Sweetening Ontologies with DOLCE

By Aldo Gangemi, Nicola Guarino, Claudio Masolo, Alessandro Oltramari, Luc Schneider. International Conference Knowledge Engineering and Knowledge Management, 2002

1,276 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bbfb341398cf8a8eb76c957ccee6a6c805989612

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

1. Mitchell P. Marcus
2. Eduard H. Hovy
3. Ralph M. Weischedel
4. Christopher D. Manning
5. Peter Mika
6. Lance A. Ramshaw
7. J. Hendler
8. James A. Overton

In Molecular Biology

1. Peter Mika
2. Richard H. Scheuermann
3. J. Hendler
4. James A. Overton
5. Matthew H. Brush
6. Eduard H. Hovy
7. Bjoern Peters
8. Martha Palmer

Citations in the corpus

• Understanding the Semantic Web through Descriptions and Situations (2003, methodology): “The Web contains plenty of examples of such creatures: catalog subjects or topics, references to distributed resources, unstructured or semi-structured (but explicitly referenced) contents , such as plans, methods, regulations, formats, profiles, etc., and even linguistic elements and texts (taken i”
• Machine Common Sense Concept Paper (2018, background): “…and qualitative reasoning [8]), less formal knowledge‐based approaches (e.g., frames [9], and scripts [10]), and a number of efforts to create logic‐based ontologies (e.g., WordNet [11], VerbNet [12], SUMO [13], YAGO [14], DOLCE [15], and hundreds of smaller ontologies on the Semantic Web [16]).”
• The Ontology for Biomedical Investigations (2016, methodology): “Upper level ontologies were evaluated such as BFO[16], Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) [48], and Suggested Upper Merged Ontology (SUMO)[49] to put the collected terms in an established framework.”
• Open Knowledge Extraction Challenge (2015, neutral): “In this task by Entity we mean any discourse referent (the actors and objects around which a story unfolds), either named or anonymous that is an individual of one of the following DOLCE Ultra Lite classes [5], i.”
• An Intrinsic Information Content Metric for Semantic Similarity in WordNet (2004, background): “Fortunately, some research has emerged that looks for these inconsistencies allowing a restructure of the taxonomy ([20], [2]).”
• Deep learning for noise-tolerant RDFS reasoning (2019, methodology): “Consistency Checking In [82], the authors aimed to detect systematic errors in DBpedia by aligning the DBpedia ontology and the upper level ontology DOLCE-Zero [83].”
• OntoNotes: The 90% Solution (2006, background): “Omega, which has been used for MT, summarization, and database alignment, has been assembled semi-automatically by merging a variety of sources, including Princeton’s WordNet, New Mexico State University’s Mikrokosmos, and a variety of Upper Models, including DOLCE (Gangemi et al., 2002), SUMO (Nile”
• Ontological realism: A methodology for coordinated evolution of scientific ontologies (2010, neutral): “Both DOLCE and SUMO seem to us, however, to be much more coherent as ontologies for scientific purposes than the upper level of Cyc, which is marred not least by the fact that it is associated at lower levels with very many terms and definitions which, because of Cyc’s primary focus on formalizing wha”