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Crowdsourcing systems on the World-Wide Web
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.
Deep Learning for Entity Matching: A Design Space Exploration
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.
Learning to map between ontologies on the semantic web
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.
Reconciling schemas of disparate data sources: a machine-learning approach
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.
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS
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.
Deep entity matching with pre-trained language models
- Yuliang Li, Jinfeng Li, Yoshihiko Suhara, A. Doan, W. Tan
- Computer ScienceProceedings of the VLDB Endowment
- 1 April 2020
The fine-tune and cast EM as a sequence-pair classification problem to leverage Transformer-based language models with a simple architecture and establish that Ditto can achieve the previous SOTA results with at most half the number of labeled data.
Learning to match ontologies on the Semantic Web
- A. Doan, J. Madhavan, Robin Dhamankar, Pedro M. Domingos, A. Halevy
- Computer ScienceThe VLDB journal
- 1 November 2003
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.
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.…
Principles of Data Integration
iMAP: discovering complex semantic matches between database schemas
- Robin Dhamankar, Yoonkyong Lee, A. Doan, A. Halevy, Pedro M. Domingos
- Computer ScienceACM SIGMOD Conference
- 13 June 2004
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.