-
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)
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- Content
- Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
-
Frey, J.; Streitmatter, D.; Götz, F.; Hellmann, S.; Arndt, N.: DBpedia Archivo : a Web-Scale interface for ontology archiving under consumer-oriented aspects (2020)
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Bold, N.; Kim, W.-J.; Yang, J.-D.: Converting object-based thesauri into XML Topic Maps (2010)
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Amarger, F.; Chanet, J.-P.; Haemmerlé, O.; Hernandez, N.; Roussey, C.: SKOS sources transformations for ontology engineering : agronomical taxonomy use case (2014)
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Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018)
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- Date
- 16.11.2018 14:22:01
-
Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010)
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-
Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996)
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- Source
- Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al
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Bringsjord, S.; Clark, M.; Taylor, J.: Sophisticated knowledge representation and reasoning requires philosophy (2014)
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- Date
- 9. 2.2017 19:22:14
-
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
-
Zhitomirsky-Geffet, M.; Bar-Ilan, J.: Towards maximal unification of semantically diverse ontologies for controversial domains (2014)
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- Date
- 20. 1.2015 18:30:22
-
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
-
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.
-
Aitken, S.; Reid, S.: Evaluation of an ontology-based information retrieval tool (2000)
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- Content
- Beitrag für: Workshop on the Applications of Ontologies and Problem-Solving Methods, (eds) Gómez-Pérez, A., Benjamins, V.R., Guarino, N., and Uschold, M. European Conference on Artificial Intelligence 2000, Berlin.
-
Yu, L.-C.; Wu, C.-H.; Chang, R.-Y.; Liu, C.-H.; Hovy, E.H.: Annotation and verification of sense pools in OntoNotes (2010)
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- Abstract
- The paper describes the OntoNotes, a multilingual (English, Chinese and Arabic) corpus with large-scale semantic annotations, including predicate-argument structure, word senses, ontology linking, and coreference. The underlying semantic model of OntoNotes involves word senses that are grouped into so-called sense pools, i.e., sets of near-synonymous senses of words. Such information is useful for many applications, including query expansion for information retrieval (IR) systems, (near-)duplicate detection for text summarization systems, and alternative word selection for writing support systems. Although a sense pool provides a set of near-synonymous senses of words, there is still no knowledge about whether two words in a pool are interchangeable in practical use. Therefore, this paper devises an unsupervised algorithm that incorporates Google n-grams and a statistical test to determine whether a word in a pool can be substituted by other words in the same pool. The n-gram features are used to measure the degree of context mismatch for a substitution. The statistical test is then applied to determine whether the substitution is adequate based on the degree of mismatch. The proposed method is compared with a supervised method, namely Linear Discriminant Analysis (LDA). Experimental results show that the proposed unsupervised method can achieve comparable performance with the supervised method.
-
Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021)
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- Source
- Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]
-
Schmitz-Esser, W.: Language of general communication and concept compatibility (1996)
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- Pages
- S.11-22
-
Davies, J.; Fensel, D.; Harmelen, F. van: Conclusions: ontology-driven knowledge management : towards the Semantic Web? (2004)
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- Source
- Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a
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Kayed, A.; Hirzallah, N.; Al Shalabi, L.A.; Najjar, M.: Building ontological relationships : a new approach (2008)
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Suchanek, F.M.; Kasneci, G.; Weikum, G.: YAGO: a large ontology from Wikipedia and WordNet (2008)
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- Abstract
- This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO's precision at 95%-as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO's data.
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Soergel, D.: Towards a relation ontology for the Semantic Web (2011)
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- Abstract
- The Semantic Web consists of data structured for use by computer programs, such as data sets made available under the Linked Open Data initiative. Much of this data is structured following the entity-relationship model encoded in RDF for syntactic interoperability. For semantic interoperability, the semantics of the relationships used in any given dataset needs to be made explicit. Ultimately this requires an inventory of these relationships structured around a relation ontology. This talk will outline a blueprint for such an inventory, including a format for the description/definition of binary and n-ary relations, drawing on ideas put forth in the classification and thesaurus community over the last 60 years, upper level ontologies, systems like FrameNet, the Buffalo Relation Ontology, and an analysis of linked data sets.
