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).

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. 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. Outline
 PROPheT
 Key features
 Architecture & core components
 Use case
 User evaluation
 Conclusions and future work

4. PROPheT – PERICLES Ontology Population Tool
 GUI-equipped instance extraction and population engine

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. 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. PROPheT – Architecture

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. 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. 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. PROPheT – Use Case Scenario
Scenario: “I want to populate my Cities domain ontology with instances of cities and
towns from ENVO and LinkedGeoData”

12. Step 1: Load my ontology to PROPheT

13. Step 2: Search for Instances

14. Step 3: Select Desired Results

15. Step 4: Select Class to Import Instance

16. Step 5: Perform Property Mapping

17. Step 6: Review Results

18. Step 7: My Populated Model

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. 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. PROPheT - User Evaluation
 15 participants with Computer Science background
 80% of them familiar with ontologies

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)