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  • × theme_ss:"Wissensrepräsentation"
  1. Zhang, M.; Zhou, G.D.; Aw, A.: Exploring syntactic structured features over parse trees for relation extraction using kernel methods (2008) 0.08 
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    Abstract
    Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree. Our study reveals that the syntactic structured features embedded in a parse tree are very effective in relation extraction and can be well captured by the convolution tree kernel. Evaluation on the ACE benchmark corpora shows that using the convolution tree kernel only can achieve comparable performance with previous best-reported feature-based methods. It also shows that our method significantly outperforms previous two dependency tree kernels for relation extraction. Moreover, this paper proposes a composite kernel for relation extraction by combining the convolution tree kernel with a simple linear kernel. Our study reveals that the composite kernel can effectively capture both flat and structured features without extensive feature engineering, and easily scale to include more features. Evaluation on the ACE benchmark corpora shows that the composite kernel outperforms previous best-reported methods in relation extraction.
  2. Li, J.; Zhang, Z.; Li, X.; Chen, H.: Kernel-based learning for biomedical relation extraction (2008) 0.07 
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    Abstract
    Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.
  3. 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.
  4. Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007) 0.03 
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    Abstract
    As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.
  5. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.03 
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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.
  6. 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.
  7. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.02 
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    Pages
    S.11-22
  8. 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
  9. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.02 
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    Date
    26.12.2011 13:22:07
  10. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.02 
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    Date
    30. 5.2010 16:22:35
  11. 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
  12. 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
  13. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.01 
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    Date
    31. 7.2010 16:58:22
  14. OWL Web Ontology Language Test Cases (2004) 0.01 
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    Date
    14. 8.2011 13:33:22
  15. Giunchiglia, F.; Villafiorita, A.; Walsh, T.: Theories of abstraction (1997) 0.01 
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    Date
    1.10.2018 14:13:22
  16. Hauff-Hartig, S.: Wissensrepräsentation durch RDF: Drei angewandte Forschungsbeispiele : Bitte recht vielfältig: Wie Wissensgraphen, Disco und FaBiO Struktur in Mangas und die Humanities bringen (2021) 0.01 
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    Date
    22. 5.2021 12:43:05
  17. Priss, U.: Faceted information representation (2000) 0.01 
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    Date
    22. 1.2016 17:47:06
  18. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.01 
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    Date
    11. 2.2011 18:22:58
  19. 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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    22. 7.2010 19:41:16
  20. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.01 
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    Date
    11. 2.2011 18:22:25

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