A survey on ontology mapping

By Namyoun Choi, Il-Yeol Song, Hyoil Han. 2006

683 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems, Knowledge Graphs & Representation

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. Pavel Shvaiko
2. Jérôme Euzenat
3. Simon Lacoste-Julien
4. Zoubin Ghahramani
5. Bhavana Dalvi
6. Grigoris Antoniou
7. Giorgos Flouris
8. Dimitris Plexousakis

In Computer Networks and Communications

1. Pavel Shvaiko
2. Jérôme Euzenat
3. Grigoris Antoniou
4. Giorgos Flouris
5. Dimitris Plexousakis
6. Haridimos Kondylakis
7. Dimitris Manakanatas
8. Simon Lacoste-Julien

In Information Systems

1. Pavel Shvaiko
2. Jérôme Euzenat
3. Grigoris Antoniou
4. Giorgos Flouris
5. Dimitris Plexousakis
6. Haridimos Kondylakis
7. Dimitris Manakanatas
8. Zoubin Ghahramani

In Knowledge Graphs & Representation

1. Jérôme Euzenat
2. Pavel Shvaiko
3. Simon Lacoste-Julien
4. Alex Davies
5. Gjergji Kasneci
6. Thore Graepel
7. Zoubin Ghahramani
8. Konstantina Palla

Citations in the corpus

• SIGMa: simple greedy matching for aligning large knowledge bases (2012, background): “A significant amount of research has been done in this area – particularly under the umbrella term of ontology matching [7, 16, 12].”
• Ontology Matching with Semantic Verification (2009, neutral): “Ontology matching consists in deriving an alignment consisting of correspondences between two ontologies [8].”
• Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, background): “Importantly, having enriched a given KB with glosses using the proposed framework, this will facilitate its alignment and integration with other KBs [32].”
• Ontology Matching: State of the Art and Future Challenges (2013, background): “As evaluations of the recent years indicate, the field of ontology matching has made a measurable improvement, the speed of which is albeit slowing down.”
• Ontology Matching (2007, neutral): “There have already been some comparisons of matching systems, in particular in (Parent and Spaccapietra 2000; Rahm and Bernstein 2001; Do et al. 2002; Kalfoglou and Schorlemmer 2003b; Noy 2004a; Doan and Halevy 2005; Shvaiko and Euzenat 2005; Choi et al. 2006; Bellahsene et al. 2011).”
• Ontology change: classification and survey (2008, background): “It is a common practice in the literature to consider heterogeneity resolution to be an internal part of ontology merging or integration (De Bruijn et al. 2004), (Choi et al. 2006), (Heflin et al. 1999), (Pinto et al. 1999).”