“I am very very happy to work with Yefei for more than one and half year, I have learned a lot from him. He proposed a lot of novel ideas. We together launched several good features to Google display ads. I think Yefei is a very good leader from the aspects of tech and non-tech. ”
About
Activity
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Join us in celebrating the Class of 2025! Watch our Convocation webcast, beginning at 9:15 a.m. CT: https://lnkd.in/gdy-F-Zx
Join us in celebrating the Class of 2025! Watch our Convocation webcast, beginning at 9:15 a.m. CT: https://lnkd.in/gdy-F-Zx
Liked by Yefei Peng, Ph.D.
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Every campus needs a good window seat 📸 Jason Smith
Every campus needs a good window seat 📸 Jason Smith
Liked by Yefei Peng, Ph.D.
Experience & Education
Publications
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Ontology Mapping: As a Binary Classification Problem
Concurrency and Computation: Practice and Experience
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An adaptive ontology mapping approach with neural network based constraint satisfaction
Journal of Web Semantics
Ontology mapping seeks to find semantic correspondences between similar elements of different ontolo- gies. It is a key challenge to achieve semantic interoperability in building the Semantic Web. This paper proposes a new generic and adaptive ontology mapping approach, called the PRIOR+, based on propa- gation theory, information retrieval techniques and artificial intelligence. The approach consists of three major modules, i.e., the IR-based similarity generator, the adaptive similarity…
Ontology mapping seeks to find semantic correspondences between similar elements of different ontolo- gies. It is a key challenge to achieve semantic interoperability in building the Semantic Web. This paper proposes a new generic and adaptive ontology mapping approach, called the PRIOR+, based on propa- gation theory, information retrieval techniques and artificial intelligence. The approach consists of three major modules, i.e., the IR-based similarity generator, the adaptive similarity filter and weighted similar- ity aggregator, and the neural network based constraint satisfaction solver. The approach first measures both linguistic and structural similarity of ontologies in a vector space model, and then aggregates them using an adaptive method based on their harmonies, which is defined as an estimator of performance of similarity. Finally to improve mapping accuracy the interactive activation and competition neural network is activated, if necessary, to search for a solution that can satisfy ontology constraints. The experimental results show that harmony is a good estimator of f-measure; the harmony based adaptive aggregation outperforms other aggregation methods; neural network approach significantly boosts the performance in most cases. Our approach is competitive with top-ranked systems on benchmark tests at OAEI campaign 2007, and performs the best on real cases in OAEI benchmark tests.
Other authorsSee publication -
Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences Among Ontologies
In the Proceedings of the 9th International Semantic Web Conference (ISWC)
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Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences Among Ontologies
In the Proceedings of the 5th International Workshop on Ontology Matching (OM ) at 9th International Semantic Web Conference (ISWC)
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Supporting information access in e-learning by integrating digital libraries and ontology
Online Informatioin Review
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Learning to Map Ontologies with Neural Network
In the Proceedings of the 4th International Workshop on Ontology Matching (OM) at 8th International Semantic Web Conference (ISWC)
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A Harmony Based Adaptive Ontology Mapping Approach
The 2008 International Conference on Semantic Web and Web Services (SWWS'08)
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Neural Network based Constraint Satisfaction in Ontology Mapping
AAAI Conference on Artificial Intelligence
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Ontology Mapping: As a Binary Classification Problem
In the Proceedings of the 4th International Conference on Semantics, Knowledge and Grid
Patents
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Content Selection
US 10417286
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Content rate display adjustment between different categories of online documents in a computer network environment
US 10614491
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LOCATION-BASED PREDICTION OF TRANSPORT SERVICES
US 20160335576
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Network Computing System Deploying Treatment Content For An Application-based Service
US 2019 287 126A1
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Predictive System For Selecting Groups Of Users In Connection With A Network Service
US 20190066161
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Smart User-Customized Graphical Keyboard
US 8850349
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System And Method For Generating Subphrase Queries
US 20090198671
Honors & Awards
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Google Display Network (GDN) Innovation Gold Award
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Mobile Display Ads Recommendation and Personalization
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Google Display Network (GDN) Innovation Gold Award
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Deep learning based Display Ads Recommendation and Personalization
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Yahoo! You Rock Team Award
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Yahoo! Super Star Team Award
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Recommendations received
1 person has recommended Yefei
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