TY - CHAP A1 - Mayr, Philipp A1 - Tudhope, Douglas A1 - Golub, Koraljka A1 - Wartena, Christian A1 - De Luca, Ernesto William T1 - Editorial for the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) T2 - NKOS 2016 : Networked Knowledge Organization Systems Workshop ; Proceedings of the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) co-located with the 20th International Conference on Theory and Practice of Digital Libraries 2016 (TPDL 2016), Hannover, Germany, September 9, 2016. N2 - Knowledge Organization Systems (KOS), in the form of classification systems, thesauri, lexical databases, ontologies, and taxonomies, play a crucial role in digital information management and applications generally. Carrying semantics in a well-controlled and documented way, Knowledge Organisation Systems serve a variety of important functions: tools for representation and indexing of information and documents, knowledge-based support to information searchers, semantic road maps to domains and disciplines, communication tool by providing conceptual framework, and conceptual basis for knowledge based systems, e.g. automated classification systems. New networked KOS (NKOS) services and applications are emerging, and we have reached a stage where many KOS standards exist and the integration of linked services is no longer just a future scenario. This editorial describes the workshop outline and overview of presented papers at the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) in Hannover, Germany. KW - Informationsmanagement Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:960-opus4-11130 UR - http://ceur-ws.org/Vol-1676/editorial.pdf SP - 1 EP - 6 ER - TY - CHAP A1 - Aga, Rosa Tsegaye A1 - Wartena, Christian A1 - Drumond, Lucas A1 - Schmidt-Thieme, Lars T1 - Learning thesaurus relations from distributional features T2 - LREC 2016, Tenth International Conference on Language Resources and Evaluation N2 - In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics. KW - distributional semantics KW - thesauri KW - context vectors KW - supervised machine learning KW - Thesaurus KW - Überwachtes Lernen KW - Semantik Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:960-opus4-10894 SN - 978-2-9517408-9-1 SP - 2071 EP - 2075 ER - TY - CHAP A1 - Aga, Rosa Tsegaye A1 - Drumond, Lucas A1 - Wartena, Christian A1 - Schmidt-Thieme, Lars T1 - Integrating distributional and lexical information for semantic classification of words using MRMF T2 - Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, December 11-17 2016 N2 - Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multirelational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results. KW - Klassifikation KW - Semantik Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:960-opus4-11153 SP - 2708 EP - 2717 ER - TY - CHAP A1 - Aga, Rosa Tsegaye A1 - Wartena, Christian T1 - CogALex-V Shared Task: HsH-Supervised – supervised similarity learning using entry wise product of context vectors T2 - Proceedings of the Workshop on Cognitive Aspects of the Lexicon, December 12, 2016, Osaka, Japan N2 - The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied. KW - Klassifikation Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:960-opus4-11163 SP - 114 EP - 118 ER - TY - CHAP A1 - Heller, Lambert A1 - Blümel, Ina A1 - Cartellieri, Simone A1 - Wartena, Christian T1 - Discovery and efficient reuse of technology pictures using Wikimedia infrastructures. A proposal T2 - Tenth International AAAI Conference on Web and Social Media (ICWSM), Cologne, Germany, 17-20 May 2016 N2 - Multimedia objects, especially images and figures, are essential for the visualization and interpretation of research findings. The distribution and reuse of these scientific objects is significantly improved under open access conditions, for instance in Wikipedia articles, in research literature, as well as in education and knowledge dissemination, where licensing of images often represents a serious barrier. Whereas scientific publications are retrievable through library portals or other online search services due to standardized indices there is no targeted retrieval and access to the accompanying images and figures yet. Consequently there is a great demand to develop standardized indexing methods for these multimedia open access objects in order to improve the accessibility to this material. With our proposal, we hope to serve a broad audience which looks up a scientific or technical term in a web search portal first. Until now, this audience has little chance to find an openly accessible and reusable image narrowly matching their search term on first try - frustratingly so, even if there is in fact such an image included in some open access article. KW - Open Access KW - Wikimedia Commons KW - Wikidata KW - Information Dissemination Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:960-opus4-8743 N1 - This paper was accepted and presented at the wiki workshop of ICWSM 2016, though due to technical reason it was not included in the conference report. ER -