-
Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006)
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- Source
- Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]
-
Narock, T.; Zhou, L.; Yoon, V.: Semantic similarity of ontology instances using polarity mining (2013)
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- Abstract
- Semantic similarity is vital to many areas, such as information retrieval. Various methods have been proposed with a focus on comparing unstructured text documents. Several of these have been enhanced with ontology; however, they have not been applied to ontology instances. With the growth in ontology instance data published online through, for example, Linked Open Data, there is an increasing need to apply semantic similarity to ontology instances. Drawing on ontology-supported polarity mining (OSPM), we propose an algorithm that enhances the computation of semantic similarity with polarity mining techniques. The algorithm is evaluated with online customer review data. The experimental results show that the proposed algorithm outperforms the baseline algorithm in multiple settings.
-
Menczer, F.: Lexical and semantic clustering by Web links (2004)
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- Abstract
- Recent Web-searching and -mining tools are combining text and link analysis to improve ranking and crawling algorithms. The central assumption behind such approaches is that there is a correiation between the graph structure of the Web and the text and meaning of pages. Here I formalize and empirically evaluate two general conjectures drawing connections from link information to lexical and semantic Web content. The link-content conjecture states that a page is similar to the pages that link to it, and the link-cluster conjecture that pages about the same topic are clustered together. These conjectures are offen simply assumed to hold, and Web search tools are built an such assumptions. The present quantitative confirmation sheds light an the connection between the success of the latest Web-mining techniques and the small world topology of the Web, with encouraging implications for the design of better crawling algorithms.
-
Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011)
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- Abstract
- One of the main themes in text mining is text representation, which is fundamental and indispensable for text-based intellegent information processing. Generally, text representation inludes two tasks: indexing and weighting. This paper has comparatively studied TF*IDF, LSI and multi-word for text representation. We used a Chinese and an English document collection to respectively evaluate the three methods in information retreival and text categorization. Experimental results have demonstrated that in text categorization, LSI has better performance than other methods in both document collections. Also, LSI has produced the best performance in retrieving English documents. This outcome has shown that LSI has both favorable semantic and statistical quality and is different with the claim that LSI can not produce discriminative power for indexing.
-
Wongthontham, P.; Abu-Salih, B.: Ontology-based approach for semantic data extraction from social big data : state-of-the-art and research directions (2018)
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- Theme
- Data Mining
-
Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002)
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- Date
- 22. 2.2003 17:25:39
22. 2.2003 18:17:40
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- Date
- 30. 3.2001 13:32:22
-
Chen, H.; Lally, A.M.; Zhu, B.; Chau, M.: HelpfulMed : Intelligent searching for medical information over the Internet (2003)
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- Footnote
- Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"
-
Surfing versus Drilling for knowledge in science : When should you use your computer? When should you use your brain? (2018)
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- Content
- Editorial: Surfing versus Drilling for Knowledge in Science: When should you use your computer? When should you use your brain? Blaise Pascal: Les deux infinis - The two infinities / Philippe Hünenberger and Oliver Renn - "Surfing" vs. "drilling" in the modern scientific world / Antonio Loprieno - Of millimeter paper and machine learning / Philippe Hünenberger - From one to many, from breadth to depth - industrializing research / Janne Soetbeer - "Deep drilling" requires "surfing" / Gerd Folkers and Laura Folkers - Surfing vs. drilling in science: A delicate balance / Alzbeta Kubincová - Digital trends in academia - for the sake of critical thinking or comfort? / Leif-Thore Deck - I diagnose, therefore I am a Doctor? Will drilling computer software replace human doctors in the future? / Yi Zheng - Surfing versus drilling in fundamental research / Wilfred van Gunsteren - Using brain vs. brute force in computational studies of biological systems / Arieh Warshel - Laboratory literature boards in the digital age / Jeffrey Bode - Research strategies in computational chemistry / Sereina Riniker - Surfing on the hype waves or drilling deep for knowledge? A perspective from industry / Nadine Schneider and Nikolaus Stiefl - The use and purpose of articles and scientists / Philip Mark Lund - Can you look at papers like artwork? / Oliver Renn - Dynamite fishing in the data swamp / Frank Perabo 34 Streetlights, augmented intelligence, and information discovery / Jeffrey Saffer and Vicki Burnett - "Yes Dave. Happy to do that for you." Why AI, machine learning, and blockchain will lead to deeper "drilling" / Michiel Kolman and Sjors de Heuvel - Trends in scientific document search ( Stefan Geißler - Power tools for text mining / Jane Reed 42 Publishing and patenting: Navigating the differences to ensure search success / Paul Peters
-
Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016)
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- Date
- 1. 2.2016 18:25:22
-
Kozikowski, P. et al.: Support of part-whole relations in query answering (2016)
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- Date
- 1. 2.2016 18:25:22
-
Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016)
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- Source
- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
-
Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016)
0.02
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- Source
- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
-
Sacco, G.M.: Dynamic taxonomies and guided searches (2006)
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- Date
- 22. 7.2006 17:56:22
-
Efthimiadis, E.N.: End-users' understanding of thesaural knowledge structures in interactive query expansion (1994)
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- Date
- 30. 3.2001 13:35:22
-
Mandalka, M.: Open semantic search zum unabhängigen und datenschutzfreundlichen Erschliessen von Dokumenten (2015)
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- Content
- Automatische Texterkennung (OCR) Dokumente, die nicht im Textformat, sondern als Grafiken vorliegen, wie z.B. Scans werden automatisch durch automatische Texterkennung (OCR) angereichert und damit auch der extrahierte Text durchsuchbar. Auch für eingebettete Bilddateien bzw. Scans innerhalb von PDF-Dateien. Unscharfe Suche mit Listen Ansonsten ist auch das Recherche-Tool bzw. die Such-Applikation "Suche mit Listen" integriert, mit denen sich schnell und komfortabel abgleichen lässt, ob es zu den einzelnen Einträgen in Listen jeweils Treffer in der durchsuchbaren Dokumentensammlung gibt. Mittels unscharfer Suche findet das Tool auch Ergebnisse, die in fehlerhaften oder unterschiedlichen Schreibweisen vorliegen. Semantische Suche und Textmining Im Recherche, Textanalyse und Document Mining Tutorial zu den enthaltenen Recherche-Tools und verschiedenen kombinierten Methoden zur Datenanalyse, Anreicherung und Suche wird ausführlicher beschrieben, wie auch eine große heterogene und unstrukturierte Dokumentensammlung bzw. eine grosse Anzahl von Dokumenten in verschiedenen Formaten leicht durchsucht und analysiert werden kann.
-
Fieldhouse, M.; Hancock-Beaulieu, M.: ¬The design of a graphical user interface for a highly interactive information retrieval system (1996)
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- Source
- Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
-
Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998)
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- Date
- 1. 8.1996 22:08:06
-
Faaborg, A.; Lagoze, C.: Semantic browsing (2003)
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- Source
- Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003
-
Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005)
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- Date
- 11. 2.2011 18:22:58
