1. LINKED OPEN METADATA AND ONTOLOGIES
• My name is Riccardo Grosso (
riccardo.maria.grosso@gmail.com)
• I am an hobbyst metadata architect 
• Actually I work at the datacenter of the CSI-Piemonte
• For many years I worked in metadata architecture, first in
other companies, then in CSI-Piemonte
• This passion has filled a lot of my free time
• The first thanks goes to the CSI if I continue to cultivate
this passion with new ideas in my free time
• The first ex equo thanks to Carlo Batini & Matteo
Palmonari of Milan-Bicocca University, Roberto Moriondo
of Piedmont Region

2. • My name is Riccardo Grosso. In last century I
started working in data and metadata conceptual
modeling. My passion is to discover relationships
between data, and processes to which they relate,
in similar formalisms to natural language. Subject,
verb, object complement. Citizen pays tribute.
Recently I’ve realized a metadata catalog for a
government to classify informatic objects and
assets. Particularly, with regard to the metadata of
the databases, I have experimented semantics
inferences "for similarity" in the descriptive
metadata (names and descriptions of tables and
fields) to extract and exploit the knowledge
embedded in the metadata, using conceptual
schema frameworks also called light ontologies . 2

3. 3
• There is no 'time to talk about the project and
the experiments about operational and
decisional databases, but I think that this work
can be reused, to enhance the knowledge in the
linked open data world, especially public
administration that we have conceptual
schemas . Linked open data are in fact tables,
with columns, with constraints from columns

4. LOM
Insert name and descr.
of tables & columns.
Infer physical constraints
Tag metadata by likeness
of entity-names inside in
names and descr. of
tables & columns
Integrated &
abstracted
E-R schema
repository
LOD
«Universe»
(data & metadata)
Linked Open Data
(RDF/OWL)
Infer vertical
Abstraction level
Entity-ierarchy
taxonomy
Reuse
Relationships
LOMO
…
From LOD (Linked Open Data) to LOMO (Linked Open Metadata & Ontologies)

5. 5
• - We can populate the metadata universe of linked
open data in public administration
- building the data structures that contains the
linked open data available
- insert names and descriptions of tables and fields
of linked open data, and their constraints formed
by primary and foreign keys
- Use entities 'conceptual frameworks available for
similarity to tag where these entities' are present
in the names and descriptions of tables and fields
(it can be better with the use of text mining, or
computational linguistics )
- Reassemble the hierarchy of the entities' tagged
- To infer a level of abstraction of the concepts
measured vertical
- so we obtain linked open metadata level

6. • But we are not so happy, and we want to relate the
entities' extracted, with the relationships present
in the light ontologies, and reuse all the repository
of integrated and abstracted conceptual schemes .
The abstraction helps to simplify and better
understand the tagged knowledge.
- We then relate the entities extracted
- Now we are happy: we have linked open metadata
and ontologies, and vertical relations of
abstractions, given by hierarchies, and horizontal
correlations from entities
- The hierarchy of entities' are the steps of ascent
in the abstraction of the conceptual schemas,
represent the level of abstraction
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7. • PROPOSAL:
– So we could think: if we create a collaborative project
to make everything?
– Administrations should only engages with IT resources
that manage their services , providing logical and
physical schemas of linked open data published ( for
insiders : most important is the sql – bill with comments
of tables and fields , pardon the jargon )
– Such schemes would be instanced by administrations ,
and cataloged. Most important is to catalog where is
each linked open data db implemented in datacenter
(physical instance)
– The catalogs get made public.
– The metadata catalogs would be enhanced with the use
of ontologies for semantic inference , and conceptual
schemes instantiated on each data base structure ,
with each administration, always in a public way
– And what about the cost ? I personally put my
experience, my methods and my vintage tools of
inference 

8. • WHY ?
– Best reuse of knowledge
– Helps in master data management
– Ontological query, 2 levels:
• Ontologies and linked open metadata driving:
– Physical data query
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