Learning to map between ontologies on the semantic web

By AnHai Doan, Jayant Madhavan, Pedro M. Domingos, Alon Y. Halevy. The Web Conference, 2002

1,086 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8f4edd71b1819eff95b669ea9042ab0f3705fe0b

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. Natasha Noy
4. Alon Y. Halevy
5. Erhard Rahm
6. Pedro M. Domingos
7. Jayant Madhavan
8. Lise Getoor

In Molecular Biology

1. Pavel Shvaiko
2. Natasha Noy
3. Heiko Paulheim
4. Marc Ehrig
5. Mark A. Musen
6. Johannes Fürnkranz
7. Yannis Kalfoglou
8. Marco Schorlemmer

Citations in the corpus

• Semantic integration: a survey of ontology-based approaches (2004, background): “GLUE uses multiple learners exploiting information in concept instances and tax­onomic structure of ontologies.”
• The PROMPT suite: interactive tools for ontology merging and mapping (2003, background): “, AnchorPrompt, GLUE (Doan et al., 2002), OBSERVER (Mena et al.”
• Cleaning Framework for Big Data - Object Identification and Linkage (2015, methodology): “The probabilistic constraint matching algorithms takes two ontologies as input and calculates several similarity measures between data items [10].”
• Generic schema matching, ten years later (2011, background): “ Graph matching – based on comparing the relationships between elements in the schema graphs by, for example, either fixed-point computations on a similarity propagation graph [53], or probabilistic constraint satisfaction algorithms [22].”
• Meteor-s web service annotation framework (2004, methodology): “The techniques used are also varied, ranging from machine learning [22][23], graph analysis [20][24] to heuristic based matching [24].”
• Ontology Mapping - An Integrated Approach (2004, background): “We can therefore infer that concepts that have the same instances are the same (R10) [10].”
• Link mining: a survey (2005, background): “[32], but there is much more to be done.”
• Unsupervised generation of data mining features from linked open data (2012, methodology): “Since some datasets in Linked Open Data use multiple ontologies in parallel, they allow for applying an instance-based matching approach [10], i.”
• Usage patterns of collaborative tagging systems (2006, background): “Currently this is being performed, problematically, on a small scale by experts and, equally problematically, on a large scale by machines [2, 3].”
• Data integration: the teenage years (2006, background): “[33] Anhai Doan, Jayant Madhavan, Pedro Domingos, and Alon Halevy.”