Search (106 results, page 1 of 6)

  • × theme_ss:"Wissensrepräsentation"
  1. 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) 0.13
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  2. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.11
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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.
  3. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.09
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    Content
    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  4. Conde, A.; Larrañaga, M.; Arruarte, A.; Elorriaga, J.A.; Roth, D.: litewi: a combined term extraction and entity linking method for eliciting educational ontologies from textbooks (2016) 0.05
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    Abstract
    Major efforts have been conducted on ontology learning, that is, semiautomatic processes for the construction of domain ontologies from diverse sources of information. In the past few years, a research trend has focused on the construction of educational ontologies, that is, ontologies to be used for educational purposes. The identification of the terminology is crucial to build ontologies. Term extraction techniques allow the identification of the domain-related terms from electronic resources. This paper presents LiTeWi, a novel method that combines current unsupervised term extraction approaches for creating educational ontologies for technology supported learning systems from electronic textbooks. LiTeWi uses Wikipedia as an additional information source. Wikipedia contains more than 30 million articles covering the terminology of nearly every domain in 288 languages, which makes it an appropriate generic corpus for term extraction. Furthermore, given that its content is available in several languages, it promotes both domain and language independence. LiTeWi is aimed at being used by teachers, who usually develop their didactic material from textbooks. To evaluate its performance, LiTeWi was tuned up using a textbook on object oriented programming and then tested with two textbooks of different domains-astronomy and molecular biology.
    Date
    22. 1.2016 12:38:14
  5. Andelfinger, U.; Wyssusek, B.; Kremberg, B.; Totzke, R.: Ontologies in knowledge management : panacea or mirage? 0.02
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    Content
    In der Finanzwirtschaft mit ihren automatisierten Handelssystemen (auf Basis technischer Ontologien) wird beispielsweise inzwischen bei außergewöhnlichen Kursbewegungen der Börse der automatische Handel unterbrochen, so dass dann auf pragmatisch-natürlichsprachliche Weise nach den Gründen für die Ausschläge gesucht werden kann. Aus Sicht der technischen Ontologien wäre eine solche Unterbrechung des Computerhandels (zur Beruhigung der Märkte) nicht zwingend erforderlich, aber sie ist sehr sinnvoll aus einer außerhalb der technischen Ontologie stehenden Perspektive, die alleine nach Sinn und Bedeutung stabiler Kursverläufe zu fragen imstande ist. Der hier sich abzeichnende ,pragmatic turn' beim Einsatz technischer Ontologien ist auch in vielfältiger Weise in Trends wie z.B. Folksonomies, Sozialen Netzwerken und Open-SourceEntwicklergruppen zu erkennen. Diese Gemeinschaften zeichnen sich dadurch aus, dass sie zwar (technische) Ontologien einsetzen, diese jedoch in intensive soziale Austauschprozesse einbinden, in denen die formalen Wissensrepräsentationen mit situativer Bedeutung und Sinn versehen und angereichert werden. Dieser Trend zu ,weicheren' Formen der Nutzung von (technischen) Ontologien scheint nach aktuellem Wissensstand auf jeden Fall in der Praxis erfolgversprechender als die anfänglichen Hoffnungen des Semantic Web oder vollständiger (technischer) Ontologien - ganz abgesehen vom laufenden Pflegeaufwand 'vollständiger' technischer Ontologien.
    Source
    http://www.cba.neu.edu/uploadedFiles/Site_Sections/OLKC_2010/Program_Overview/Parallel_Sessions/180_Wyssusek_Abstract_Ontologies%20in%20Knowledge%20Management%281%29.pdf
  6. Xu, G.; Cao, Y.; Ren, Y.; Li, X.; Feng, Z.: Network security situation awareness based on semantic ontology and user-defined rules for Internet of Things (2017) 0.02
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    Abstract
    Internet of Things (IoT) brings the third development wave of the global information industry which makes users, network and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability and network flow. Further, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods. [http://ieeexplore.ieee.org/abstract/document/7999187/]
  7. Koopman, B.; Zuccon, G.; Bruza, P.; Sitbon, L.; Lawley, M.: Information retrieval as semantic inference : a graph Inference model applied to medical search (2016) 0.02
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    Abstract
    This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem-the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by keyword-based search methods, prompting us to perform additional relevance assessment on these new documents. A third of the newly retrieved documents judged were found to be relevant. Our analysis provides a thorough understanding of when and how to apply inference for retrieval, including a categorisation of queries according to the effect of inference. The inference mechanism promoted recall by retrieving new relevant documents not found by previous keyword-based approaches. In addition, it promoted precision by an effective reranking of documents. When inference is used, performance gains can generally be expected on hard queries. However, inference should not be applied universally: for easy, unambiguous queries and queries with few relevant documents, inference did adversely affect effectiveness. These conclusions reflect the fact that for retrieval as inference to be effective, a careful balancing act is involved. Finally, although the Graph Inference model is developed and applied to medical search, it is a general retrieval model applicable to other areas such as web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search.
  8. Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (2010) 0.01
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    Date
    2. 3.2013 12:29:05
    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
  9. Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.01
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    Date
    29. 7.2011 14:44:56
    26.12.2011 13:40:22
  10. Hinkelmann, K.: Ontopia Omnigator : ein Werkzeug zur Einführung in Topic Maps (20xx) 0.01
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    Date
    4. 9.2011 12:29:09
  11. Griffiths, T.L.; Steyvers, M.: ¬A probabilistic approach to semantic representation (2002) 0.01
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    Date
    29. 6.2015 14:55:01
    29. 6.2015 16:09:05
  12. Roth, G.; Schwegler, H.: Kognitive Referenz und Selbstreferentialität des Gehirns : ein Beitrag zur Klärung des Verhältnisses zwischen Erkenntnistheorie und Hirnforschung (1992) 0.00
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    Date
    20.12.2018 12:39:29
  13. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.00
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    Pages
    S.11-22
  14. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.00
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    Date
    13.12.2017 14:17:22
  15. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.00
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  16. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.00
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  17. Nielsen, M.: Neuronale Netze : Alpha Go - Computer lernen Intuition (2018) 0.00
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  18. Clark, M.; Kim, Y.; Kruschwitz, U.; Song, D.; Albakour, D.; Dignum, S.; Beresi, U.C.; Fasli, M.; Roeck, A De: Automatically structuring domain knowledge from text : an overview of current research (2012) 0.00
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  19. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.00
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  20. Broughton, V.: Facet analysis as a fundamental theory for structuring subject organization tools (2007) 0.00
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    Date
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