• Publications
  • Influence
Crowdsourcing systems on the World-Wide Web
TLDR
The practice of crowdsourcing is transforming the Web and giving rise to a new field of inquiry called "crowdsourcing", which aims to provide real-time information about events in a democratic manner. Expand
Learning to map between ontologies on the semantic web
TLDR
Glue is described, a system that employs machine learning techniques to find semantic mappings between ontologies and is distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. Expand
Reconciling schemas of disparate data sources: a machine-learning approach
TLDR
LSD is a system that employs and extends current machine-learning techniques to semi-automatically find semantic mappings between the source schemas and the mediated schema, and its architecture is extensible to additional learners that may exploit new kinds of information. Expand
Deep Learning for Entity Matching: A Design Space Exploration
TLDR
The results show that DL does not outperform current solutions on structured EM, but it can significantly outperform them on textual and dirty EM, which suggests that practitioners should seriously consider using DL for textual anddirty EM problems. Expand
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS
TLDR
This work presents Tuffy, a scalable Markov Logic Networks framework that achieves scalability via three novel contributions: a bottom-up approach to grounding, a novel hybrid architecture that allows to perform AI-style local search efficiently using an RDBMS, and a theoretical insight that shows when one can improve the efficiency of stochastic local search. Expand
Ontology Matching: A Machine Learning Approach
This chapter studies ontology matching: the problem of finding the semantic mappings between two given ontologies. This problem lies at the heart of numerous information processing applications.Expand
Learning to match ontologies on the Semantic Web
TLDR
GLUE is described, a system that employs machine learning techniques to find semantic mappings between ontologies and is distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. Expand
Principles of Data Integration
TLDR
This book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. Expand
iMAP: discovering complex semantic matches between database schemas
TLDR
The iMAP system is described, which semi-automatically discovers both 1-1 and complex matches, and introduces a novel feature that generates explanation of predicted matches, to provide insights into the matching process and suggest actions to converge on correct matches quickly. Expand
Corpus-based schema matching
TLDR
Experimental results are presented that demonstrate corpus-based matching outperforms direct matching (without the benefit of a corpus) in multiple domains. Expand
...
1
2
3
4
5
...