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A Domain-Agnostic Tool for Scalable Ontology Population and Enrichment from Diverse Linked Data Sources

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PROPheT (PERICLES Ontology Population Tool) was presented in DAMDID 2017, Moscow, by Panagiotis Mitzias.
Find more information about PROPheT and download the tool here:
http://mklab.iti.gr/project/prophet-ontology-populator

Publication:
Kontopoulos, E., Mitzias, P., Riga, M., Kompatsiaris, I. A Domain-Agnostic Tool for Scalable Ontology Population and Enrichment from Diverse Linked Data Sources. In: Kalinichenko, L.A. et al. (eds.) Selected Papers of the XIX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2017). pp. 184–190 CEUR Workshop Proceedings Vol 2022, Moscow, Russia (2017).

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A Domain-Agnostic Tool for Scalable Ontology Population and Enrichment from Diverse Linked Data Sources

  1. 1. A Domain-Agnostic Tool for Scalable Ontology Population and Enrichment from Diverse Linked Data Sources Efstratios Kontopoulos, Panagiotis Mitzias, Marina Riga, Ioannis Kompatsiaris
  2. 2. Aim of this Work  Issues:  Increasing interest in building ontologies  Various sources of Linked Open Data (LOD)  Manual ontology population is time-consuming and error-prone  Aims:  Facilitate re-use of knowledge  Automate instance extraction and population  Support most LOD sources
  3. 3. Outline  PROPheT  Key features  Architecture & core components  Use case  User evaluation  Conclusions and future work
  4. 4. PROPheT – PERICLES Ontology Population Tool  GUI-equipped instance extraction and population engine
  5. 5. About PERICLES  PERICLES: Promoting and Enhancing Reuse of Information throughout the Content Lifecycle taking account of Evolving Semantics  Funding: FP7, ICT Call 9, objective ICT-2011.4.3 Digital Preservation, €10M budget  Duration: 2013-2017  Website: http://pericles-project.eu/
  6. 6. PROPheT – Key Features  Three modes of instance population:  class-based populating  instance-based populating  instance enrichment  User-driven mapping of classes and data properties  Exporting populated ontology in popular formats (.owl, .rdf, .ttl, .nt and .n3.)  Flexibility to work with any domain ontology and any SPARQL endpoint
  7. 7. PROPheT – Architecture
  8. 8. PROPheT – Core Components  My Model (MM) – e.g. my_ontology.owl  External Model (EM) – e.g. DBpedia  Extraction Module (search mechanisms)  Mapping Module  Storage Module (database)  Export Module
  9. 9. PROPheT – Search Mechanisms  Search by class e.g. find instances of class dbo:Artist  Search by instance label e.g. find instances with label Picasso  Search by existing instance e.g. find instances similar to Picasso instance  Enrich existing instance e.g. find data properties for Picasso instance
  10. 10. PROPheT – Mapping Module  Define My Model class to import new instances  Map External Model properties to My Model properties  Store new mappings to database for future use
  11. 11. PROPheT – Use Case Scenario Scenario: “I want to populate my Cities domain ontology with instances of cities and towns from ENVO and LinkedGeoData”
  12. 12. Step 1: Load my ontology to PROPheT
  13. 13. Step 2: Search for Instances
  14. 14. Step 3: Select Desired Results
  15. 15. Step 4: Select Class to Import Instance
  16. 16. Step 5: Perform Property Mapping
  17. 17. Step 6: Review Results
  18. 18. Step 7: My Populated Model
  19. 19. Use Case Population Times Class Instances Population time (sec) LinkedGeoData City 10.000 120 ENVO City 10.000 204 LinkedGeoData Town 10.000 158 Factors:  Endpoint response speed  Number of datatype properties/values per instance
  20. 20. Instance Retrieval and Population Times Ontology Instances Retrieval time (sec) Population time (sec) DBPedia 10.000 648 250 OpenData Communities 10.000 510 210 DBLP 10.000 316 192 Nobel Prize 10.000 270 170 Eurostat 10.000 440 225
  21. 21. PROPheT - User Evaluation  15 participants with Computer Science background  80% of them familiar with ontologies
  22. 22. Conclusions & Future Work  Conclusions:  It is user-friendly  It makes ontology population simple  No SPARQL experience is required  Future work:  Optimize overall speed  Handle object properties  Make it a Protégé plugin  Add more search options (e.g. by class label)

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