Joint Keynote at Int. Conference on Knowledge Engineering and Semantic Web and Prague Computer Science Seminar, Prague, September 22, 2016 The challenges of Big Data are frequently explained by dealing with Volume, Velocity, Variety and Veracity. The large variety of data in organizations results from accessing different information systems with heterogeneous schemata or ontologies. In this talk I will present the research efforts that target the management of such broad data. They include: (i) an integrated development environment for programming with broad data, (ii) a query language that allows for typing of query results, (iii) a typed lambda-calculus based on description logics, and (iv) efficient access to data repositories via schema indices.

1. Steffen Staab Programming with Semantic Broad Data 1Institute for Web Science and Technologies · University of Koblenz-Landau, Germany
Web and Internet Science Group · ECS · University of Southampton, UK &
Programming with
Semantic Broad Data
Steffen Staab
@ststaab
west.uni-koblenz.de

2. Steffen Staab Programming with Semantic Broad Data 2
The World of Big Data – Volume & Velocity
Genome data
• Up to 200 GB/person
Video data
• Upload 300 hrs/min
Sensor data
• 5000 sensors/jet
engine
• 1 Tera bit/s
360 TB/disc
https://flic.kr/p/8zuDTm
https://flic.kr/p/59jc2h

3. Steffen Staab Programming with Semantic Broad Data 3
The World of Big Data – Volume & Velocity
Genome data
• Up to 200 GB/person
Video data
• Upload 300 hrs/min
Sensor data
• 5000 sensors/jet
engine
• 1 Tera bit/s
https://flic.kr/p/8zuDTm
https://flic.kr/p/59jc2h
18 concepts
Noise
amplitudes

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The World of Big Data – Variety
Data models
• Graph data
• Relational
• XML
• RDF
• CSV
• JPEG
• MPEG-1, 2, 4
• Dicom
• PDF
• Excel
• ...
Conceptual models
aka ER schemata
aka Logical schemata
aka XML schemata
aka RDFS / OWL ontologies
Foaf, Dublin Core, Marc81,
Unifact,.....
Dozens - Hundreds "¥"

5. Steffen Staab Programming with Semantic Broad Data 5
The World of Big Data – Variety – 15 years ago
SAP
• In the order of 10,000
‘concepts’
• Days to find the right column
Medical information system
(Lars)
• Treating transplant patients
• Approx. 10,000 concepts
Only my
very limited
experiences
Big consulting
business

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The World of Big Data – Variety – Today!
Wikidata
• 1,148,230 concepts
• 2515 relations
UMLS
• 1 Mio concepts
Bioinformatics
• 1000s public databases
• 35 in Bio2rdf
(11 bio triples)
eGov datasets
• 200,000 by Fraunh. Fokus
• 20,000 by ODI
Knowledge Graphs
• Ask Google, Microsoft,
Samsung, HP, ...
Sensor types
• 330 broad types in Wikipedia
• Tens of thousands
How to write
valid, robust
programs?
How to find data?

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How to write a valid, robust program?
SELECT ?x
WHERE
{
?x a CONCEPT15
}
SELECT ?x
WHERE
{
?x a CONCEPT151735
}
https://flic.kr/p/8zuDTm
18 concepts
1,166,040 concepts
1,148,230 concepts
Sept, ´16
March, ´16

8. Steffen Staab Programming with Semantic Broad Data 8
How to approach big data
In fhe following I am guessing
what Axel Polleres might have told you
about Enterprise Linked Data

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Traditional Information Architecture
Business
Logics
Structured Data
Unstructured
Data
Presentation and
Interaction
Characteristics:
• Processes are
known
• Data structures
are known
• Meaning of data
primarily in
schema and code

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Big Data in Today‘s Information Architecture
Characteristics:
• Little structure
• Semi-structured
data
• Meaning of data of
primary
importance!

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Variety Issue 1: Data Models
Data Models:
• Relational
• Tree (XML,...)
• Document oriented
• Stream
• Array
• Graph-DB
RDF
Graph data model as
common denominator

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Dealing with Issue 1: RDF as Data Model
RDF
Graph data model as
common denominator
knows
Bowie
Saran-
don
8-1-1947
bornOn

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Variety Issue 2: Conceptual Models
Conceptual Models:
• ER
• UML
• ...
RDFS
Ontology as common
denominator

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Variety Issue 2: RDFS as common conceptual meta model
RDFS
for explicit conceptual
description
knows
Bowie
Saran-
don
8-1-1947
bornOn
MusicArtist Actor
typetype

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Variety Issue 3: System Boundaries
IRIs
for globally unique
referencing
f:knows
m:Bowie
d:Saran
-don
8-1-1947
m:bornOn
m:Music
Artist
d:Actor
rdf:typerdf:type
m = http://musicbrainz.org
d = http://dbpedia.org
f = http://xmlns.com/foaf/0.1/
rdf = https://www.w3.org/2001/sw/

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A Practical Perspective on
Broad Data with LITEQ

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Drosophila: Linked Open Data Cloud
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
Dozens of domains
Hundreds of data sources
Thousands of concepts
Millions of entities
Billions of triples
Semantic
Broad
Data

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Programming with Linked Data

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c1
Programming with Linked Data
Tasks of the Programmer
1 Schema exploration
2 Programming
code types
3 Programming queries
4 Programming procedures
for
• creating,
• manipulating,
• persisting
objects

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Node Path Query Language Using Autocompletion
Exploration of classes

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Node Path Query Language Using Autocompletion
Exploration of classes
Exploration of relations

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Node Path Query Language: Query Formulation
Exploration of classes
Exploration of relations
Querying for instances
Type
set of mo:MusicArtist
No definition or
declaration needed

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Node Path Query Language for Code Development
Exploration of classes
Exploration of relations
Querying for instances
Developing code with queries
All translated into SPARQL queries at
• Development time
• Type inference at compile time
(but also as part of IDE)
• Querying again at run time
One language to bind them all

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Node Path Query Language for Code Development
Exploration of classes
Exploration of relations
Querying for instances
Developing code with queries
Developing code with new classes
All translated into SPARQL queries at
• Development time
• Run time update
• Persistence!

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Formal NPQL Syntax
Data browsing
Restricting Class Expressions
Evaluating Class Expressions
Navigating from Data to Classes
Navigating from Data to Property Types
URI set
Intensional
Queries
Extensional
Queries
Navigational
Queries

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NPQL Algebra (Example)
Reversibility
can be used to simplify path expressions.

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Summary on LITEQ
Language Integrated Types, Extensions, and Queries
NPQL (Node Path Query Language)
• Navigational Queries
• Intensional Queries
• Extensional Queries
• Compilation to SPARQL
LITEQ
• Implementation of NPQL as F# Type Provider in Visual Studio
• Autocompletion using NPQL queries
• Automatic typing
of extensional query results
by intensional queries

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„That seems to work very well in practice,
but how does it work in theory?“
17 let allArtists =
Store.NPQL().``mo:MusicArtist``.Extension
What is implied by such a line...
...for the programme?
...for the compiler?
seems to

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A Foundational Perspective on
Semantic Broad Data Using DL

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What we want to have: Static Type Checking
But:
• In LITEQ: Queries must receive types
• Number of types in our system very/infinitely large
• Existing type systems expect complete knowledge
Programming with Data from a Knowledge Base
Issue in our prototype

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Related Work
Generic Types
• Everything is a node
or an edge
• No type checking!
 Only 2nd place in
Halo competition
Mapping approaches
• Hibernate
• LITEQ
• ActiveRDF
• Summer / Winter
• ...
Preferred in SemWeb now Been there, done that

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Example – and Issues with Mapping
Mapping DL types to PL types problematic because
1. Mix of nominal (MusicArtist) and structural typing (recorded.Song)
2. Schema-less information (influencedBy)
3. Inference (hendrix:MusicArtist)
4. Sheer size of terminology
How to type a
query?

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Example
Code
To be rejected
is not subtype of
How to type a
query?

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Example
Code
To be accepted
is a
How to type a
query?

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What we want to have: Static Type Checking
Challenge:
• A programming language that accepts
concept expressions as types and
can deal with inferences
Programming with Data from a Knowledge Base
DL

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Given 
• Atomic Types: A={...Ai...}
• Plus Function types: T={...Ai..., ...TiTj...}
Add elements
• Concept expressions ( Intensional NPQL queries )
• Instances ( Extensional NPQL queries)
Add knowledge
• Typing and subtyping derived from knowledge base
Core Ideas of DL

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Concept Forming
Expressions
Syntax Semantics
Top T I
Bottom  I
Concept Name A AI
Intersection A  B AI  BI
Negation A I AI
Existential Restriction R.C { a I | (a,b) RI and b  CI}
Axioms Syntax Semantics
T-Box Subclass C  D AI  BI
A-Box Concept assertion a:C aI CI
A-Box Role assertion (a,b) : R (aI,bI)  RI
Description Logics Fragment

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Universal model of computation
• Abstraction
• Application
Example:
• f.x.f (f x)
Evaluation rules
 Calculus

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Syntax for core DL

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Core DL: Evaluation and Typing
Nominal DL-Type

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Subtyping
 many types
Add KB knowledge
only when needed for
checking application,
not proactively

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• Queries return sets
• Concept set type needed
• Set operators needed
• Map, Fold, Element
• Queries may return infinite sets
• No theoretical problem,
but lack of well-defined stopping conditions in KBs
• Type dispatch based on inferencing
Further issues and opportunities in DL

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DL Interpreter in F# and using HermiT

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Theorem: A well-typed closed term does not get stuck
during evaluation (with common exceptions).
Result for DL
Typing is a safety net,
but does not solve the halting problem
(empty list)

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Conclusion

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Broad data
• has grown from 104 to 106 concepts (plus data)
• continues to grow
– more integration of distributed databases
– more sensors of different types
– More crowdwork
• has not been recognized as a problem of its own, yet
• will lead to
– brittleness
– high maintenance efforts
– loss of opportunities
Present of Broad Data

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New Methods for Broad data
• Explore
– Understand
• Find
• Relate (see e.g. Linda‘s talk today)
• Program
• Maintain
Future of Broad Data

48. Steffen Staab Programming with Semantic Broad Data 49Institute for Web Science and Technologies · University of Koblenz-Landau, Germany
Web and Internet Science Group · ECS · University of Southampton, UK &
Thank you for your attention!
Thanks to my collaborators for this work:
Stefan Schegelmann, Martin Leinberger, Matthias Thimm (WeST, Koblenz)
Evelyne Viegas (Microsoft Research, Redmond)
Ralf Lämmel (SOFTLANG, Koblenz)