Ontology Learning and Its Application to Automated Terminology Translation

By Roberto Navigli, Paola Velardi, Aldo Gangemi. IEEE Intelligent Systems, 2003

400 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/84f6eeeac62804de0502a54c444bacfad0f64730

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Citations in the corpus

A Delphi-based approach to developing expert systems with the cooperation of multiple experts (2007, neutral): “To cope with the knowledge acquisition problem, many knowledge acquisition tools or methods have been proposed to build rapid prototypes and to improve the quality of the elicited knowledge, e.g., ETS (Boose, 1984 (Boose, , 1985 , TEIRESIAS (Davis, 1979) , MORE (Kahn, Nowlan, & McDermott, 1985) , SA”
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites (2004, methodology): “Our current work is precisely in this direction: The results of a terminology translation experiment appear in Navigli, Velardi, and Gangemi (2003), while preliminary results on a query expansion task are presented in Navigli and Velardi (2003).”
A fuzzy ontology and its application to news summarization (2005, background): “[14] propose the OntoLearn with ontology learning capability to extract relevant domain terms from a corpus of text.”
A Protégé Plug-In for Ontology Extraction from Text Based on Linguistic Analysis (2004, background): “, 1998), TextToOnto (Maedche and Staab, 2000; Maedche) and Ontolearn (Navigli et al., 2003).”
Automatic fuzzy ontology generation for semantic Web (2006, background): “Typically, ontology can be generated from various data types such as textual data [10], [11], [12], [13], [14], [15], [16], [17], dictionary [18], [19], knowledge-based [20] semistructured schemata [21], [22], [23], and relational schemata [24], [25], [26], [27].”
Book Review: Ontology Learning from Text: Methods, Evaluation and Applications, edited by Paul Buitelaar, Philipp Cimiano and Bernado Magnini (2005, background): “…the learning of taxonomies proposed in the literature, including Hearst patterns, the head-matching/NP-structure technique (originally proposed by Navigli, Velardi, and Gangemi [2003]), hyponymy information from WordNet, subsumption based on corpus-based syntactic features (an extension of…”