Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites

By Roberto Navigli, Paola Velardi. International Conference on Computational Logic, 2004

484 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0cbb5f29b4d45f8849b1851b406f43449794917a

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In Artificial Intelligence

Citations in the corpus

Structural semantic interconnection: a knowledge-based approach to Word Sense Disambiguation (2005, methodology): “Statistical and natural language processing (NLP) tools are used for automatic extraction of terms [27].”
Word sense disambiguation: A survey (2009, methodology): “WSD has been used in semantic Web-related research .elds, like ontology learning, to build domain 10:54 R. Navigli taxonomies [Navigli et al. 2003; Navigli and Velardi 2004; Cimiano 2006] and enrich large-scale semantic networks [Navigli and Velardi 2005; Pennacchiotti and Pantel 2006; Snow et…”
A software engineering approach to ontology building (2009, methodology): “This task can be supported by using some automatic tools to extract knowledge from textual documents, such as OntoLearn [ 2 ], Text-To-Onto [WR12], or similar tools.”
Book Review: Ontology Learning from Text: Methods, Evaluation and Applications, edited by Paul Buitelaar, Philipp Cimiano and Bernado Magnini (2005, background): “OntoLearn is one of the more complex and sophisticated ontology-learning systems described in the literature (Navigli and Velardi 2004), and its current architecture involves ﬁve steps for which the authors provide quantitative evaluations.”
Ontology Learning from Text: An Overview (2005, background): “However, speciﬁcally in the ontology learning context, researchers have exploited the fact that ambiguous terms have very speciﬁc meanings in particular domains allowing for an integrated approach to sense disambiguation and domain speciﬁc synonym extraction (compare [5], [22], [30], [31] and [39]).”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “ogies 51 Burkart and Huber Table 15: Overview of research that improves ontologies Approach Description References - Automated generation Wong et al. (2011) OntoLearn Automated extraction, generation Navigli and Velardi (2004) - Semi-Automated engineering Maedche and Staab (2001) In the next section”
Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance (2006, methodology): “To match deﬁnitions in a semantic manner we adopted a knowledge-based Word Sense Disambiguation algorithm, Structural Semantic Interconnections (SSI, Navigli and Velardi (2004)).”
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation (2010, methodology): “Beyond WSD, graphbased methods have been adopted in many NLP tasks such as summarization [33]–[35], keyword extraction [34], sentiment analysis [36], sentence retrieval for question answering [37], ontology learning [ 38 ], human conversation analysis [39], and for estimating word dependency distrib”
OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models (2026, unknown): “Velardi et al.’s OntoLearn (Navigli and Velardi 2004) employed lexical patterns and syntactic parsing to induce taxonomies, with the Reloaded version (Velardi, Faralli, and Navigli 2013) using graph-based pruning algorithms to construct coherent tree hierarchies, successfully reconstructing parts…”