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1
Key Discovery for Numerical Data: Application to Oenological Practices
In: 22th International Conference on Graph-Based Representation and Reasoning ; ICCS: International Conference on Conceptual Structures ; https://hal.archives-ouvertes.fr/hal-01837440 ; ICCS: International Conference on Conceptual Structures, Jul 2016, Annecy, France. pp.222-236, ⟨10.1007/978-3-319-40985-6_17⟩ ; https://www.irit.fr/ICCS2016/ (2016)
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2
KNEWS: Using Logical and Lexical Semantics to Extract Knowledge from Natural Language
In: Proceedings of the European Conference on Artificial Intelligence (ECAI) 2016 conference ; https://hal.inria.fr/hal-01389390 ; Proceedings of the European Conference on Artificial Intelligence (ECAI) 2016 conference, Aug 2016, The Hague, Netherlands (2016)
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3
Evaluating Lexical Similarity to build Sentiment Similarity
In: Proceedings of the Language and Resource Conference, LREC ; Language and Resource Conference, LREC ; https://hal.archives-ouvertes.fr/hal-01394768 ; Language and Resource Conference, LREC, May 2016, portoroz, Slovenia (2016)
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4
Biomedical term extraction: overview and a new methodology
In: ISSN: 1386-4564 ; EISSN: 1573-7659 ; Information Retrieval Journal ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01274539 ; Information Retrieval Journal, Springer, 2016, Medical Information Retrieval, 19 (1), pp.59-99. ⟨10.1007/s10791-015-9262-2⟩ (2016)
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5
A Pragma-Semantic Analysis of the Emotion/Sentiment Relation in Debates
In: 4th International Workshop on Artificial Intelligence and Cognition ; https://hal.inria.fr/hal-01342438 ; 4th International Workshop on Artificial Intelligence and Cognition, Jul 2016, New York, United States (2016)
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6
Graph-FCA in Practice
In: International Conference on Conceptual Structures (ICCS) ; https://hal.inria.fr/hal-01405491 ; International Conference on Conceptual Structures (ICCS), Jul 2016, Annecy, France. pp.107 - 121, ⟨10.1007/978-3-319-40985-6_9⟩ (2016)
Abstract: International audience ; With the rise of the Semantic Web, more and more relational data are made available in the form of knowledge graphs (e.g., RDF, conceptual graphs). A challenge is to discover conceptual structures in those graphs, in the same way as Formal Concept Analysis (FCA) discovers conceptual structures in tables. Graph-FCA has been introduced in a previous work as an extension of FCA for such knowledge graphs. In this paper, algorithmic aspects and use cases are explored in order to study the feasibility and usefulness of G-FCA. We consider two use cases. The first one extracts linguistic structures from parse trees, comparing two graph models. The second one extracts workflow patterns from cooking recipes, highlighting the benefits of n-ary relationships and concepts.
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]; Formal Concept Analysis; Graph Pattern; Knowledge Graph; Semantic Web
URL: https://hal.inria.fr/hal-01405491
https://hal.inria.fr/hal-01405491/file/paper-final.pdf
https://doi.org/10.1007/978-3-319-40985-6_9
https://hal.inria.fr/hal-01405491/document
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7
Proceedings of the 7th Workshop on Computational Models of Narrative
Miller, Ben; Lieto, Antonio; Ronfard, Rémi. - : HAL CCSD, 2016
In: 7th Workshop on Computational Models of Narrative (CMN 2016) ; https://hal.inria.fr/hal-01427217 ; 7th Workshop on Computational Models of Narrative (CMN 2016), Jul 2016, Cracovie, Poland. 53, 2016, OASICS, 978-3-95977-020-0 ; http://drops.dagstuhl.de/portals/oasics/index.php?semnr=16021 (2016)
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