Stefan Decker Keynote at CSHALS

Digital Enterprise Research Institute www.deri.ie

From Linked Data to
Networked Knowledge
or
Science is a Social Construct
Stefan Decker

Stefan.Decker@deri.org
© Copyright 2011 Digital Enterprise Research Institute. All rights
reserved.

http://www.StefanDecker.org/
Enabling Networked Knowledge

Cave Drawings 30000 BC


Writing: 3200 BC
(Sumerian cuneiform)


Printing Press
Digital Enterprise Research Institute (Gutenberg 1450) www.deri.ie


Photography
Digital Enterprise Research Institute (Daguerre 1839) www.deri.ie


Phonograph (Edison 1877)


Movies (Lumiere 1895)


Bush’s camera on the
head


Memex

Posited by Vannevar Bush in “As We May Think”
The Atlantic Monthly, July 1945

“A memex is a device
in which an individual stores
all his books, records, and communications,
and which is mechanized so that it may be consulted
with exceeding speed and flexibility”

9

Sketch of memex

10

oNLine System- NLS, 1968
(Doug Engelbart, SRI)

“By ‘augmenting human
intellect’ we mean
increasing the
capability of a man to
approach a complex
problem situation, to
gain comprehension
to suit his particular
needs, and to derive
solutions to
problems.”
The Mouse;
Word Processing;
Data Sharing;
Hypertext;


ARPANET (1969)
(John Postel, David Crocker, Vint Cerf)


Xanadu (Ted Nelson
~1960-???)

13

World Wide Web
(Tim Berners-Lee 1989)

WWW (Tim Berners-
Lee)
“There was a second
part of the dream […]
we could then use
computers to help us
analyse it, make sense
of what we re doing,
where we individually fit
in, and how we can
better work together.”


Making Progress…

Memex (Vannevar Bush)
A memex is “a device in which an individual
stores all his books, records, and
communications.”
Augmenting Human Intellect
(Doug Engelbart)
“By "augmenting human intellect" we mean
increasing the capability of a man to approach a
complex problem situation, to gain
comprehension to suit his particular needs, and to
derive solutions to problems.”
WWW (Tim Berners-Lee)
“There was a second part of the dream […] we
could then use computers to help us analyse it,
make sense of what we re doing, where we
individually fit in, and how we can better work
together.”

15 of 46

A Network of Data
and Knowledge

?

 Interconnected
 Universal
 All encompassing

 assists humans,
organisations and systems
with problem solving
 enabling innovation and
increased productivity


?


What enabled the Web?

1. Scalability: No growth scalability problem
(e.g., no back links from HTML pages)

2. No censorship: no lengthy permission or
review process

3. Positive feedback loop: exploit Metcalf’s
Law



Metcalfe's law:
The value of a
network is
proportional to the
square of the number
of connected members


Metcalfe’s Law 1: Links


Metcalfe’s Law 2


Requirements for a Data Web

1. Scalability. No centralized
infrastructure (e.g., a central object
repository) required.

2. No censorship. It must be possible
to publish data without having to ask
for prior permission.

3. Positive feedback loop. Capitalize on
Metcalfe’s Law.


Enabling Metcalfe’s Law

1. Global Object Identity.
2. Composability: The value of data can be increased if it can be
combined with other data. Composability has a number of
consequences:

1. schema-less. ( Combined data originating from difference sources
unlikely to conform to a schema)

2. self-describing

3. “object centric”. In order to integrate information about different
entities data must be related to these entities.

4. graph-based. The composition of multiple object-centric data
sources results in a graph in the general case.


Observations

• The relational model does not fulfill these requirements (not
composable, no global object id)

• XML is not object centric and not composable.

• Graph based data formats are composable

• RDF fulfills these requirements.

• Claim: Any data format that fulfills the requirements is “more
or less” isomorphic to RDF.


The usual two
Digital Enterprise Research Institute
Ingredients www.deri.ie

1. RDF – Resource Description Framework
Graph based Data – nodes and arcs
 Identifies objects (URIs)
 Interlink information (Relationships)

1. Vocabularies (Ontologies)
 provide shared understanding of a domain
 organise knowledge in a machine-comprehensible way
 give an exploitable meaning to the data

26 of 46

Linked Open Data cloud
- domains
BestBuy
http://lod-cloud.net/ Overstock.com
Facebook
US government
UK government
Media

User-generated

Government Publications
BBC
New York Times

Cross-domain
Geo
Life sciences

LinkedGeoData
Over 200 open data sets with more than 25 billion facts,
interlinked by 400 million typed links, doubling every 10 month!
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.

27

Provocative?

Do we really need
Ontologies?


Issues with Ontologies

 Differentiation in Classes and Instances is difficult:
no single way to abstract the world (observe Upper
Ontology wars…..aehm…discussions!)

 Choices between Instances and Classes done at
design cause usability issues (different treatment in
applications) (animal-mammal-whale)

 Ontologies cement power structures (prevent
information sharing)
 Sharing is only top-down


How did
classes/instances

happen?
 Predecessor: Frame Representation Systems
 “Prototypes: KRL, RLL, and JOSIE employ prototype
frames to represent information about a typical
instance of a class as opposed to the class itself and
as opposed to actual instances of the class.” [Karp,
1993]
 AFAIK: Classes as subsets and instances as
elements [Hayes, 1979].
 Formalization of Frame Systems (Description Logic)
picked up on [Hayes, 1979] and left out alternatives


Note: How did DL &
Ontologies/Classes happen in the
Semantic Web?

 Stefan Decker, Dieter Fensel, Frank van Harmelen,
Ian Horrocks, Sergey Melnik, Michel C. A. Klein,
Jeen Broekstra: Knowledge Representation on the
Web. Description Logics 2000: 89-97

 OIL -> DAML+OIL -> OWL -> OWL 2.0


Class- vs. Prototype-
based Programming

Languages
Class-based: Prototype-based:

 Classes: methods, common > No classes, only objects
properties > Objects define their own
 Inheritance along class properties and methods
chain > Objects delegate to their
 Instances defined by their prototype(s)
class > Any object can be the
 Structure typically cannot be prototype of another object
changed at runtime
Prototype-based languages unify objects and classes

From: A. Lienhard, O. Nierstrasz: Prototype based programming
http://www.slidefinder.net/0/03prototypes/03prototypes/10603817

© A. Lienhard, O. Nierstrasz 3.32

Examples

> JavaScript,
> Self,
> NewtonScript,
> Omega, Cecil,

From: A. Lienhard, O. Nierstrasz: Prototype based programming
http://www.slidefinder.net/0/03prototypes/03prototypes/10603817


How it could look like:
(Horizontal Information

Sharing)


“Instantiation”


Research Agenda

 Knowledge Representation Constructs
(Specialisation)
 Logic based Formalisation of Prototypes
 Reasoning (e.g., with Rules)
 Complexity
 Large Scale Storage, Querying
 Collaboration facilities


Linked Data Vocabularies
as Social Constructs


Neologism

http://vocab.deri.ie/
Neologism is a simple, Drupal-based RDF-S
vocabulary editor and publishing system, that
allows for:
• Collaborativelly creating and maintaining
RDFS vocabularies
• Making the vocab available for humans
(HTML, graph) and machines (RDF/XML,
Turtle)
• Importing external vocabularies
• Working with external namespaces such
as via PURL.org, etc.
• More at http://neologism.deri.ie/


Linked Open Data cloud
- domains
Digital Enterprise Research Institute
BestBuy
www.deri.ie

http://lod-cloud.net/ Overstock.com
Facebook
US government
UK government
Media

User-generated

Government Publications
BBC
New York Times

Cross-domain
Geo
Life sciences

LinkedGeoData
Over 200 open data sets with more than 25 billion facts,
interlinked by 400 million typed links, doubling every 10 month!
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.

39


n
Actio

Visualisation,
Collaboration,
Exploitation
Abstraction,
Reasoning,
Analytics
Networked Data
Management n
o
m ati
Infor


User Role Analysis

 Who are the
influencers?
• Who are the
initiators?
• Who tends to answer
questions?
• What fraction of the
network is non-social?
• How stable are these
roles?
Work by Vaclav Belak, Conor Hayes et al, DERI.

6 Enabling Networked Knowledge

User Role Analysis:
Orthogonal Features

■Persistence
■ Mean/Std. Dev. posts
per thread
C
D ■Initialisation
B
■ % initiated threads
A
■Popularity
■ % in-degree
■ % posts that receive
reply

Post
■Reciprocity
Response ■ % bi-directional
neighbours
■ % bi-directional threads
43

Role Analysis

Reciprocity Persistence Popularity Initiation

Popular Initiator High High Very High
Popular Participant High High Low

Supporter Medium Medium Low

Elitist Low L-M
neighborhood
Hi thread
reponse
Grunt Low to Low to
Medium Medium

Taciturn Very Low Low to
Medium

44

Example

Post
C
D Response
B
A

Reciprocity Persistence Popularity Initiation
Popular initiator A 2/3 1/7 2/7 1
Popular participant B 2/3 2/7 2/7 0
Grunt C 1/3 3/7 0 0
Taciturn D 0 1/7 0 0

45

Analysis of Forums

Boards.ie data from 01/07/2006 to 31/12/2006
 Personal Issues
 Christianity
 Weather
 Windows
 Development
 Humanities
 Politics


Personal Issues Forum

 Mostly taciturns
 Not a lot of dialog


Christianity vs Weather

 Some popular  Popular initiators
initiators  Large portion of grunts
 Some lengthy  Not as much discussion
discussions


Windows, Development, Politics

 Less social (technical)
 No popular initiators
 Lots of grunts


Humanities Forum

 highly social elitists
 Some supporters


The Evolution of
Communities

Work by Vaclav Belak, Conor Hayes et al, DERI.


Motivation

Kuhn claimed the development of
scientific knowledge proceeds in
discrete steps:
1.Pre-paradigm period
2.Paradigm period (normal
science)
 paradigm articulation
1.Crisis
2.Reaction to the crisis
 paradigm shift


Cross-Community Effects

community
shift

community
specialization

 Co-citation networks of Semantic Web community


Methodology Pipeline

Publications from major conferences
selected from DBLP

Community shifts and specializations


Community & Topic
Detection

 Communities identified using:
 Infomap
 Reasons:
 publicly available implementations
 weighted directed networks

 Communities traced from one snapshot to the next
according to the highest Jaccard coefficient
 Ancestors and descendant obtained by a
modification of Jaccard coefficient


Topics of Louvain Community 26

15


A Network of
Knowledge

 Interconnected
 Universal
 All encompassing
•Search •Science
•Collaboration •Commercialization`
•Text Mining

Linked Data

 assists humans,
organisations and systems
with problem solving
 enabling innovation and
increased productivity


Stefan Decker Keynote at CSHALS

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