Rule identification using ontology while acquiring rules from Web pages
International Journal of Human-Computer Studies
From the textual description of an accident to its causes
Artificial Intelligence
Recent Literature Collected by Didier DUBOIS, Henri PRADE and Salvatore SESSA
Fuzzy Sets and Systems
Lexico-syntactic causal pattern text mining
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Gene ontology classification of biomedical literatures using context association
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Discovering patterns from ontology-derived texts
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Learning causality for news events prediction
Proceedings of the 21st international conference on World Wide Web
Learning to predict from textual data
Journal of Artificial Intelligence Research
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SEKE is a semantic expectation-based knowledge extraction system for extracting causation knowledge from natural language texts. It is inspired by human behavior on analyzing texts and capturing information with semantic expectations. The framework of SEKE consists of different kinds of generic templates organized in a hierarchical fashion. There are semantic templates, sentence templates, reason templates, and consequence templates. The design of templates is based on the expected semantics of causation knowledge. They are robust and flexible. The semantic template represents the target relation. The sentence templates act as a middle layer to reconcile the semantic templates with natural language texts. With the designed templates, SEKE is able to extract causation knowledge from complex sentences. Another characteristic of SEKE is that it can discover unseen knowledge for reason and consequence by means of pattern discovery. Using simple linguistic information, SEKE can discover extraction pattern from previously extracted causation knowledge and apply the newly generated patterns for knowledge discovery. To demonstrate the adaptability of SEKE for different domains, we investigate the application of SEKE on two domain areas of news articles, namely the Hong Kong stock market movement domain and the global warming domain. Although these two domain areas are completely different, in respect to their expected semantics in reason and consequence, SEKE can effectively handle the natural language texts in these two domains for causation knowledge extraction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 327–358, 2005.