Graph-Based Acquisition of Expressive Knowledge

International Conference Knowledge Engineering and Knowledge Management 2004 11 cites
2004:02005:02006:12007:12008:12009:12010:12011:12012:12013:12014:12015:2
2004 2015
11

V. Chaudhri, Kenneth S. Murray, John Pacheco, Peter Clark, B. Porter, P. Hayes

Abstract

10.1109/TCBB.2018.2847336. Bioinformatic Workflow Extraction from Scientific Texts based on Word Sense Disambiguation. Halioui A, Valtchev P, Diallo AB. This paper introduces a method for automatic workflow extraction from texts using Process-Oriented Case-Based Reasoning (POCBR). While the current workflow management systems implement mostly different complicated graphical tasks based on advanced distributed solutions (e.g., cloud computing and grid computation), workflow knowledge acquisition from texts using case-based reasoning represents more expressive and semantic case representations. We propose in this context, an ontology-based workflow extraction framework to acquire processual knowledge from texts. Our methodology extends the classic NLP techniques to extract and disambiguate complex tasks and relations in texts. Using a graph-based representation of workflows and a domain ontology, our extraction process uses a context-aware approach to recognize workflow components in texts: data and control flows. We applied our framework in a technical domain in bioinformatics: i.e., phylogenetic analyses. An evaluation based on workflow semantic similarities in a gold standard proves that our approach provides promising results in the process extraction domain. Both data and implementation of our framework are available in: http://labo.bioinfo.uqam.ca/tgowler. DOI: 10.1109/TCBB.2018.2847336 PMID: 29994265 [Indexed for MEDLINE]

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