Scaling up all pairs similarity search
Proceedings of the 16th international conference on World Wide Web
Adaptive sorted neighborhood methods for efficient record linkage
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Ed-Join: an efficient algorithm for similarity joins with edit distance constraints
Proceedings of the VLDB Endowment
Combining a Logical and a Numerical Method for Data Reconciliation
Journal on Data Semantics XII
Creating relational data from unstructured and ungrammatical data sources
Journal of Artificial Intelligence Research
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
Discovering and Maintaining Links on the Web of Data
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Domain-independent entity coreference in RDF graphs
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Trie-join: efficient trie-based string similarity joins with edit-distance constraints
Proceedings of the VLDB Endowment
When owl: sameAs isn't the same: an analysis of identity in linked data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
A self-training approach for resolving object coreference on the semantic web
Proceedings of the 20th international conference on World wide web
Efficient exact edit similarity query processing with the asymmetric signature scheme
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Efficient similarity joins for near-duplicate detection
ACM Transactions on Database Systems (TODS)
Fast-join: An efficient method for fuzzy token matching based string similarity join
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Automatically generating data linkages using a domain-independent candidate selection approach
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Zhishi.me: weaving chinese linking open data
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
Mining information for instance unification
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Leveraging terminological structure for object reconciliation
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
Leveraging unlabeled data to scale blocking for record linkage
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication
IEEE Transactions on Knowledge and Data Engineering
Ontology Matching: State of the Art and Future Challenges
IEEE Transactions on Knowledge and Data Engineering
Domain-Independent Entity Coreference for Linking Ontology Instances
Journal of Data and Information Quality (JDIQ) - Special Issue on Entity Resolution
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Due to the decentralized nature of the Semantic Web, the same real world entity may be described in various data sources and assigned syntactically distinct identifiers. In order to facilitate data utilization in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process can also be referred to as Entity Coreference, i.e., finding which identifiers refer to the same real world entity. This proposal will investigate algorithms to solve this entity coreference problem in the Semantic Web in several aspects. The essence of entity coreference is to compute the similarity of instance pairs. Given the diversity of domains of existing datasets, it is important that an entity coreference algorithm be able to achieve good precision and recall across domains represented in various ways. Furthermore, in order to scale to large datasets, an algorithm should be able to intelligently select what information to utilize for comparison and determine whether to compare a pair of instances to reduce the overall complexity. Finally, appropriate evaluation strategies need to be chosen to verify the effectiveness of the algorithms.