Original Contribution: Stacked generalization
Neural Networks
The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning domain-independent string transformation weights for high accuracy object identification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to match and cluster large high-dimensional data sets for data integration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Iterative record linkage for cleaning and integration
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Disambiguating Web appearances of people in a social network
WWW '05 Proceedings of the 14th international conference on World Wide Web
Reference reconciliation in complex information spaces
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A testbed for people searching strategies in the WWW
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting relationships for object consolidation
Proceedings of the 2nd international workshop on Information quality in information systems
Joint deduplication of multiple record types in relational data
Proceedings of the 14th ACM international conference on Information and knowledge management
Combining Multiple Clusterings by Soft Correspondence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Domain-independent data cleaning via analysis of entity-relationship graph
ACM Transactions on Database Systems (TODS)
Improving Grouped-Entity Resolution Using Quasi-Cliques
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Entity Resolution with Markov Logic
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Combining Information Extraction Systems Using Voting and Stacked Generalization
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Adaptive graphical approach to entity resolution
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Towards breaking the quality curse.: a web-querying approach to web people search.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Web People Search via Connection Analysis
IEEE Transactions on Knowledge and Data Engineering
WEST: Modern Technologies for Web People Search
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
The SemEval-2007 WePS evaluation: establishing a benchmark for the web people search task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
PSNUS: web people name disambiguation by simple clustering with rich features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Selecting diversifying heuristics for cluster ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Self-tuning in graph-based reference disambiguation
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Frameworks for entity matching: A comparison
Data & Knowledge Engineering
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
EIF: a framework of effective entity identification
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Entity Resolution and Information Quality
Entity Resolution and Information Quality
What have fruits to do with technology?: the case of Orange, Blackberry and Apple
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Attribute and object selection queries on objects with probabilistic attributes
ACM Transactions on Database Systems (TODS)
Exploiting Web querying for Web people search
ACM Transactions on Database Systems (TODS)
Quality-aware similarity assessment for entity matching in Web data
Information Systems
Scaling multiple-source entity resolution using statistically efficient transfer learning
Proceedings of the 21st ACM international conference on Information and knowledge management
Adaptive Connection Strength Models for Relationship-Based Entity Resolution
Journal of Data and Information Quality (JDIQ) - Special Issue on Entity Resolution
Super-EGO: fast multi-dimensional similarity join
The VLDB Journal — The International Journal on Very Large Data Bases
Big data challenge: a data management perspective
Frontiers of Computer Science: Selected Publications from Chinese Universities
Query-driven approach to entity resolution
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
Entity Resolution (ER) is an important real world problem that has attracted significant research interest over the past few years. It deals with determining which object descriptions co-refer in a dataset. Due to its practical significance for data mining and data analysis tasks many different ER approaches has been developed to address the ER challenge. This paper proposes a new ER Ensemble framework. The task of ER Ensemble is to combine the results of multiple base-level ER systems into a single solution with the goal of increasing the quality of ER. The framework proposed in this paper leverages the observation that often no single ER method always performs the best, consistently outperforming other ER techniques in terms of quality. Instead, different ER solutions perform better in different contexts. The framework employs two novel combining approaches, which are based on supervised learning. The two approaches learn a mapping of the clustering decisions of the base-level ER systems, together with the local context, into a combined clustering decision. The paper empirically studies the framework by applying it to different domains. The experiments demonstrate that the proposed framework achieves significantly higher disambiguation quality compared to the current state of the art solutions.