The document addresses the semantic issues and challenges in opening up data from Trentino, highlighting problems such as lack of explicit semantics, semantic heterogeneity, and data duplication. It proposes an entity-centric semantic layer to improve data integration and provides a structured importing pipeline to enhance the quality and accessibility of linked open data. The process includes domain analysis, schema matching, data validation, semantic enrichment, and publishing of semantified entities back into a repository.

1. OGD: Part 5 – Semantic Issues
Juan Pane: jpane@pol.una.py
Lorenzino Vaccari: lorenzino.vaccari@gmail.com
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Juan Pane, Lorenzino Vaccari
http://dati.trentino.it/
08/10/2013

2. Outline
• Overview
• Issues of opening data
• Entity centric Semantic layer
• Importing pipeline
• Importing tool
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3. Available
Structured
Linked Open
Data
Open formats
Redefenceable
Linked
The best data is
an open data
Vs.
All data must be
perfect
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4. Lack of explicit semantics
The real meaning of the data was kept in the developers mind
when creating the data
http://goo.gl/npEHKr (Thanks to Moaz Reyad)
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5. Lack of explicit semantics
Can lead to things like:
http://goo.gl/npEHKr (Thanks to Moaz Reyad)
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6. Semantic heterogeneity
Difference in the meaning of local data
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7. Issues when Opening Trentino Data
 Each department has authority on only some part of the data.
 Dataset originally created for internal use only.
 Dataset created for a specific need.
 Dataset created with custom format:
 For structure (some exceptions)
 For data
 Lack of reuse -> duplication.
 Lack of programmers.
 We cannot TELL them what/how to do (always).
 Data changes
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8. Available
Data Catalog
Structured
Open formats
Redefenceable
Linked
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Entity
Centric
Semantic
Layer
Juan Pane, Lorenzino Vaccari
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9. Entity centric: Added value
 Aggregated data
 Accurate data, manually curated
 Unique identifiers, distributed perspectives
 Re-think identifiers
 Semantified values
E1
E2
name
name
Ignacio P. F.
nationality
italian
born in
Paraguay
lives in
Trento
date of birth
1980
affiliation
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Juan Pane
Univ. Trento
affiliation
PF-UNA
Juan Pane, Lorenzino Vaccari
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10. Entities
 Real world: is something that has a distinct, separate
existence, although it need not be a material (physical)
existence. Has a set of properties, which evolve over time.
Example:
 Mental: personal (local) model created and maintained by a
person that references and describes a real world entity.
 Digital: capture the semantics of real world entities,
provided by people.
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11. Entity Centric Semantic Layer:
• Address the integration problems due to semantic
heterogeneity:
• Different formats
• Different identifiers
• Implicit semantics
• Homonyms, synonyms, aliases
• Partial knowledge
• Knowledge evolution
http://www.webfoundation.org/2011/11/5-staropen-data-initiatives/
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12. Entity-based Integration
• Focus on entities as first class citizens
• Entities are objects which are so important in our everyday life to be referred with a name
• Each entity has its own metadata (e.g. name, latitude, longitude, …)
• Each entity is in relation with many other entities (e.g. Einstein was born in Ulm, his affiliation
was Charles University, Ulm is a city in Germany)
• There are relatively “few” commonsense entity types (person, …, event)
• There are many domain specific entities (bus stops, cycling paths, ..)
• All components have explicit semantics: schema, entities, attributes, values
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13. Importing pipeline, Macro Steps
Domain analysis
1.
Study the needed entity types, adapt the knowledge base
accordingly. First time bootstrapping

Import entities
2.
Semi-automatic tool.




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Domain experts are expensive.
Human attention is a scarce resource.
Incremental enrichment and aggregation of entities.
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14. Open Data Peculiarities
 All data comes from a CKAN repository (DCAT).
 Process one data file at a time.
 Each data file can be represented as a table.
 Each row in the table represents a (partial) entity.
 The format of the values might not be enforced in the data
files.
 Not all data is relevant.
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15. Available
Data Catalog
Structured
Open formats
Redefenceable
Linked
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Entity centric
Importing tool
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16. Importing tool process
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17. 1. Source Selection
Import one data file at a time
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18. 2. Schema Matching
Select a target type of entity -> correspondences between the input columns and
the output attributes
LocalitaTuristica
nome
provincia
descrizione
Andalo (1047)
Provincia di
Trento
Canazei (1450)
Trento Prov.
18
lat
long
Sorge su un'ampia sella prativa 3
al centro...
654463
712857
Situato all'estremità
settentrionale della...
511504
147444
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funivie
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• Nome
• Provincia
• Quota
• Coordinate
• Descrizione
• popolazione
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19. 3. Data Validation
Applies format and structure validation and possible automatic transformations
needed to have the input data in the expected format.
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20. 4. Semantic Enrichment (1/2)
Entity disambiguation: Transform text references into links to existing entities.
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21. 4. Semantic Enrichment (2/2)
Natural Language Processing: Extract concepts and entity references from
free-text.
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22. 5. Reconciliation
Run Identity Management Algorithms to identify each row as a new or existing
entity.
Result
• No Match
• Match
• Multiple
Matches
Action:
• Use ID
• New ID
• Ignore
Row
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23. 6. Exporting
At this point:
 We know what to export.
 All values for target attributes conform to the expected format.
 All text has been semantified (NLP).
 All textual references to entities are converted to links
 Each row has an identifier
v0
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i
i+1
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24. 7. Publishing
Put back the semantified entities into CKAN so that the entities
can be Open Data and can be found in the same catalog as the
original data.
 Developers and find the data files of the cleaned, aggregated
entities
 But can also interact with the entities via the Entitypedia APIs
8. Visualization
Search and Navigation
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25. Semantic Layer: Services
Tool for aiding the “semantification” of the datasets in the catalog
based on:
• Schema matching services
• Identity Management services
• Entity Matching services
• Global Unique Identifier services
• Semantic search and indexing services
• Natural Language Processing
• Entity store
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26. Our Goal
TN
UK
ES
BE
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http://www.shabra.com/wp-content/uploads/2011/03/lets-work-together.jpg

28. Gracias!
Grazie!
Mercy!
Thanks!
Kiitos!
Dank u!
Gràcies!
Gratias!
Danke!
ευχαριστώ
We thank in particular CLEI 2013, Autonomous Province of Trento, TrentoRise association,
Universidad Nacional de Asuncion, and University of Trento
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