'Semantic matching' presentation slideshows

Support for context-driven applications in Ambient Intelligence environments

Support for context-driven applications in Ambient Intelligence environments. PhD Defense. Davy Preuveneers. Promotor: Prof. Dr. ir. Y. Berbers July 1, 2009. Introduction Middleware Contributions Evaluation Conclusions. Context-driven applications. Applications

1.2k views • 103 slides

SCHEMA-BASED SEMANTIC MATCHING

826 views • 58 slides

SCHEMA-BASED SEMANTIC MATCHING

595 views • 58 slides

Using Non-Taxonomic Knowledge to Improve Semantic Matching

Using Non-Taxonomic Knowledge to Improve Semantic Matching. Peter Yeh July 22, 2003. Talk Outline. Introduction Analysis of Existing Techniques Our Approach Initial Evaluation Proposed Work. Introduction.

605 views • 49 slides

Textkernel : Spinoff from R&D in machine learning and language technology

365 views • 36 slides

Discovering Missing Background Knowledge in Ontology Matching

312 views • 31 slides

Lu Yang, Biplab Sarker, Virendrakumar C. Bhavsar and Harold Boley bhavsar@unb

Range Similarity Measures between Buyers and Sellers in e-Marketplaces. Lu Yang, Biplab Sarker, Virendrakumar C. Bhavsar and Harold Boley bhavsar@unb.ca Faculty of Computer Science University of New Brunswick (UNB) Fredericton, Canada IICAI, December 20, 2005. Motivation

315 views • 31 slides

S-Match: an Algorithm and an Implementation of Semantic Matching

S-Match: an Algorithm and an Implementation of Semantic Matching. Pavel Shvaiko. paper with Fausto Giunchiglia and Mikalai Yatskevich. 1 st European Semantic Web Symposium, 11 May 2004, Crete, Greece. Semantic Matching The S-Match Algorithm

600 views • 26 slides

S-Match: an Algorithm and an Implementation of Semantic Matching

S-Match: an Algorithm and an Implementation of Semantic Matching. Pavel Shvaiko. paper with Fausto Giunchiglia and Mikalai Yatskevich. 1 st European Semantic Web Symposium, 11 May 2004, Crete, Greece. Semantic Matching The S-Match Algorithm

264 views • 26 slides

Search-based Neural Structured Learning for Sequential Question Answering

Search-based Neural Structured Learning for Sequential Question Answering. Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang. ACL-2017. Answer Highly Compositional Questions. Challenging research problem Advocated in semantic parsing [Pasupat & Liang 2015]

238 views • 23 slides

MatchIT 1.1: Data Integration with Semantic Mapping Technologies

MatchIT 1.1: Data Integration with Semantic Mapping Technologies. Michael Schidlowsky Sr. Software Architect. Data Integration. Motivated by: Organizational Changes Mergers and Acquisitions Internal reorganizations (e.g., DHS) Data Mining Standards Conformance Migration Efforts

383 views • 22 slides

MatchIT 1.1: Data Integration with Semantic Mapping Technologies

MatchIT 1.1: Data Integration with Semantic Mapping Technologies. Michael Schidlowsky Sr. Software Architect. Data Integration. Motivated by: Organizational Changes Mergers and Acquisitions Internal reorganizations (e.g., DHS) Data Mining Standards Conformance Migration Efforts

228 views • 22 slides

Policy Management & Enforcement

Policy Management & Enforcement. Overview. S3MS Deployment Center Already demoed at M18 Off-device Inlining demo Already demoed at M18 DEMO 1: On-device Matching DEMO 2: On-device Inlining. S3MS Deployment Center. S3MS Deployment Center. Main changes between M18 and M24

283 views • 18 slides

BACKGROUND KNOWLEDGE IN ONTOLOGY MATCHING

BACKGROUND KNOWLEDGE IN ONTOLOGY MATCHING. Pavel Shvaiko. joint work with Fausto Giunchiglia and Mikalai Yatskevich. INFINT 2007 Bertinoro Workshop on Information Integration October 1, Italy. Outline. Introduction Lack of background knowledge Conclusions and future directions.

604 views • 17 slides

BACKGROUND KNOWLEDGE IN ONTOLOGY MATCHING

BACKGROUND KNOWLEDGE IN ONTOLOGY MATCHING. Pavel Shvaiko. joint work with Fausto Giunchiglia and Mikalai Yatskevich. INFINT 2007 Bertinoro Workshop on Information Integration October 1, Italy. Outline. Introduction Lack of background knowledge Conclusions and future directions.

324 views • 17 slides

A Comparison of Web Service Interface Similarity Measures

A Comparison of Web Service Interface Similarity Measures. Natallia Kokash. Introduction. Semantic Service Descriptions Web Service Description Languages Questions Motivating Example Related Work Matching Algorithm Structural Matching Lexical Matching Vector-Space Model (VSM)

281 views • 14 slides