This document contains information about Daniel Fernández-Álvarez and his PhD thesis work. It provides details such as his contact information, affiliation with WESO, background and education. It describes the key idea of his thesis as extracting structured semantic knowledge through data mining over social media and linking it to existing linked data sources. It presents his work on record linkage algorithms and LOD portals, as well as the goals and methodology of his thesis, including using techniques like ClassRank to analyze relevant classes in RDF graphs.

1. Daniel Fernández-Álvarez
danifdezalvarez@gmail.com

2. Oviedo, Spain

3. WESO (Web Semantics Oviedo)
Head:
José Emilio
Labra-Gayo
Daniel
Gayo-Avello
Aquilino Adolfo
Juan-Fuente
Patricia Ordoñez
de Pablos
Juan Luis
Mateo-Cerdán

4. WESO (Web Semantics Oviedo)
Main interest:
Practical applications of semantic web technologies
Areas:
Linked Open Data, ShEx/SHACL, e-learning, e-commerce, NLP, WoT…
Main funding:
Collaboration with private companies
(looking for international partners to apply for funding)

5. Me (Dani)
Ph.D. student
3rd year of thesis (part time)
Background:
Bachelor of Computer Science, Systems (University of Oviedo)
Master in Web Engineering (University of Oviedo)
More than 3 years in WESO
Title of the thesis:
Extraction of structured semantic knowledge through data mining over social media

6. Beyond the thesis
• Record linkage:
• Algorithm to find equivalent entities in two different sources.
• Music specialized
• MERA: A Musical Entities Reconciliation Architecture Based on Semantic
Technologies , International Journal on Semantic Web and Information Systems
(IJSWIS) Volume 13, Issue 4 – 2017 (JCR, Q3).
• LOD portals:
• Proposal for a system containing LOD data about Spanish law.
• Buscador jurídico y portal de Linked Open Data como herramienta para la
política de ciudad, AECPA XIII – 2017 (national proceeding).
• Inference of Shapes:
• Proposal for automatic inference of shapes (ShEx) from RDF
content.
• Inference and Serialization of Latent Graph Schemata using ShEx, The Tenth
International Conference on Advances in Semantic Processing, SEMAPRO16 –
2016 (international proceeding).

7. Thesis (I)
Key idea
Applications:
Improvement of social search engines
“Boost” for Linked Data
Dataset to explore knowledge dimensions (social, spatial, time…)
Model
knowledge
Natural language
in social media
+ Pattern recognition
New structured
knowledge

8. Thesis (II)
First question:
Given our social sources (Twitter, Reddit) and linked data sources (DBpedia, Wikidata),
which kind of knowledge is swapped between them?
ClassRank
Relevance of each class in an RDF graph
Outputs:
1) Most relevant classes in the graph
2) Amount of instances of each one and used class-pointers

9. ClassRank
Hungary Greece USA
Parlimentary
system
PageRank: 0,1 PageRank: 0,2 PageRank: 0,3
PageRank: …
ClassRank: 0,3
PageRank: …
ClassRank: 0,6
Country
• Pagerank-based. The ClassRank score of a class is:
• The accumulated centrality (PageRank) score
of its instances.
• The chance of reaching one of its instances
while surfing the graph randomly.
• Key concept: classpointers.
• We consider properties beyond rdf:type and
owl:class as linkers between classes and
instances/pseudo-instances.

10. ClassRank on Dbpedia (en)
Rank Class URI Main classpointer ClassRank score Nº of instances
1 foaf:Person rdf:type 0.110918 1179233
2 dbo:CareerStation rdf:type 0.065889 890186
3 owl:Thing rdf:type 0.056176 111626
4 dbo:Person rdf:type 0.050109 611330
5 dbo:Settlement rdf:type 0.042353 238436
6 dbo:SoccerClub rdf:type 0.041327 20877
7 dbo:Country rdf:type 0.031503 3294
8 dbo:SportsTeamMember rdf:type 0.021138 313727
9 dbo:City rdf:type 0.014402 20943
10 dbr:Unitary_state dbo:governmentType 0.013804 145
11 dbo:PersonFunction rdf:type 0.013382 163558
12 dbr:Arthropod dbo:phylum 0.012404 139325
…
Graph used: mappingbased_objects_en.ttl + instance_types_en.ttl + persondata_en.ttl

11. Daniel Fernández-Álvarez
danifdezalvarez@gmail.com