Automating the approximate record-matching process
Information Sciences—Informatics and Computer Science: An International Journal
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
QoS in Ontology-Based Service Classification and Discovery
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Service-Based Semantic Search in P2P Systems
ECOWS '09 Proceedings of the 2009 Seventh IEEE European Conference on Web Services
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
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
Legal document clustering with built-in topic segmentation
Proceedings of the 20th ACM international conference on Information and knowledge management
Structured data clouding across multiple webs
Information Systems
Leveraging terminological structure for object reconciliation
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
Tailoring linked data exploration through inCloud filtering
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Data Linking for the Semantic Web
International Journal on Semantic Web & Information Systems
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The availability of large collections of linked data that can be accessed through public services and search endpoints requires methods and techniques for reducing the data complexity and providing high-level views of data contents defined according to users specific needs. To this end, a crucial step is the definition of data classification methods and techniques for the thematic aggregation of linked data. In this paper, we propose matching and clustering techniques specifically conceived for linked data classification, by focusing on the high level of heterogeneity of data descriptions in terms of the number and kind of their descriptive features.