'Semantic metadata' diaporamas de présentation

Capturing and Applying Existing Knowledge to Semantic Applications

628 views • 52 slides

Capturing and Applying Existing Knowledge to Semantic Applications

655 views • 52 slides

Vision and Infrastructure Behind the Cancer Biomedical Informatics Grid

634 views • 50 slides

Evaluating Semantic Metadata without the Presence of a Gold Standard

Evaluating Semantic Metadata without the Presence of a Gold Standard. Yuangui Lei, Andriy Nikolov, Victoria Uren, Enrico Motta Knowledge Media Institute, The Open University {y.lei,a.nikolov,v.s.uren,e.motta}@open.ac.uk. Focuses.

428 views • 32 slides

Scalable Ontological Sense Matching

Scalable Ontological Sense Matching. Dr. Geoffrey P Malafsky TECHi2 LLC, Fairfax, VA. Need for Smarter Systems. Enormous and ever increasingly amounts of data and information are available

384 views • 29 slides

Schema-Driven Relationship Extraction from Unstructured Text

Schema-Driven Relationship Extraction from Unstructured Text. Cartic Ramakrishnan Kno.e.sis Center, Wright State University, Dayton, OH. Motivation Problem Description & Approach Results Future Work. Outline. Anecdotal Example. UNDISCOVERED PUBLIC KNOWLEDGE.

364 views • 28 slides

Schema-Driven Relationship Extraction from Unstructured Text

Schema-Driven Relationship Extraction from Unstructured Text. Cartic Ramakrishnan, Krys Kochut and Amit Sheth LSDIS Lab, University of Georgia, Athens, GA November 7 th 2006 ISWC 2006. Outline. Motivation Problem Description & Approach Results Future Work. Anecdotal Example.

372 views • 28 slides

Schema-Driven Relationship Extraction from Unstructured Text

Schema-Driven Relationship Extraction from Unstructured Text. Cartic Ramakrishnan Kno.e.sis Center, Wright State University, Dayton, OH. Motivation Problem Description & Approach Results Future Work. Outline. Anecdotal Example. UNDISCOVERED PUBLIC KNOWLEDGE.

342 views • 28 slides

Schema-Driven Relationship Extraction from Unstructured Text

Schema-Driven Relationship Extraction from Unstructured Text. Cartic Ramakrishnan Kno.e.sis Center, Wright State University, Dayton, OH. Motivation Problem Description & Approach Results Future Work. Outline. Anecdotal Example. UNDISCOVERED PUBLIC KNOWLEDGE.

286 views • 28 slides

Amit Sheth CTO/SrVP, Voquette (voquette) [formerly Founder/CEO, Taalee, taalee]

Amit Sheth CTO/SrVP, Voquette (www.voquette.com) [formerly Founder/CEO, Taalee, www.taalee.com] Director, Large Scale Distributed Information Systems Lab, University Of Georgia (lsdis.cs.uga.edu) amit@sheth.org.

364 views • 25 slides

Structured Data

Structured Data. HTML XML XHTML JSON XMLSchema. Structured Data. Machine processable data needs to be structured There are many examples Properties files: h ost= example.com p ort=8080 p rotocol=https Comma Separated Values: host,port,protocol example.com,8080,https

368 views • 23 slides

Improving Human-Semantic Web Interaction: The Rhizomer Experience

317 views • 21 slides

Gunnar AAstrand Grimnes Alun Preece & Pete Edwards University of Aberdeen ggrimnes@csd.abdn.ac.uk

11/4/2002. Personal Agents - The impact of the Semantic Web First presented at AgentLink II - Amsterdam, December 2001. Gunnar AAstrand Grimnes Alun Preece & Pete Edwards University of Aberdeen ggrimnes@csd.abdn.ac.uk. Challenges: Representation of training events. Data volume.

243 views • 17 slides

Semantic Web Applications GoodRelations BBC Artists BBC World Cup 2010 Website Emma Nherera

Semantic Web Applications GoodRelations BBC Artists BBC World Cup 2010 Website Emma Nherera . GoodRelations is the most powerful vocabulary for publishing all of the details of your products and services in a way friendly to search engines, mobile applications, and browser extensions.

344 views • 16 slides

308 views • 14 slides

Final Study Report on Semantic Metadata Mapping Procedure

284 views • 11 slides