Ontology Matching: A Machine Learning Approach

By AnHai Doan, Jayant Madhavan, Pedro M. Domingos, Alon Y. Halevy. Handbook on Ontologies, 2004

585 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/32d12b621e75cdb5942896c16421496bdfd4a6fa

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. Luke S. Zettlemoyer
2. Percy Liang
3. Mohit Bansal
4. Hannaneh Hajishirzi
5. Jianfeng Gao
6. Jonathan Berant
7. Yoshua Bengio
8. Caiming Xiong

In Information Systems

1. Pavel Shvaiko
2. Jérôme Euzenat
3. Luke S. Zettlemoyer
4. Eunsol Choi
5. Yoav Artzi
6. Tom Kwiatkowski
7. Nadia Burkart
8. Yoshua Bengio

In Management Science and Operations Research

1. Jérôme Euzenat
2. Pavel Shvaiko
3. Nadia Burkart
4. Tom Kwiatkowski
5. Jennifer Tour Chayes
6. Yoshua Bengio
7. Marco F. Huber
8. Luke S. Zettlemoyer

Citations in the corpus

• Ontology Matching with Semantic Verification (2009, unknown): “GLUE [11] discovers mappings through multiple learners that analyze the taxonomy and the information within concept instances of ontologies.”
• Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, methodology): “Finally, the databases research community has a long history of developing schema matching techniques (Doan et al., 2004; Euzenat et al., 2007), which has inspired more recent work on distant supervision for relation extraction with Freebase (Zhang et al., 2012).”
• Large-scale Semantic Parsing via Schema Matching and Lexicon Extension (2013, background): “…have resorted to defining standard similarity metrics between relations and attributes, as well as machine learning algorithms for learning and predicting matches between relations (Doan et al., 2004; Wick et al., 2008b; Wick et al., 2008a; Nottelmann and Straccia, 2007; Berlin and Motro, 2006).”
• Tackling Climate Change with Machine Learning (2019, background): “[313] AnHai Doan, Jayant Madhavan, Pedro Domingos, and Alon Halevy.”
• A Survey on the Explainability of Supervised Machine Learning (2020, background): “the dierent 52 A Survey on the Explainability of Supervised Machine Learning Table 16: Overview of ontology approaches that improve ML Approach Description References GLUE Improve ontologies with ML Doan et al. (2004) - Improve ML with ontologies Tsymbal et al. (2007) - Improve ML with ontologies Xu”
• Ontology Matching (2007, methodology): “It has been used in various matching approaches as a classifier (Doan et al. 2004; Straccia and Troncy 2006; Lambrix and Tan 2006; Nandi and Bernstein 2009; Spiliopoulos et al. 2010; Esposito et al. 2010; Tournaire et al. 2011).”