-
Rocha Souza, R.; Lemos, D.: a comparative analysis : Knowledge organization systems for the representation of multimedia resources on the Web (2020)
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- Content
- DOI:10.5771/0943-7444-2020-4-300.
- Source
- Knowledge organization. 47(2020) no.4, S.300-319
-
Wei, W.; Liu, Y.-P.; Wei, L-R.: Feature-level sentiment analysis based on rules and fine-grained domain ontology (2020)
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- Date
- 4. 3.2020 14:17:52
-
Hocker, J.; Schindler, C.; Rittberger, M.: Participatory design for ontologies : a case study of an open science ontology for qualitative coding schemas (2020)
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- Date
- 20. 1.2015 18:30:22
- Source
- Aslib journal of information management. 72(2020) no.4, S.671-685
-
Pepper, S.; Arnaud, P.J.L.: Absolutely PHAB : toward a general model of associative relations (2020)
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- Abstract
- There have been many attempts at classifying the semantic modification relations (R) of N + N compounds but this work has not led to the acceptance of a definitive scheme, so that devising a reusable classification is a worthwhile aim. The scope of this undertaking is extended to other binominal lexemes, i.e. units that contain two thing-morphemes without explicitly stating R, like prepositional units, N + relational adjective units, etc. The 25-relation taxonomy of Bourque (2014) was tested against over 15,000 binominal lexemes from 106 languages and extended to a 29-relation scheme ("Bourque2") through the introduction of two new reversible relations. Bourque2 is then mapped onto Hatcher's (1960) four-relation scheme (extended by the addition of a fifth relation, similarity , as "Hatcher2"). This results in a two-tier system usable at different degrees of granularities. On account of its semantic proximity to compounding, metonymy is then taken into account, following Janda's (2011) suggestion that it plays a role in word formation; Peirsman and Geeraerts' (2006) inventory of 23 metonymic patterns is mapped onto Bourque2, confirming the identity of metonymic and binominal modification relations. Finally, Blank's (2003) and Koch's (2001) work on lexical semantics justifies the addition to the scheme of a third, superordinate level which comprises the three Aristotelean principles of similarity, contiguity and contrast.
-
Simoes, G.; Machado, L.; Gnoli, C.; Souza, R.: Can an ontologically-oriented KO do without concepts? (2020)
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-
Zhou, H.; Guns, R.; Engels, T.C.E.: Towards indicating interdisciplinarity : characterizing interdisciplinary knowledge flow (2023)
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-
Almeida, M.B.; Felipe, E.R.; Barcelos, R.: Toward a document-centered ontological theory for information architecture in corporations (2020)
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-
Buente, W.; Baybayan, C.K.; Hajibayova, L.; McCorkhill, M.; Panchyshyn, R.: Exploring the renaissance of wayfinding and voyaging through the lens of knowledge representation, organization and discovery systems (2020)
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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)
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- Date
- 22. 5.2021 12:43:05
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Jiang, Y.-C.; Li, H.: ¬The theoretical basis and basic principles of knowledge network construction in digital library (2023)
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- Content
- Vgl.: https://www.nomos-elibrary.de/10.5771/0943-7444-2023-4/ko-knowledge-organization-jahrgang-50-2023-heft-4.
- Source
- Knowledge organization. 50(2023) no.4, S.245 - 256
-
Hudon, M.: Facet (2020)
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- DOI:10.5771/0943-7444-2020-4-320.
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- Knowledge organization. 47(2020) no.4, S.320-333
-
Banerjee, D.; Ghosh, S.S.; Mondal, T.M.: OnE : an ontology evaluation framework (2020)
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- DOI:10.5771/0943-7444-2020-4-283.
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- Knowledge organization. 47(2020) no.4, S.283-299
-
Jia, J.: From data to knowledge : the relationships between vocabularies, linked data and knowledge graphs (2021)
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- Date
- 22. 1.2021 14:24:32
-
MacFarlane, A.; Missaoui, S.; Frankowska-Takhari, S.: On machine learning and knowledge organization in multimedia information retrieval (2020)
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- Abstract
- Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).
-
Sinha, P.K.; Dutta, B.: ¬A systematic analysis of flood ontologies : a parametric approach (2020)
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- Date
- 4. 3.2020 14:57:06
-
Peponakis, M.; Mastora, A.; Kapidakis, S.; Doerr, M.: Expressiveness and machine processability of Knowledge Organization Systems (KOS) : an analysis of concepts and relations (2020)
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- Source
- International journal on digital libraries. 20(2020) no.4, S.433-452
-
Frey, J.; Streitmatter, D.; Götz, F.; Hellmann, S.; Arndt, N.: DBpedia Archivo (2020)
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- Abstract
- We are proud to announce DBpedia Archivo (https://archivo.dbpedia.org) an augmented ontology archive and interface to implement FAIRer ontologies. Each ontology is rated with 4 stars measuring basic FAIR features. We discovered 890 ontologies reaching on average 1.95 out of 4 stars. Many of them have no or unclear licenses and have issues w.r.t. retrieval and parsing.
