Search (37 results, page 1 of 2)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Wissensrepräsentation"
  1. Information and communication technologies : international conference; proceedings / ICT 2010, Kochi, Kerala, India, September 7 - 9, 2010 (2010) 0.06 
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    LCSH
    Data mining
    RSWK
    Data Mining / Kongress / Cochin <Kerala, 2010>
    Subject
    Data Mining / Kongress / Cochin <Kerala, 2010>
    Data mining
  2. Cui, H.: Competency evaluation of plant character ontologies against domain literature (2010) 0.06 
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    Abstract
    Specimen identification keys are still the most commonly created tools used by systematic biologists to access biodiversity information. Creating identification keys requires analyzing and synthesizing large amounts of information from specimens and their descriptions and is a very labor-intensive and time-consuming activity. Automating the generation of identification keys from text descriptions becomes a highly attractive text mining application in the biodiversity domain. Fine-grained semantic annotation of morphological descriptions of organisms is a necessary first step in generating keys from text. Machine-readable ontologies are needed in this process because most biological characters are only implied (i.e., not stated) in descriptions. The immediate question to ask is How well do existing ontologies support semantic annotation and automated key generation? With the intention to either select an existing ontology or develop a unified ontology based on existing ones, this paper evaluates the coverage, semantic consistency, and inter-ontology agreement of a biodiversity character ontology and three plant glossaries that may be turned into ontologies. The coverage and semantic consistency of the ontology/glossaries are checked against the authoritative domain literature, namely, Flora of North America and Flora of China. The evaluation results suggest that more work is needed to improve the coverage and interoperability of the ontology/glossaries. More concepts need to be added to the ontology/glossaries and careful work is needed to improve the semantic consistency. The method used in this paper to evaluate the ontology/glossaries can be used to propose new candidate concepts from the domain literature and suggest appropriate definitions.
    Date
    1. 6.2010 9:55:22
  3. Semantic applications (2018) 0.06 
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    Content
    Introduction.- Ontology Development.- Compliance using Metadata.- Variety Management for Big Data.- Text Mining in Economics.- Generation of Natural Language Texts.- Sentiment Analysis.- Building Concise Text Corpora from Web Contents.- Ontology-Based Modelling of Web Content.- Personalized Clinical Decision Support for Cancer Care.- Applications of Temporal Conceptual Semantic Systems.- Context-Aware Documentation in the Smart Factory.- Knowledge-Based Production Planning for Industry 4.0.- Information Exchange in Jurisdiction.- Supporting Automated License Clearing.- Managing cultural assets: Implementing typical cultural heritage archive's usage scenarios via Semantic Web technologies.- Semantic Applications for Process Management.- Domain-Specific Semantic Search Applications.
    LCSH
    Data mining
    Data Mining and Knowledge Discovery
    RSWK
    Data Mining
    Subject
    Data Mining
    Data mining
    Data Mining and Knowledge Discovery
  4. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.04 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  5. Djioua, B.; Desclés, J.-P.; Alrahabi, M.: Searching and mining with semantic categories (2012) 0.04 
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    Abstract
    A new model is proposed to retrieve information by building automatically a semantic metatext structure for texts that allow searching and extracting discourse and semantic information according to certain linguistic categorizations. This paper presents approaches for searching and mining full text with semantic categories. The model is built up from two engines: The first one, called EXCOM (Djioua et al., 2006; Alrahabi, 2010), is an automatic system for text annotation, related to discourse and semantic maps, which are specification of general linguistic ontologies founded on the Applicative and Cognitive Grammar. The annotation layer uses a linguistic method called Contextual Exploration, which handles the polysemic values of a term in texts. Several 'semantic maps' underlying 'point of views' for text mining guide this automatic annotation process. The second engine uses semantic annotated texts, produced previously in order to create a semantic inverted index, which is able to retrieve relevant documents for queries associated with discourse and semantic categories such as definition, quotation, causality, relations between concepts, etc. (Djioua & Desclés, 2007). This semantic indexation process builds a metatext layer for textual contents. Some data and linguistic rules sets as well as the general architecture that extend third-party software are expressed as supplementary information.
  6. Mohr, J.W.; Bogdanov, P.: Topic models : what they are and why they matter (2013) 0.04 
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    Abstract
    We provide a brief, non-technical introduction to the text mining methodology known as "topic modeling." We summarize the theory and background of the method and discuss what kinds of things are found by topic models. Using a text corpus comprised of the eight articles from the special issue of Poetics on the subject of topic models, we run a topic model on these articles, both as a way to introduce the methodology and also to help summarize some of the ways in which social and cultural scientists are using topic models. We review some of the critiques and debates over the use of the method and finally, we link these developments back to some of the original innovations in the field of content analysis that were pioneered by Harold D. Lasswell and colleagues during and just after World War II.
    Theme
    Data Mining
  7. Bauckhage, C.: Moderne Textanalyse : neues Wissen für intelligente Lösungen (2016) 0.04 
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    Theme
    Data Mining
  8. Finke, M.; Risch, J.: "Match Me If You Can" : Sammeln und semantisches Aufbereiten von Fußballdaten (2017) 0.04 
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    Abstract
    Interviews, Spielstatistiken oder Videoaufzeichnungen sind für Fußballfans zwar zahlreich im Internet verfügbar, aber auf viele verschiedene Websites verstreut. "Semantic Media Mining" verknüpft nun Fußballdaten aus unterschiedlichen Quellen, bereitet sie semantisch auf und führt sie auf einer einzigen Website zusammen. Dadurch dokumentieren und visualisieren wir mehr als 50 Jahre Fußballgeschichte mit über 500 Mannschaften und 40.000 Spielern der Champions League, sowie der 1. und 2. Bundesliga.
  9. Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012) 0.03 
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    Abstract
    Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is- part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.
  10. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.03 
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    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
  11. Sánchez, D.; Batet, M.; Valls, A.; Gibert, K.: Ontology-driven web-based semantic similarity (2010) 0.02 
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    Abstract
    Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge-such as the structure of a taxonomy-or implicit knowledge-such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the ?rst case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to computer accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies - like specific domain ontologies - and massive corpus - like the Web. In this paper, several of the presente issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures' dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.
  12. Reasoning Web : Semantic Interoperability on the Web, 13th International Summer School 2017, London, UK, July 7-11, 2017, Tutorial Lectures (2017) 0.02 
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    Content
    Neumaier, Sebastian (et al.): Data Integration for Open Data on the Web - Stamou, Giorgos (et al.): Ontological Query Answering over Semantic Data - Calì, Andrea: Ontology Querying: Datalog Strikes Back - Sequeda, Juan F.: Integrating Relational Databases with the Semantic Web: A Reflection - Rousset, Marie-Christine (et al.): Datalog Revisited for Reasoning in Linked Data - Kaminski, Roland (et al.): A Tutorial on Hybrid Answer Set Solving with clingo - Eiter, Thomas (et al.): Answer Set Programming with External Source Access - Lukasiewicz, Thomas: Uncertainty Reasoning for the Semantic Web - Calvanese, Diego (et al.): OBDA for Log Extraction in Process Mining
  13. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.02 
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    Date
    13.12.2017 14:17:22
  14. Nielsen, M.: Neuronale Netze : Alpha Go - Computer lernen Intuition (2018) 0.02 
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    Source
    Spektrum der Wissenschaft. 2018, H.1, S.22-27
  15. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.01 
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    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
  16. Deokattey, S.; Neelameghan, A.; Kumar, V.: ¬A method for developing a domain ontology : a case study for a multidisciplinary subject (2010) 0.01 
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    Date
    22. 7.2010 19:41:16
  17. Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (2010) 0.01 
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    Source
    Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
  18. Madalli, D.P.; Balaji, B.P.; Sarangi, A.K.: Music domain analysis for building faceted ontological representation (2014) 0.01 
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  19. Hohmann, G.: ¬Die Anwendung des CIDOC-CRM für die semantische Wissensrepräsentation in den Kulturwissenschaften (2010) 0.01 
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    Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
  20. Semenova, E.: Ontologie als Begriffssystem : Theoretische Überlegungen und ihre praktische Umsetzung bei der Entwicklung einer Ontologie der Wissenschaftsdisziplinen (2010) 0.01 
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    Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly

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