtrees) represented by its children. The syntax of query trees is described. Algorithms for processing individual pictorial queries and for parsing and computing the overall result of a pictorial query tree are outlined.

A method for finding all images from the same category as a given query image (termed 'categorization') using texture features is presented. The hypothesis that two images that are similar in texture are likely to belong to the same category is examined. A new texture feature called an N/spl times/M-gram is presented. It is based on the N-gram technique that is commonly used for text similarity. The process of computing an image profile in terms of its N/spl times/M-grams is described. Results of experiments on images from various categories are presented. The N/spl times/M-gram method with three different similarity measures is compared to the results of categorization using other well-known texture features and grey-level distribution features. The results show that, for our test images, texture features are suitable for image categorization, and N/spl times/M-gram based methods are the best overall choice of texture features for this task.

A method for specifying pictorial queries to an image database is introduced. A pictorial query specification consists of a query image, and a similarity level that specifies the required extent of similarity between the query image and database images that are to be retrieved. The query image is constructed by positioning objects so that the desired locational and spatial constraints hold. Two image similarity factors are considered: (1) contextual similarity: how well does the content of one image match that of another; (2) spatial similarity: the relative locations of the matching symbols in the two images. Algorithms for retrieving all database images that conform to a given pictorial query specification are presented and compared.

A system named MARCO (denoting map retrieval by content) that is used for the acquisition, storage, indexing, and retrieval of map images is presented. The input to MARCO are raster images of separate map layers and raster images of map composites. A legend-driven map interpretation system converts map layer images from their physical representation to their logical representation. This logical representation is then used to automatically index both the composite and the layer images. Methods for incorporating logical and physical layer images as well as composite images into the framework of a relational database management system are described. Indices are constructed on both the contextual and the spatial data thereby enabling efficient retrieval of layer and composite images based on contextual as well as spatial specifications. Example queries and query processing strategies using these indices are described. The user interface is demonstrated via the execution of an example query. Results of an experimental study on a large amount of data are presented. The system is evaluated in terms of accuracy and in terms of query execution time.

A system for the acquisition, storage, indexing, and retrieval of map images is presented. The input to this system are raster images of separate map layers and map composites. A legend driven map interpretation system converts layer images from a physical to a logical representation. This logical representation is used to automatically index both the composite and the layer images. Methods for incorporating logical and physical layers as well as composite images into the framework of a relational database management system are described. An example query and a corresponding query processing strategy that uses these indices is presented. The user interface is demonstrated via an example query execution.

A system is presented that utilizes the symbolic knowledge found in the legend of the map to drive geographic symbol recognition. The geographic symbol layer(s) of the map are first scanned. The legend of the map is located and segmented. The geographic symbols are identified and their semantic meaning is attached to them. The geographical symbols in input maps are classified using statistical pattern recognition. An experimental study was conducted on a large amount of data with recognition rates of over 95%.