Search (34 results, page 1 of 2)

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Abstract

Understanding what kinds of Web pages are the most useful for Web search engine users is a critical task in Web information retrieval (IR). Most previous works used hyperlink analysis algorithms to solve this problem. However, little research has been focused on query-independent Web data cleansing for Web IR. In this paper, we first provide analysis of the differences between retrieval target pages and ordinary ones based on more than 30 million Web pages obtained from both the Text Retrieval Conference (TREC) and a widely used Chinese search engine, SOGOU (www.sogou.com). We further propose a learning-based data cleansing algorithm for reducing Web pages that are unlikely to be useful for user requests. We found that there exists a large proportion of low-quality Web pages in both the English and the Chinese Web page corpus, and retrieval target pages can be identified using query-independent features and cleansing algorithms. The experimental results showed that our algorithm is effective in reducing a large portion of Web pages with a small loss in retrieval target pages. It makes it possible for Web IR tools to meet a large fraction of users' needs with only a small part of pages on the Web. These results may help Web search engines make better use of their limited storage and computation resources to improve search performance.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

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Abstract

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

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Abstract

Das World Wide Web kann als die größte uns bekannte "Datenbank" angesehen werden. Leider ist das heutige Web großteils auf die Präsentation für menschliche Benutzerinnen ausgelegt und besteht aus sehr heterogenen Datenbeständen. Überdies fehlen im Web die Möglichkeiten Informationen strukturiert und aus verschiedenen Quellen aggregiert abzufragen. Das heutige Web ist daher für die automatische maschinelle Verarbeitung nicht geeignet. Um Webdaten dennoch effektiv zu nutzen, wurden Sprachen, Methoden und Werkzeuge zur Extraktion und Aggregation dieser Daten entwickelt. Dieser Artikel gibt einen Überblick und eine Kategorisierung von verschiedenen Ansätzen zur Datenextraktion aus dem Web. Einige Beispielszenarien im B2B Datenaustausch, im Business Intelligence Bereich und insbesondere die Generierung von Daten für Semantic Web Ontologien illustrieren die effektive Nutzung dieser Technologien.

Source

Semantic Web: Wege zur vernetzten Wissensgesellschaft. Hrsg.: T. Pellegrini, u. A. Blumauer

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Abstract

The amount of information on the Web has been expanding at an enormous pace. There are a variety of Web documents in different genres, such as news, reports, reviews. Traditionally, the information displayed on Web sites has been static. Recently, there are many Web sites offering content that is dynamically generated and frequently updated. It is also common for Web sites to contain information in different languages since many countries adopt more than one language. Moreover, content may exist in multimedia formats including text, images, video, and audio.

Footnote

Einführung in einen Themenschwerpunkt "Mining Web resources for enhancing information retrieval"

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Abstract

"Garbage in, garbage out" is a well-known phrase in computer analysis, and one that comes to mind when mining Web data to draw conclusions about Web users. The challenge is that data analysts wish to infer patterns of client-side behavior from server-side data. However, because only a fraction of the user's actions ever reaches the Web server, analysts must rely an incomplete data. In this paper, we propose a client-side monitoring system that is unobtrusive and supports flexible data collection. Moreover, the proposed framework encompasses client-side applications beyond the Web browser. Expanding monitoring beyond the browser to incorporate standard office productivity tools enables analysts to derive a much richer and more accurate picture of user behavior an the Web.

Footnote

Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"

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Abstract

We present an approach to enhancing information access through Web structure mining in contrast to traditional approaches involving usage mining. Specifically, we mine the hardwired hierarchical hyperlink structure of Web sites to identify patterns of term-term co-occurrences we call Web functional dependencies (FDs). Intuitively, a Web FD x -> y declares that all paths through a site involving a hyperlink labeled x also contain a hyperlink labeled y. The complete set of FDs satisfied by a site help characterize (flexible and expressive) interaction paradigms supported by a site, where a paradigm is the set of explorable sequences therein. We describe algorithms for mining FDs and results from mining several hierarchical Web sites and present several interface designs that can exploit such FDs to provide compelling user experiences.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

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Abstract

The dynamic nature and size of the Internet can result in difficulty finding relevant information. Most users typically express their information need via short queries to search engines and they often have to physically sift through the search results based on relevance ranking set by the search engines, making the process of relevance judgement time-consuming. In this paper, we describe a novel representation technique which makes use of the Web structure together with summarisation techniques to better represent knowledge in actual Web Documents. We named the proposed technique as Semantic Virtual Document (SVD). We will discuss how the proposed SVD can be used together with a suitable clustering algorithm to achieve an automatic content-based categorization of similar Web Documents. The auto-categorization facility as well as a "Tree-like" Graphical User Interface (GUI) for post-retrieval document browsing enhances the relevance judgement process for Internet users. Furthermore, we will introduce how our cluster-biased automatic query expansion technique can be used to overcome the ambiguity of short queries typically given by users. We will outline our experimental design to evaluate the effectiveness of the proposed SVD for representation and present a prototype called iSEARCH (Intelligent SEarch And Review of Cluster Hierarchy) for Web content mining. Our results confirm, quantify and extend previous research using Web structure and summarisation techniques, introducing novel techniques for knowledge representation to enhance Web content mining.

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Footnote

Rez. in: JASIST 55(2004) no.3, S.275-276 (C. Chen): "This is a book about finding significant statistical patterns on the Web - in particular, patterns that are associated with hypertext documents, topics, hyperlinks, and queries. The term pattern in this book refers to dependencies among such items. On the one hand, the Web contains useful information an just about every topic under the sun. On the other hand, just like searching for a needle in a haystack, one would need powerful tools to locate useful information an the vast land of the Web. Soumen Chakrabarti's book focuses an a wide range of techniques for machine learning and data mining an the Web. The goal of the book is to provide both the technical Background and tools and tricks of the trade of Web content mining. Much of the technical content reflects the state of the art between 1995 and 2002. The targeted audience is researchers and innovative developers in this area, as well as newcomers who intend to enter this area. The book begins with an introduction chapter. The introduction chapter explains fundamental concepts such as crawling and indexing as well as clustering and classification. The remaining eight chapters are organized into three parts: i) infrastructure, ii) learning and iii) applications.
Part I, Infrastructure, has two chapters: Chapter 2 on crawling the Web and Chapter 3 an Web search and information retrieval. The second part of the book, containing chapters 4, 5, and 6, is the centerpiece. This part specifically focuses an machine learning in the context of hypertext. Part III is a collection of applications that utilize the techniques described in earlier chapters. Chapter 7 is an social network analysis. Chapter 8 is an resource discovery. Chapter 9 is an the future of Web mining. Overall, this is a valuable reference book for researchers and developers in the field of Web mining. It should be particularly useful for those who would like to design and probably code their own Computer programs out of the equations and pseudocodes an most of the pages. For a student, the most valuable feature of the book is perhaps the formal and consistent treatments of concepts across the board. For what is behind and beyond the technical details, one has to either dig deeper into the bibliographic notes at the end of each chapter, or resort to more in-depth analysis of relevant subjects in the literature. lf you are looking for successful stories about Web mining or hard-way-learned lessons of failures, this is not the book."

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Abstract

Als in den siebziger Jahren des vergangenen Jahrhunderts immer häufiger die Bezeichnung Informationsmanager für Leute propagiert wurde, die bis dahin als Dokumentare firmierten, wurde dies in den etablierten Kreisen der Archivare und Bibliothekare gelegentlich belächelt und als Zeichen einer Identitätskrise oder jedenfalls einer Verunsicherung des damit überschriebenen Berufsbilds gewertet. Für den Berufsstand der Medienarchivare/Mediendokumentare, die sich seit 1960 in der Fachgruppe 7 des Vereins, später Verbands deutscher Archivare (VdA) organisieren, gehörte diese Verortung im Zeichen neuer inhaltlicher Herausforderungen (Informationsflut) und Technologien (EDV) allerdings schon früh zu den Selbstverständlichkeiten des Berufsalltags. "Halt, ohne uns geht es nicht!" lautete die Überschrift eines Artikels im Verbandsorgan "Info 7", der sich mit der Einrichtung von immer mächtigeren Leitungsnetzen und immer schnelleren Datenautobahnen beschäftigte. Information, Informationsgesellschaft: diese Begriffe wurden damals fast nur im technischen Sinne verstanden. Die informatisierte, nicht die informierte Gesellschaft stand im Vordergrund - was wiederum Kritiker auf den Plan rief, von Joseph Weizenbaum in den USA bis hin zu den Informations-Ökologen in Bremen. Bei den nationalen, manchmal auch nur regionalen Projekten und Modellversuchen mit Datenautobahnen - auch beim frühen Btx - war nie so recht deutlich geworden, welche Inhalte in welcher Gestalt durch diese Netze und Straßen gejagt werden sollten und wer diese Inhalte eigentlich selektieren, portionieren, positionieren, kurz: managen sollte. Spätestens mit dem World Wide Web sind diese Projekte denn auch obsolet geworden, jedenfalls was die Hardware und Software anging. Geblieben ist das Thema Inhalte (neudeutsch: Content). Und - immer drängender im nicht nur technischen Verständnis - das Thema Informationsmanagement. MedienInformationsManagement war die Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar überschrieben, und auch die Folgetagung 2001 in Köln, die der multimedialen Produktion einen dokumentarischen Pragmatismus gegenüber stellte, handelte vom Geschäftsfeld Content und von Content-Management-Systemen. Die in diesem 6. Band der Reihe Beiträge zur Mediendokumentation versammelten Vorträge und Diskussionsbeiträge auf diesen beiden Tagungen beleuchten das Titel-Thema aus den verschiedensten Blickwinkeln: archivarischen, dokumentarischen, kaufmännischen, berufsständischen und juristischen. Deutlich wird dabei, daß die Berufsbezeichnung Medienarchivarln/Mediendokumentarln ziemlich genau für all das steht, was heute mit sog. alten wie neuen Medien im organisatorischen, d.h. ordnenden und vermittelnden Sinne geschieht. Im besonderen Maße trifft dies auf das Internet und die aus ihm geborenen Intranets zu. Beide bedürfen genauso der ordnenden Hand, die sich an den alten Medien, an Buch, Zeitung, Tonträger, Film etc. geschult hat, denn sie leben zu großen Teilen davon. Daß das Internet gleichwohl ein Medium sui generis ist und die alten Informationsberufe vor ganz neue Herausforderungen stellt - auch das durchzieht die Beiträge von Weimar und Köln.

Date

11. 5.2008 19:49:22

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Abstract

In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

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Abstract

External information plays an important role in group decision-making processes, yet research about external information support for Group Support Systems (GSS) has been lacking. In this study, we propose an approach to build a concept space to provide external concept support for GSS users. Built on a Web mining algorithm, the approach can mine a concept space from the Web and retrieve related concepts from the concept space based on users' comments in a real-time manner. We conduct two experiments to evaluate the quality of the proposed approach and the effectiveness of the external concept support provided by this approach. The experiment results indicate that the concept space mined from the Web contained qualified concepts to stimulate divergent thinking. The results also demonstrate that external concept support in GSS greatly enhanced group productivity for idea generation tasks.

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Abstract

Web Mining - ein Schlagwort, das mit der Verbreitung des Internets immer öfter zu lesen und zu hören ist. Die gegenwärtige Forschung beschäftigt sich aber eher mit dem Nutzungsverhalten der Internetnutzer, und ein Blick in Tagungsprogramme einschlägiger Konferenzen (z.B. GOR - German Online Research) zeigt, dass die Analyse der Inhalte kaum Thema ist. Auf der GOR wurden 1999 zwei Vorträge zu diesem Thema gehalten, auf der Folgekonferenz 2001 kein einziger. Web Mining ist der Oberbegriff für zwei Typen von Web Mining: Web Usage Mining und Web Content Mining. Unter Web Usage Mining versteht man das Analysieren von Daten, wie sie bei der Nutzung des WWW anfallen und von den Servern protokolliert wenden. Man kann ermitteln, welche Seiten wie oft aufgerufen wurden, wie lange auf den Seiten verweilt wurde und vieles andere mehr. Beim Web Content Mining wird der Inhalt der Webseiten untersucht, der nicht nur Text, sondern auf Bilder, Video- und Audioinhalte enthalten kann. Die Software für die Analyse von Webseiten ist in den Grundzügen vorhanden, doch müssen die meisten Webseiten für die entsprechende Analysesoftware erst aufbereitet werden. Zuerst müssen die relevanten Websites ermittelt werden, die die gesuchten Inhalte enthalten. Das geschieht meist mit Suchmaschinen, von denen es mittlerweile Hunderte gibt. Allerdings kann man nicht davon ausgehen, dass die Suchmaschinen alle existierende Webseiten erfassen. Das ist unmöglich, denn durch das schnelle Wachstum des Internets kommen täglich Tausende von Webseiten hinzu, und bereits bestehende ändern sich der werden gelöscht. Oft weiß man auch nicht, wie die Suchmaschinen arbeiten, denn das gehört zu den Geschäftsgeheimnissen der Betreiber. Man muss also davon ausgehen, dass die Suchmaschinen nicht alle relevanten Websites finden (können). Der nächste Schritt ist das Herunterladen der Websites, dafür gibt es Software, die unter den Bezeichnungen OfflineReader oder Webspider zu finden ist. Das Ziel dieser Programme ist, die Website in einer Form herunterzuladen, die es erlaubt, die Website offline zu betrachten. Die Struktur der Website wird in der Regel beibehalten. Wer die Inhalte einer Website analysieren will, muss also alle Dateien mit seiner Analysesoftware verarbeiten können. Software für Inhaltsanalyse geht davon aus, dass nur Textinformationen in einer einzigen Datei verarbeitet werden. QDA Software (qualitative data analysis) verarbeitet dagegen auch Audiound Videoinhalte sowie internetspezifische Kommunikation wie z.B. Chats.

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Abstract

With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize and represent their information needs using simple queries. Users have to keep modifying their input queries until they get expected results. Therefore, it is often desirable for search engines to give suggestions on related queries to users. Besides, by identifying those related queries, search engines can potentially perform optimizations on their systems, such as query expansion and file indexing. In this work we propose a method that suggests a list of related queries given an initial input query. The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules. Users can utilize the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it also performs reasonably well on less frequent input queries.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

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Abstract

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

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Abstract

Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

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Abstract

This research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based an facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model an test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Webbased scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.

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Abstract

With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"