A semiotic metrics suite for assessing the quality of ontologies
By Andrew Burton-Jones, Veda C. Storey, Vijayan Sugumaran, Punit Ahluwalia. Data & Knowledge Engineering, 2005
382 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/3f85ded18b349f4dae069d0e717ee83eddd8d73c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- M. Suárez-Figueroa
- Michele Missikoff
- Antonio De Nicola
- María Poveda-Villalón
- Roberto Navigli
- Asunción Gómez-Pérez
- Marko Grobelnik
- Janez Brank
In Information Systems
- M. Suárez-Figueroa
- María Poveda-Villalón
- Roberto Navigli
- Michele Missikoff
- Antonio De Nicola
- Asunción Gómez-Pérez
- Aldo Gangemi
Citations in the corpus
- A software engineering approach to ontology building (2009, methodology): “This paper illustrates the phases, the steps, and intermediate outcomes of the UPON method, designed in accordance with the UP method, aimed at guiding ontology engineers in the production of an effective and valuable domain ontology [4].”
- OOPS! (OntOlogy Pitfall Scanner!): An On-line Tool for Ontology Evaluation (2014, methodology): “…evaluating an ontology depending on its final (re)use (Suárez-Figueroa, 2010); some others propose quality models based on features, criteria, and metrics (Burton-Jones et al, 2005); whereas others present methods for pattern-based evaluation (Djedidi, & Aufaure, 2010; Presutti et al., 2008).”
- A SURVEY OF ONTOLOGY EVALUATION TECHNIQUES (2005, background): “(2) Each concept c of the ontology is represented by a set of terms including its name in the ontology and the hypernyms of this name, taken from WordNet.”