Linked Datapast, present and futures

Pierre-Yves Vandenbussche

ESSnet May 27th 2019

@pyvandenbussche

Introduction – Python notebook

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Outline

• 1. Semantics on the Web

− Vision and construction

• 2. Linked Data: Pragmatic use

• 3. Promising futures

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1. Semantics on the WebVision and construction

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Creation of the Web (1989)

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https://www.w3.org/History/1989/proposal.html

• A “Graph” view of the Web with semantic relations

between documents and entities

Theory towards the Semantic Web (1994)

https://www.w3.org/Talks/WWW94Tim/

https://videos.cern.ch/record/2671957

• “Flat” view of the Web for a computer. No typed relations

• A Web for agents

• Coordination needed:

Construction of the Semantic Web (1999-2012)

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• SPARQL – Querying

• OWL – Formal semantic

• RDF-S – Semantic

• RDF – Representation

2. Linked DataPragmatic use of SW

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Adoption technology ≠ Original thoughts

Shift from Semantic Web to Linked Data

• Ontology / AI implication fails to address most of real world data that is uncertain, incomplete, inconsistent and includes errors

− Relation between concepts can be defined as logical entailments in a formal system (Student(?x) => Person(?x))

• Linked Data

− Emphasis on sharing the information in form of a graph

− Facilitating data integration through common vocabularies with light formal commitment

− Constraints

o ShEx (extension for wikidata May 2019) / SHACL

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2012

2015

Peak of inflated expectations

Trough of disillusionment

Shift from Semantic Web to Linked Data

• Reflected in industry

− Search Engines:

o Google 2012 (Peter Norvig) vs

o Google 2012 (Ramanathan V. Guha)

− Wikipedia (Jimmy Wales) https://www.youtube.com/watch?v=MY4s8uuHmy0

• W3C broaden its Semantic Web activity (https://www.w3.org/2001/sw/) giving rise to the Data Activity / Web of Data (https://www.w3.org/2013/data/)

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Gartner 2012 and 2015

Semantic Web

Linked Data

Linked Data successes /Limitations • Common semantics for a community:

− Schema.org: Web pages metadata / Enhanced search engines

− Museum and art – Getty

− Library – DC/Bibframe

− Statistical data – ESSnet!

• Linked Open Data

− Dbpedia, Wikidata, Eurostat, etc.

• Semantic pipeline

− BBC

− Thomson Reuters

• Limitations

− Cost / incentive

− Use Feedback

− Tools and maintenance

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Jem Rayfield, https://www.slideshare.net/JemRayfield/dsp-bbcjem-

rayfieldsemtech2011

The Rise of Graph data

• Enterprise Knowledge Graphs

− Google Knowledge Vault

− Microsoft Academic KG

− Facebook Graph Search

− …

• Graph mining

• Convergence with Property Graphs

− RDF* (https://www.w3.org/Data/events/data-ws-2019/)

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3. Promising futuresKnowledge Discovery and Sentient Web

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Knowledge Discovery

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• Literature based

discovery - Swanson

1980

• Panama papers (2016)

− Neo4J

− Linkurious

• Discovery of Cancer related

protein interactions

“Artificial Intelligence to win Nobel Prize and Beyond” Hiroaki Kitano - ISWC 2016

Discovery of Cancer related protein interactions

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Prediction of new phosphorylation relationsData

Angiogenesis was expressed in the

majority of cases. In CRC, the

microvascular density (MVD) was

higher than that from ACC. The ratio

CD31/CD105 was 1 in ACC and 3 in

CRC. VEGF was positive in 25% of

ACC and 80% of CRC. In CRC were

more mature vessels, marked only

with CD31 than immature vessels or

endothelial isolated cells marked

with both CD31 and CD105. In ACC

prevailed the neoformed vessels

marked with both CD31 and CD105.

18060184

Unstructured data

LOD

ESR1

Tamoxifen

PIK3CA

SGK1

AKT1 PDPK1

Copanislib

GSK650394

S102

T291 S74 T37

T65 T369

S529

Known Links Fujitsu Prediction

Sentient Web (Graphs, + IoT + AI/ML)

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“Ecosystems of services with awareness of the world through sensors, and reasoning based upon graph data & rules together with graph algorithms and machine learning” Dave Raggett (W3C/ERCIM) / Michael N. Huns (University of South Carolina)

• Combining symbolic information with statistics based upon prior knowledge and past experience

− Large range of reasoning techniques

o Deductive, inductive, abductive, causal, counterfactual, temporal, spatial, etc.

o Together with efficient graph algorithms

− Continuous learning

o Heuristics, simulated annealing, reinforcement learning

Thank you

Pierre-Yves Vandenbussche

@pyvandenbussche

Linked Data example

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Sovereign Statewd:Q3624078

wd:Q219

rdfs:label“Bulgaria”@en

“74.61”

“8031.59”

“7,265,115”

“BG”NUTS code

wdt:P605

“BG”

BULGARIANUTS code

BULGARIA

NUTS Regionramon:NUTSRegion

eus:geo

Participation rates of

4-years-olds in educationR05_1

“2012”

“79.5”

dic/geo#BG