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What is SEMANTIC MATCHING? What does SEMANTIC MATCHING mean? SEMANTIC MATCHING meaning
What is SEMANTIC MATCHING? What does SEMANTIC MATCHING mean? SEMANTIC MATCHING meaning
Published: 2017/04/16
Channel: The Audiopedia
Textkernel Match! - Semantic Matching Software Demo
Textkernel Match! - Semantic Matching Software Demo
Published: 2012/10/29
Channel: Textkernel
Fitman webinar 2015 09-21 Metadata and Ontologies Semantic Matching (SeMa)
Fitman webinar 2015 09-21 Metadata and Ontologies Semantic Matching (SeMa)
Published: 2015/09/25
Channel: FITMAN FI
What is PHONO-SEMANTIC MATCHING? What does PHONO-SEMANTIC MATCHING mean*
What is PHONO-SEMANTIC MATCHING? What does PHONO-SEMANTIC MATCHING mean*
Published: 2017/02/02
Channel: The Audiopedia
Consortium Wizard based on Semantic matching for the Episteme project
Consortium Wizard based on Semantic matching for the Episteme project
Published: 2012/11/25
Channel: GSI UPM Grupo de Sistemas Inteligentes
FITMAN SE Metadata Ontologies Semantic Matching
FITMAN SE Metadata Ontologies Semantic Matching
Published: 2014/05/19
Channel: FITMAN FI
NLP 02 : String Similarity, Cosine Similarity, Levenshtein Distance
NLP 02 : String Similarity, Cosine Similarity, Levenshtein Distance
Published: 2014/02/25
Channel: Gyu-Ho Lee
Semantic Optical Flow (CVPR 2016)
Semantic Optical Flow (CVPR 2016)
Published: 2016/06/22
Channel: Michael Black
Consortium Wizard based on Semantic matching for the Episteme project
Consortium Wizard based on Semantic matching for the Episteme project
Published: 2014/02/03
Channel: GSI UPM Grupo de Sistemas Inteligentes
Text mining for ontology learning and matching
Text mining for ontology learning and matching
Published: 2014/12/15
Channel: togotv
The Power of a Semantic Network
The Power of a Semantic Network
Published: 2017/02/28
Channel: censhare
02 01 what Are Web Semantics 1
02 01 what Are Web Semantics 1
Published: 2016/07/18
Channel: lynda arabic
Metadata and Ontologies Semantic Matching Specific Enabler FITMAN SeMa
Metadata and Ontologies Semantic Matching Specific Enabler FITMAN SeMa
Published: 2013/12/04
Channel: FITMAN FI
Textkernel: Specialist in Semantic Recruitment Technology (HD)
Textkernel: Specialist in Semantic Recruitment Technology (HD)
Published: 2014/10/20
Channel: Textkernel
Semantic Match! in HR - Textkernel
Semantic Match! in HR - Textkernel
Published: 2013/10/22
Channel: Textkernel
Product Match: Semantic Based Product Information Management
Product Match: Semantic Based Product Information Management
Published: 2012/06/25
Channel: dataladder
Schema matching and mapping lesson 1
Schema matching and mapping lesson 1
Published: 2017/01/17
Channel: SA - Data Analyst
CS511@UIUC Project Midterm: Semantic Matching
CS511@UIUC Project Midterm: Semantic Matching
Published: 2010/11/12
Channel: ggott216
Natural Language Interfacee To Database Using Semantic Matching For Converting English To Sql (java)
Natural Language Interfacee To Database Using Semantic Matching For Converting English To Sql (java)
Published: 2015/04/14
Channel: 1 Crore Projects Vadapalani
What is CALQUE? What does CALQUE mean? CALQUE meaning, definition & explanation
What is CALQUE? What does CALQUE mean? CALQUE meaning, definition & explanation
Published: 2016/07/18
Channel: The Audiopedia
NDSS 2017:  Automated Synthesis of Semantic Malware Signatures using Maximum Satisfiability
NDSS 2017: Automated Synthesis of Semantic Malware Signatures using Maximum Satisfiability
Published: 2017/04/24
Channel: NDSS Symposium
HR Slam 2013 Textkernel   Semantic Match! in HR
HR Slam 2013 Textkernel Semantic Match! in HR
Published: 2012/12/15
Channel: HRInnovationSlam
Knowledge discovery in ontology matching
Knowledge discovery in ontology matching
Published: 2010/10/19
Channel: joel1070812
Semantic search engine
Semantic search engine
Published: 2016/10/09
Channel: Magha Ram
Semantic Definition and  Matching For National Spatial Data Infrastructure
Semantic Definition and Matching For National Spatial Data Infrastructure
Published: 2013/06/13
Channel: Coğrafi Bilgi Sistemleri Genel Müdürlüğü
Language Power
Language Power 'Semantic Priming'.
Published: 2012/04/05
Channel: Debbie Halls-Evans
[Samsung Smart TV Semantic SDK Tutorial #1] Semantic service discovery and matching tools
[Samsung Smart TV Semantic SDK Tutorial #1] Semantic service discovery and matching tools
Published: 2014/04/02
Channel: 박유미
Compiler Design lecture: Semantic Analysis, various Phases of compiler | 15
Compiler Design lecture: Semantic Analysis, various Phases of compiler | 15
Published: 2015/08/17
Channel: Gate Instructors
Coccinelle: A program matching and transformation tool
Coccinelle: A program matching and transformation tool
Published: 2015/01/16
Channel: Linux.conf.au 2015 -- Auckland, New Zealand
Can algorithm do a better job of matching the right candidates with the right jobs? #RecHangout
Can algorithm do a better job of matching the right candidates with the right jobs? #RecHangout
Published: 2016/07/06
Channel: Louis Welcomme
CVPR14: Video Classification using Semantic Concept Co-occurrences
CVPR14: Video Classification using Semantic Concept Co-occurrences
Published: 2014/06/09
Channel: Amir R. Zamir
Schema matching and mapping lesson 2
Schema matching and mapping lesson 2
Published: 2017/01/17
Channel: SA - Data Analyst
Black Hat EU 2013 - OptiSig: Semantic Signature for Metamorphic Malware
Black Hat EU 2013 - OptiSig: Semantic Signature for Metamorphic Malware
Published: 2013/10/17
Channel: Black Hat
Stackathon Presentation: The Block
Stackathon Presentation: The Block
Published: 2017/08/17
Channel: Fullstack Academy
Graph vs. Semantic Graph Databases - Selecting the Right Database for Your Next Project
Graph vs. Semantic Graph Databases - Selecting the Right Database for Your Next Project
Published: 2014/07/23
Channel: AllegroGraph
Treo: Semantic Search over Schema & Vocabularies
Treo: Semantic Search over Schema & Vocabularies
Published: 2013/09/04
Channel: Treo Deri
Enhancing Search Engine Query Relevance using Source and Target Schema Semantic Similarity
Enhancing Search Engine Query Relevance using Source and Target Schema Semantic Similarity
Published: 2017/08/09
Channel: Sumit Jain
Beyond Keywords: Moving From Words to Concepts
Beyond Keywords: Moving From Words to Concepts
Published: 2014/07/15
Channel: ISOOSI Research Engine
The Stock Sonar
The Stock Sonar
Published: 2010/09/15
Channel: TheStockSonar
Ranking Relevance in Yahoo Search
Ranking Relevance in Yahoo Search
Published: 2016/10/10
Channel: KDD2016 video
Learn Roslyn Now - E05 - Semantic Model and Symbols
Learn Roslyn Now - E05 - Semantic Model and Symbols
Published: 2015/11/19
Channel: Josh Varty
Introducing Treo: Talk to your Data
Introducing Treo: Talk to your Data
Published: 2013/08/22
Channel: Treo Deri
Enhancing Relevancy through Personalization and Semantic Search, Trey Grainger, CareerBuilder
Enhancing Relevancy through Personalization and Semantic Search, Trey Grainger, CareerBuilder
Published: 2013/12/09
Channel: LuceneSolrRevolution
Exploit Metadata - Kiwi Semantic Web Demo #3
Exploit Metadata - Kiwi Semantic Web Demo #3
Published: 2011/04/05
Channel: Peter Reiser
DUPLICATE DETECTION OF ONLINE DATA USING SCHEMA MATCHING
DUPLICATE DETECTION OF ONLINE DATA USING SCHEMA MATCHING
Published: 2015/06/08
Channel: Vidhya Vidhya
Semantic Web Technology (Understanding the Semantic Web)
Semantic Web Technology (Understanding the Semantic Web)
Published: 2014/05/08
Channel: Olalekan Alonge
Jean-François Noubel: Ceptr: Building a Semantic, Mashable, Fully Decentralized Internet
Jean-François Noubel: Ceptr: Building a Semantic, Mashable, Fully Decentralized Internet
Published: 2015/12/08
Channel: Global Brain Institute
Jiaqi Liu   Fuzzy Search Algorithms How and When to Use Them   PyCon 2017
Jiaqi Liu Fuzzy Search Algorithms How and When to Use Them PyCon 2017
Published: 2017/05/21
Channel: PyCon 2017
Incremental Dense Semantic Scene Reconstruction
Incremental Dense Semantic Scene Reconstruction
Published: 2015/02/15
Channel: Ondra Miksik
AutoMantic - The Innovation of Interoperability
AutoMantic - The Innovation of Interoperability
Published: 2013/12/26
Channel: Jaeni Sahuri
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WIKIPEDIA ARTICLE

From Wikipedia, the free encyclopedia
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Semantic matching is a technique used in computer science to identify information which is semantically related.

Given any two graph-like structures, e.g. classifications, taxonomies database or XML schemas and ontologies, matching is an operator which identifies those nodes in the two structures which semantically correspond to one another. For example, applied to file systems it can identify that a folder labeled “car” is semantically equivalent to another folder “automobile” because they are synonyms in English. This information can be taken from a linguistic resource like WordNet.

In the recent years many of them have been offered.[1] S-Match is an example of a semantic matching operator.[2] It works on lightweight ontologies,[3] namely graph structures where each node is labeled by a natural language sentence, for example in English. These sentences are translated into a formal logical formula (according to an artificial unambiguous language) codifying the meaning of the node taking into account its position in the graph. For example, in case the folder “car” is under another folder “red” we can say that the meaning of the folder “car” is “red car” in this case. This is translated into the logical formula “red AND car”.

The output of S-Match is a set of semantic correspondences called mappings attached with one of the following semantic relations: disjointness (⊥), equivalence (≡), more specific (⊑) and less specific (⊒). In our example the algorithm will return a mapping between ”car” and ”automobile” attached with an equivalence relation. Information semantically matched can also be used as a measure of relevance through a mapping of near-term relationships. Such use of S-Match technology is prevalent in the career space where it is used to gauge depth of skills through relational mapping of information found in applicant resumes.

Semantic matching represents a fundamental technique in many applications in areas such as resource discovery, data integration, data migration, query translation, peer to peer networks, agent communication, schema and ontology merging. Its use is also being investigated in other areas such as event processing.[4] In fact, it has been proposed as a valid solution to the semantic heterogeneity problem, namely managing the diversity in knowledge. Interoperability among people of different cultures and languages, having different viewpoints and using different terminology has always been a huge problem. Especially with the advent of the Web and the consequential information explosion, the problem seems to be emphasized. People face the concrete problem to retrieve, disambiguate and integrate information coming from a wide variety of sources.

Open source software[edit]

See also[edit]

References[edit]

  1. ^ http://dit.unitn.it/~p2p/RelatedWork/Matching/JoDS-IV-2005_SurveyMatching-SE.pdf
  2. ^ http://eprints.biblio.unitn.it/archive/00000531/01/015.pdf
  3. ^ http://eprints.biblio.unitn.it/archive/00000967/01/016.pdf
  4. ^ Hasan, Souleiman, Sean O’Riain, and Edward Curry. 2012. “Approximate Semantic Matching of Heterogeneous Events.” In 6th ACM International Conference on Distributed Event-Based Systems (DEBS 2012), 252–263. Berlin, Germany: ACM. “DOI”.

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