This research looks at the complexity inherent in the causal relation and the implications for its representation in a Terminological Knowledge Base (TKB). Supported by a more general study of semantic relation hierarchies, a hierarchical refinement of the causal relation is proposed. It results from a manual search of a corpus which shows that it efficiently captures and formalizes variations expressed in text. The feasibility of determining such categorization during automatic extraction from corpora is also explored. Conceptual graphs are used as a representation formalism to which we have added certainty information to capture the degree of certainty surrounding the interaction between two terms involved in a causal relation.
2011. The Representation of Multidimensionality in a Bilingualized English-Spanish Thesaurus for Learners in Architecture and Building Construction. International Journal of Lexicography 24:2 ► pp. 198 ff.
Ittoo, Ashwin & Gosse Bouma
2011. Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts. In Natural Language Processing and Information Systems [Lecture Notes in Computer Science, 6716], ► pp. 52 ff.
Ittoo, Ashwin & Gosse Bouma
2013. Minimally-supervised learning of domain-specific causal relations using an open-domain corpus as knowledge base. Data & Knowledge Engineering 88 ► pp. 142 ff.
Marshman, Elizabeth, Marie-Claude L'Homme & Victoria Surtees
2008. Portability of cause–effect relation markers across specialised domains and text genres: a comparative evaluation. Corpora 3:2 ► pp. 141 ff.
Khoo, Christopher S. G. & Jin‐Cheon Na
2006. Semantic relations in information science. Annual Review of Information Science and Technology 40:1 ► pp. 157 ff.
Barrière, Caroline
2004. Knowledge-Rich Contexts Discovery. In Advances in Artificial Intelligence [Lecture Notes in Computer Science, 3060], ► pp. 187 ff.
L'Homme, Marie-Claude
2004. Bibliographie. In La terminologie : principes et techniques, ► pp. 259 ff.
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