Accessing data from a relational database by looking at its schema might be difficult, in particular when you are not the one that designed it. Typical questions are: what does these table and column names mean? What is this magic number corresponding to? Where is this kind of information located? In this talk, we will see how a Virtual Knowledge Graph (VKG) can make the content of a database significantly easier to access, without having to alter the database nor move and transform its data.

1. VIRTUAL KNOWLEDGE
GRAPHS
BENJAMIN COGREL
KRDB, Free University of Bozen-Bolzano
SFScon, Bolzano, 16/11/2018
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2. RELATIONAL SCHEMAS
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3. RELATIONAL SCHEMAS
FOR OPERATIONAL DATABASES
Well established, still highly relevant
Fine-grained modeling
Data quality
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4. ACCESSING A
RELATIONAL SCHEMA
(AS AN EXTERNAL ANALYST)
Require to learn too many details!
Locating information (which tables and columns)
Lots of implicits (e.g. magic codes)
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5. ACCESSING MULTIPLE
DATA SOURCES
All have different schemas!
Need to hide this heterogeneity
Data integration
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6. INTEGRATION SCHEMA
YET ANOTHER RELATIONAL SCHEMA?
Mainstream solution
Very painful to design and maintain
Far from providing the exibility needed
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7. KNOWLEDGE GRAPHS
(KG)
Adopted by Google, IBM, Facebook, Microsoft,
EBay, AirBnB, Bloomberg and others
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8. KG: MERGING
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9. KG: MERGING
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10. KG: REASONING
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11. KG: REASONING
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12. VIRTUAL KNOWLEDGE
GRAPHS (VKG)
Keep the data in-place
No need to systematically materialize (ETL)
Reasoning (almost) for free!
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13. VKG SYSTEMS
Commercial
Ultrawrap (Capsenta)
Mastro (OBDA systems)
Stardog
FOSS
Ontop (us)
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15. INTRIGUED?
LET'S GET IN TOUCH
Github:
Twitter: ,
Google Group:
http://ontop.inf.unibz.it
ontop/ontop
@ontop4obda @bcogrel
ontop4obda
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