Semantic Matching: Algorithms and Implementation
By Fausto Giunchiglia, Pavel Shvaiko. Journal on Data Semantics, 2007
559 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/16ca20410ae8a146ea05e152c2872fcaefbc3bc9
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
- Pavel Shvaiko
- Jérôme Euzenat
- Cássia Trojahn dos Santos
- Heiner Stuckenschmidt
- Christian Meilicke
- Yuzhong Qu
- Wei Hu
- Jie Tang
In Computer Networks and Communications
- Pavel Shvaiko
- Jérôme Euzenat
- Heiner Stuckenschmidt
- Cássia Trojahn dos Santos
- Christian Meilicke
- Peter Christen
- Wei Hu
- Qiong Luo
In Information Systems
- Pavel Shvaiko
- Jérôme Euzenat
- Cássia Trojahn dos Santos
- Heiner Stuckenschmidt
- Christian Meilicke
- Wei Hu
- Yuzhong Qu
- Jie Tang
Citations in the corpus
- Ten Challenges for Ontology Matching (2008, background): “The basic version of S-Match was using a standard DPLL-based satisfiability procedure of SAT4J 11 .”
- GMO: A Graph Matching for Ontologies (2005, background): “3, the input mapping to GMO is a set of matched pairs generated by another component, called LMO (A Linguistic Matching for Ontologies).”
- Knowledge Graph Completion: A Review (2020, methodology): “Semantic matching energy model (SME) [76] uses neural network structure to achieve semantic matching, mining semantic relationships”
- RiMOM: A Dynamic Multistrategy Ontology Alignment Framework (2009, background): “There are several surveys on schema matching and ontology alignment [18], [22], [23], [31], [55], [57], [61].”
- Ontology Alignment Evaluation Initiative: Six Years of Experience (2011, background): “Other kinds of relations that were investigated are subclass [67; 32] and disjointness relations [54; 32].”
- Data Matching (2012, background): “Besides relational databases, recent interest in other structured types of data, such as XML schemas and ontologies, has sparked the development of novel techniques that are aimed at integrating and matching such types of data [24, 120, 270].”
- Ontology Matching: State of the Art and Future Challenges (2013, methodology): “org/ In addition, the work on S-Match [116] discussed an automatic approach to deal with the lack of background knowledge in matching tasks by using semantic matching [117, 118] iteratively.”