Stefan Decker Keynote at CSHALS
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Transcript of Stefan Decker Keynote at CSHALS
Copyright 2011 Digital Enterprise Research Institute. All rights reserved.
Digital Enterprise Research Institute www.deri.ie
Enabling Networked Knowledge
From Linked Data to Networked Knowledge
orScience is a Social Construct
Stefan Decker
[email protected]://www.StefanDecker.org/
Digital Enterprise Research Institute www.deri.ie
Enabling Networked Knowledge
Cave Drawings 30000 BC
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Writing: 3200 BC (Sumerian cuneiform)
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Printing Press(Gutenberg 1450)
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Photography (Daguerre 1839)
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Phonograph (Edison 1877)
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Movies (Lumiere 1895)
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Bush’s camera on the head
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Enabling Networked Knowledge9
Memex
“A memex is a devicein which an individual stores all his books, records, and communications, and which is mechanized so that it may be consultedwith exceeding speed and flexibility”
Posited by Vannevar Bush in “As We May Think” The Atlantic Monthly, July 1945
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Sketch of memex
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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;
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ARPANET (1969) (John Postel, David Crocker, Vint Cerf)
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Xanadu (Ted Nelson ~1960-???)
13
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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.”
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Enabling Networked Knowledge15 of 46
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.”
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Enabling Networked Knowledge
A Network of Data and Knowledge
Interconnected Universal All encompassing
assists humans, organisations and systems with problem solving
enabling innovation and increased productivity?
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Enabling Networked Knowledge
?
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Enabling Networked Knowledge
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
What enabled the Web?
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Metcalfe's law: The value of a network is proportional to the square of the number of connected members
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Metcalfe’s Law 1: Links
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Metcalfe’s Law 2
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Enabling Networked Knowledge
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.
Requirements for a Data Web
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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.
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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.
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The usual two Ingredients
1. RDF – Resource Description FrameworkGraph based Data – nodes and arcs Identifies objects (URIs) Interlink information (Relationships)
2. Vocabularies (Ontologies) provide shared understanding of a domain organise knowledge in a machine-comprehensible way give an exploitable meaning to the data
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Linked Open Data cloud - domains
Over 200 open data sets with more than 25 billion facts,interlinked by 400 million typed links, doubling every 10 month!
http://lod-cloud.net/
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.
Media
Government
Geo
Publications
User-generated
Life sciences
Cross-domain
US governmentUK government
BBCNew York Times
LinkedGeoData
27
BestBuyOverstock.comFacebook
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Enabling Networked Knowledge
Do we really need Ontologies?
Provocative?
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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
Issues with Ontologies
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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
How did classes/instances happen?
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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
Note: How did DL & Ontologies/Classes happen in the Semantic Web?
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Enabling Networked Knowledge© A. Lienhard, O. Nierstrasz 3.32
Class- vs. Prototype-based Programming Languages
Classes: methods, common properties
Inheritance along class chain Instances defined by their
class Structure typically cannot be
changed at runtime
Class-based: Prototype-based:
> No classes, only objects> Objects define their own
properties and methods> Objects delegate to their
prototype(s)> Any object can be the
prototype of another object
Prototype-based languages unify objects and classes
From: A. Lienhard, O. Nierstrasz: Prototype based programming http://www.slidefinder.net/0/03prototypes/03prototypes/10603817
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> JavaScript, > Self,> NewtonScript,> Omega, Cecil,
Examples
From: A. Lienhard, O. Nierstrasz: Prototype based programming http://www.slidefinder.net/0/03prototypes/03prototypes/10603817
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How it could look like:(Horizontal Information Sharing)
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“Instantiation”
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Knowledge Representation Constructs (Specialisation)
Logic based Formalisation of Prototypes Reasoning (e.g., with Rules) Complexity Large Scale Storage, Querying Collaboration facilities
Research Agenda
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Linked Data Vocabulariesas Social Constructs
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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/
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Enabling Networked Knowledge
Linked Open Data cloud - domains
Over 200 open data sets with more than 25 billion facts,interlinked by 400 million typed links, doubling every 10 month!
http://lod-cloud.net/
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.
Media
Government
Geo
Publications
User-generated
Life sciences
Cross-domain
US governmentUK government
BBCNew York Times
LinkedGeoData
39
BestBuyOverstock.comFacebook
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Enabling Networked Knowledge
Networked Data Management
Abstraction,Reasoning,Analytics
Visualisation,Collaboration,Exploitation
Information
Action
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Enabling Networked Knowledge6
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User Role Analysis
6
• Who are the initiators?
• Who tends to answer questions?
• What fraction of the network is non-social?
Who are the influencers?
• How stable are these roles?
Work by Vaclav Belak, Conor Hayes et al, DERI.
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Enabling Networked Knowledge
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Enabling Networked Knowledge43
User Role Analysis: Orthogonal Features
■Persistence■Mean/Std. Dev. posts per
thread
■Initialisation■% initiated threads
■Popularity■% in-degree
■% posts that receive reply
■Reciprocity■% bi-directional
neighbours
■% bi-directional threads
AB
CD
Post
Response
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Enabling Networked Knowledge44
Role Analysis
Reciprocity Persistence
Popularity
Initiation
Popular Initiator High High Very High
Popular Participant
High High Low
Supporter Medium Medium Low
Elitist Low neighborhoodHi thread reponse
L-M
Grunt Low to Medium
Low to Medium
Taciturn Very Low Low to Medium
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Enabling Networked Knowledge45
Example
Reciprocity
Persistence
Popularity Initiation
A 2/3 1/7 2/7 1
B 2/3 2/7 2/7 0
C 1/3 3/7 0 0
D 0 1/7 0 0
AB
CD
Popular initiator
Popular participant
Grunt
Taciturn
Post
Response
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Enabling Networked Knowledge
Analysis of Forums
Boards.ie data from 01/07/2006 to 31/12/2006 Personal Issues Christianity Weather Windows Development Humanities Politics
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Personal Issues Forum
Mostly taciturns Not a lot of dialog
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Christianity vs Weather
Some popular initiators
Some lengthy discussions
Popular initiators Large portion of grunts Not as much discussion
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Enabling Networked Knowledge
Windows, Development, Politics
Less social (technical) No popular initiators Lots of grunts
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Humanities Forum
highly social elitists Some supporters
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The Evolution of Communities
Work by Vaclav Belak, Conor Hayes et al, DERI.
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Motivation
Kuhn claimed the development of scientific knowledge proceeds in discrete steps:1.Pre-paradigm period2.Paradigm period (normal science)
paradigm articulation
3.Crisis4.Reaction to the crisis
paradigm shift
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Cross-Community Effects
Co-citation networks of Semantic Web community
community shift
community specialization
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Methodology Pipeline
Community shifts and specializations
Publications from major conferences selected from DBLP
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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
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Topics of Louvain Community 26
15
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Enabling Networked Knowledge
A Network of Knowledge
enabling innovation and increased productivity
Interconnected Universal All encompassing
assists humans, organisations and systems with problem solving
Linked Data
•Search•Collaboration•Text Mining
•Science•Commercialization`