-
Intelligent information processing and web mining : Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane, Poland, June 2-5, 2003 (2003)
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-
Li, J.; Zhang, P.; Cao, J.: External concept support for group support systems through Web mining (2009)
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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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Klein, H.: Web Content Mining (2004)
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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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Shi, X.; Yang, C.C.: Mining related queries from Web search engine query logs using an improved association rule mining model (2007)
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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"
-
Wang, F.L.; Yang, C.C.: Mining Web data for Chinese segmentation (2007)
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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"
-
Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007)
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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"
-
Nicholson, S.: Bibliomining for automated collection development in a digital library setting : using data mining to discover Web-based scholarly research works (2003)
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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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Liu, Y.; Huang, X.; An, A.: Personalized recommendation with adaptive mixture of markov models (2007)
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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"
-
Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999)
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- Date
- 17. 7.2002 19:22:06
-
Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997)
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- Source
- Information systems. 22(1997) nos.5/6, S.333-347
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Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002)
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- Content
- Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.
- RSWK
- World Wide Web / Wissensorganisation / Kongress / Passau <2000>
- Subject
- World Wide Web / Wissensorganisation / Kongress / Passau <2000>
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Whittle, M.; Eaglestone, B.; Ford, N.; Gillet, V.J.; Madden, A.: Data mining of search engine logs (2007)
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- Abstract
- This article reports on the development of a novel method for the analysis of Web logs. The method uses techniques that look for similarities between queries and identify sequences of query transformation. It allows sequences of query transformations to be represented as graphical networks, thereby giving a richer view of search behavior than is possible with the usual sequential descriptions. We also perform a basic analysis to study the correlations between observed transformation codes, with results that appear to show evidence of behavior habits. The method was developed using transaction logs from the Excite search engine to provide a tool for an ongoing research project that is endeavoring to develop a greater understanding of Web-based searching by the general public.
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Kulathuramaiyer, N.; Maurer, H.: Implications of emerging data mining (2009)
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- Source
- Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
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Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997)
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- Source
- Information systems. 22(1997) nos.5/6, S.349-385
-
Ester, M.; Sander, J.: Knowledge discovery in databases : Techniken und Anwendungen (2000)
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- Content
- Einleitung.- Statistik- und Datenbank-Grundlagen.Klassifikation.- Assoziationsregeln.- Generalisierung und Data Cubes.- Spatial-, Text-, Web-, Temporal-Data Mining. Ausblick.
-
Nohr, H.: Big Data im Lichte der EU-Datenschutz-Grundverordnung (2017)
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- Content
- Vgl.: JurPC Web-Dok. 111/2017 - DOI 10.7328/jurpcb2017328111.
-
Schwartz, D.: Graphische Datenanalyse für digitale Bibliotheken : Leistungs- und Funktionsumfang moderner Analyse- und Visualisierungsinstrumente (2006)
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- Abstract
- Das World Wide Web stellt umfangreiche Datenmengen zur Verfügung. Für den Benutzer wird es zunehmend schwieriger, diese Datenmengen zu sichten, zu bewerten und die relevanten Daten herauszufiltern. Einen Lösungsansatz für diese Problemstellung bieten Visualisierungsinstrumente, mit deren Hilfe Rechercheergebnisse nicht mehr ausschließlich über textbasierte Dokumentenlisten, sondern über Symbole, Icons oder graphische Elemente dargestellt werden. Durch geeignete Visualisierungstechniken können Informationsstrukturen in großen Datenmengen aufgezeigt werden. Informationsvisualisierung ist damit ein Instrument, um Rechercheergebnisse in einer digitalen Bibliothek zu strukturieren und relevante Daten für den Benutzer leichter auffindbar zu machen.
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Liu, W.; Weichselbraun, A.; Scharl, A.; Chang, E.: Semi-automatic ontology extension using spreading activation (2005)
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- Abstract
- This paper describes a system to semi-automatically extend and refine ontologies by mining textual data from the Web sites of international online media. Expanding a seed ontology creates a semantic network through co-occurrence analysis, trigger phrase analysis, and disambiguation based on the WordNet lexical dictionary. Spreading activation then processes this semantic network to find the most probable candidates for inclusion in an extended ontology. Approaches to identifying hierarchical relationships such as subsumption, head noun analysis and WordNet consultation are used to confirm and classify the found relationships. Using a seed ontology on "climate change" as an example, this paper demonstrates how spreading activation improves the result by naturally integrating the mentioned methods.
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Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010)
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- Abstract
- With the rapid development of Web 2.0, online reviews have become extremely valuable sources for mining customers' opinions. Fine-grained opinion mining has attracted more and more attention of both applied and theoretical research. In this article, the authors study how to automatically mine product features and opinions from multiple review sources. Specifically, they propose an integration strategy to solve the issue. Within the integration strategy, the authors mine domain knowledge from semistructured reviews and then exploit the domain knowledge to assist product feature extraction and sentiment orientation identification from unstructured reviews. Finally, feature-opinion tuples are generated. Experimental results on real-world datasets show that the proposed approach is effective.
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Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006)
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- Date
- 22. 3.2008 14:46:06
