'Controlled vocabularies' presentation slideshows

Subject Analysis LCSH

2.36k views • 209 slides

Metadata as Infrastructure for Information Retrieval and Text Mining

Metadata as Infrastructure for Information Retrieval and Text Mining. Prof. Ray R. Larson University of California, Berkeley School of Information. Overview. Metadata as Infrastructure What, Where, When and Who? What are Entry Vocabulary Indexes? Notion of an EVI How are EVIs Built

641 views • 50 slides

Human Created Information and Big Data

Human Created Information and Big Data. F rom Big Content to Unified Information Access. Alejandro Quiroga, Director Sales aquiroga@attivio.com +1.617.480.6465 Rik Tamm-Daniels, VP Technology, Co-Founder. W hat is Big Data, really?. The Data Universe Big Bang. Business Intelligence.

509 views • 41 slides

Metadata Applications

Metadata Applications. Marcia Lei Zeng NSDL All Project Meeting October, 2003. Outline. Many for one: available metadata standards Different types of metadata and their functions Post-MARC metadata principles Working with your collections Using controlled vocabularies.

524 views • 33 slides

Module 7b: Term Control and Semantic Relationships

Module 7b: Term Control and Semantic Relationships. IMT530: Organization of Information Resources Winter 2008 Michael Crandall. Steps in Constructing CVs. Define your domain Gather concepts From user interviews, search logs, content analysis, preexisting vocabularies Select your approach

440 views • 32 slides

gene structures, products, functions, locations

The Plant Ontology TM Consortium (POC) and an Ontology for Maize/Corn ( Zea mays ) Plant Structure Leszek Vincent , Maize Mapping Project, Univ. of Missouri-Columbia, MO, USA.

420 views • 30 slides

INLS 520

INLS 520. Information Organization. Review. Last week Types of categorization & classification structures Classification Definitions Look at Library classification systems for Dewey & Library of Congress. Today. Controlled vocabularies Types Basic concepts Related technologies

471 views • 29 slides

Using Metadata in CONTENTdm

351 views • 26 slides

328 views • 20 slides

PRIDE Part II: The Ontology Lookup Service Proteome Harvest: Getting Data into PRIDE

PRIDE Part II: The Ontology Lookup Service Proteome Harvest: Getting Data into PRIDE. Phil Jones EMBL-EBI. Introduction: Why did we make OLS?. OLS is the Ontology Lookup Service The original pressure to develop this service was a consequence of the extensive use of ontologies in PRIDE.

320 views • 20 slides

A Handbook of Guidelines on Metadata Usage

325 views • 20 slides

INLS 520

349 views • 19 slides

The Domain-Specific Track at CLEF 2007

The Domain-Specific Track at CLEF 2007. Vivien Petras, Stefan Baerisch & Max Stempfhuber GESIS Social Science Information Centre, Bonn, Germany Budapest, September 19, 2007. Outline. The Domain-Specific Task Collections & Controlled Vocabularies Topics

460 views • 17 slides

Support for MAGE-TAB in caArray 2.0

267 views • 14 slides

Introduction to Controlled Vocabularies (Term Lists)

Introduction to Controlled Vocabularies (Term Lists). Controlled Vocabularies. What: list of terms to choose from Aim: easier to transfer data Where: Data Guidelines and Discovery Metadata Standard Vocabularies are available directly or

249 views • 11 slides

The CUAHSI Observations Data Model

The CUAHSI Observations Data Model. Jeff Horsburgh 7-9-2007. David Maidment, David Tarboton, Ilya Zaslavsky, Michael Piasecki, Jon Goodall, David Valentine, Tom Whitenack, Tim Whiteaker, Bora Beran, Ernest To. http://www.cuahsi.org/his.html. Primary Use Cases.

162 views • 8 slides

Christian Ansorge Arona , 09/04/2014

MIWP-19 Explore and improvement on the situation of controlled vocabularies in the framework of INSPIRE. Christian Ansorge Arona , 09/04/2014. MIWP-19 Controlled Vocabularies. Idea proposed, February 2014 Short discussion on objectives and tasks in Arona , April 2014

180 views • 5 slides

ECAM report #1

ECAM report #1. Generally speaking. The group is consolidating Three calls during the last months Increasing participation (and duration!) Liaisons started Mature: eIRG, Australia Shaping: ENISA, MACE Promising: EUDL, EUNIS More to come. Some areas of discussion.

269 views • 4 slides

HIVE as a Machine-aided Indexing Tool

183 views • 4 slides

The Challenge

Semantic Web Methodology and Technology Development Process. Adopt Technology Approach. Leverage Technology Infrastructure. Science/Expert Review & Iteration. Rapid Prototype. Open World: Evolve, Iterate, Redesign, Redeploy. Use Tools. Analysis. Use Case. Develop model / ontology.

121 views • 1 slides