Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
The Ontolingua Server: a tool for collaborative ontology construction
International Journal of Human-Computer Studies - Special issue: innovative applications of the World Wide Web
Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor
Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
The Knowledge Engineering Review
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A reference ontology for biomedical informatics: the foundational model of anatomy
Journal of Biomedical Informatics - Special issue: Unified medical language system
Web ontology segmentation: analysis, classification and use
Proceedings of the 15th international conference on World Wide Web
Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond)
Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond)
Matching large schemas: Approaches and evaluation
Information Systems
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Towards imaging large-scale ontologies for quick understanding and analysis
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Partition-Based block matching of large class hierarchies
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Matching large scale ontology effectively
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Automatic alignment of ontology eliminating the probable misalignments
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Scalability in ontology instance matching of large semantic knowledge base
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Ontology instance matching by considering semantic link cloud
ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
Techniques for discovering correspondences between ontologies
International Journal of Web and Grid Services
On matching large life science ontologies in parallel
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
A structure-based similarity spreading approach for ontology matching
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Exploiting relation extraction for ontology alignment
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Ontology and instance matching
Knowledge-driven multimedia information extraction and ontology evolution
evaluating the stability and credibility of ontology matching methods
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
A clustering-based approach for large-scale ontology matching
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Improving the accuracy of ontology alignment through ensemble fuzzy clustering
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
Neighbour based structural proximity measures for ontology matching systems
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Ontology Instance Matching based MPEG-7 Resource Integration
International Journal of Multimedia Data Engineering & Management
Ontology matching benchmarks: Generation, stability, and discriminability
Web Semantics: Science, Services and Agents on the World Wide Web
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It has been a formidable task to achieve efficiency and scalability for the alignment between two massive, conceptually similar ontologies. Here we assume, an ontology is typically given in RDF (Resource Description Framework) or OWL (Web Ontology Language) and can be represented by a directed graph. A straightforward approach to the alignment of two ontologies entails an O(N^2) computation by comparing every combination of pairs of nodes from given two ontologies, where N denotes the average number of nodes in each ontology. Our proposed algorithm called Anchor-Flood algorithm, boasting of O(Nlog@?(N)) computation on the average, starts off with an anchor, a pair of ''look-alike'' concepts from each ontology, gradually exploring concepts by collecting neighboring concepts, thereby taking advantage of locality of reference in the graph data structure. It outputs a set of alignments between concepts and properties within semantically connected subsets of two entire graphs, which we call segments. When similarity comparison between a pair of nodes in the directed graph has to be made to determine whether two given ontologies are aligned or not, we repeat the similarity comparison between a pair of nodes, within the neighborhood pairs of two ontologies surrounding the anchor iteratively until the algorithm meets that ''either all the collected concepts are explored, or no new aligned pair is found''. In this way, we can significantly reduce the computational time for the alignment. Moreover, since we only focus on segment-to-segment comparison, regardless of the entire size of ontologies, our algorithm not only achieves high performance, but also resolves the scalability problem in aligning ontologies. Our proposed algorithm reduces the number of seemingly-aligned but actually misaligned pairs. Through several examples with large ontologies, we will demonstrate the features of our Anchor-Food algorithm.