1 / 16

Semantic Data Visualization for Efficient Analysis

Explore strategies for transforming complex semantic data into visually accessible formats using graph-making utilities and SPARQL queries. Discover the potential of UMBEL and gFacet in enhancing data retrieval and visualization.

nitara
Télécharger la présentation

Semantic Data Visualization for Efficient Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Semantic Data Visualization Eric Bunton - OTD Project Intern 30 July 2009

  2. Agenda • About me • Problem • Solution • What was found • Further Research

  3. About Me • Graduated from Academic Magnet High School • Going to Clemson University in August, B.S. Computer Science • Created a Dictionary Extension for OpenOffice.org Writer

  4. Problem • Lot of semantic data out there • Needs to be put into easily readable form • Make it easier to understand and view • Not all of it is easily retrievable

  5. Solution • Using different graph making utilities to display the data • Construct SPARQL queries to retrieve this data • Create a way to view the data graphically • Find a way to retrieve the data more easily

  6. What was Found • Census data too complicated • Switched to dbpedia.org • Used Google Charts • Has limitations • Found a plugin for JSON input • Had to write a converter to create a valid JSON for the plugin

  7. What was Found cont. • Evaluated Tools • UMBEL • Different ontology make up • Search is more like a Google.com search • Searches by inference not by direct access • gFacet • Currently only works with dbpedia.org • A graphic approach to showing relationships • A way to easily see what is stored on dbpedia

  8. DBPedia Example • prefix db: <http://dbpedia.org/ontology/> • prefix db2: <http://dbpedia.org/ontology/> • prefix owl: <http://www.w3.org/2002/07/owl#> • prefix xschema: <http://www.w3.org/2001/XMLSchema#> • prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> • prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> • prefix prop:<http://dbpedia.org/property/> • select distinct ?name ?population where{ • ?country a db:Country ; • rdfs:label ?name ; • prop:populationCensus ?population . • FILTER (?population > 1500 && ?population <8000 && langMatches(lang(?name), "EN") ) • } limit 5

  9. DBPedia Data Table

  10. DBPedia Bar Graph

  11. gFacet Demo • http://gfacet.org/dbpedia/dbpedia.avi

  12. Further Research • Explore more into UMBEL • Figure out how to link it to other RDF data • Find other graph making utilities

  13. Questions • http://code.google.com/p/otd-semanticvisualization/

  14. UMBEL • Upper Mapping and Binding Exchange Layer • A way of linking data • Can be described as • A road map • A backbone • Middleware • An infocline • A lightweight ontology • Can’t be used to find specific data but links you to a different ontology that can • Search by inferences not specific things

  15. Linked Data • Using web to link related data • URIs are used to link data together • Easier to retrieve the data

  16. References Umbel.org Dbpedia.org SPARQL By Example (http://www.cambridgesemantics.com/2008/09/sparql-by-example/) gFacet.org W3C.com 16

More Related