# Knowledge Graphs as the Foundation for Interoperable Intelligent Systems

Fabien Gandon

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## To cite this version:

Fabien Gandon. Knowledge Graphs as the Foundation for Interoperable Intelligent Systems. KGSWC 2024, Dec 2024, Université Paris Cité, France. hal-04838584

HAL Id: hal-04838584

https://inria.hal.science/hal-04838584v1

Submitted on 14 Dec 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# Knowledge Graphs as the Foundation for Interoperable Intelligent Systems

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# WIMMICS TEAM

## Web-Instrumented Man-Machine Interactions, Communities and Semantics

- Inria

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- CNRS

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- University Côte D'Azur (UniCA)

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I3S

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i3S
sophia antipolis

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RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

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Inria Sophia Antipolis Méditerranée (1983) & UCA, I3S, CNRS - Nice, FRANCE

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UNIVERSITÉ
CÔTE D'AZUR

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MULTI-DISCIPLINARY TEAM

- 40~60 members

- ~15 nationalities

- 2 DR, 5 Professors

- 1CR, 1 Assistant professors

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UNIVERSITÉ
CÔTE D'AZUR

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i3S
sophia antipolis

DR / Tenured Senior Researchers / Professors:

- Michel BUFFA, UCA, Web, Social Media, Web Audio/Music, K. Graphs

- Elena CABRIO, UCA, NLP, KR, Linguistics, Q&A, Text Mining, K. Graphs

- Catherine FARON, UCA, KR, AI, Semantic Web, K. Graphs

- Fabien GANDON, Inria, AI, KRR, Semantic Web, Social Web, K. Graphs

- Andrea TETTAMANZI, UCA, AI, Logics, Evo, Learning, Agents, K. Graphs

- Serena VILLATA, CNRS, AI, Argumentation, Licenses, Rights, K. Graphs

- Marco WINCKLER, UCA, Human-Computer Interaction, Web, K. Graphs

CR / ISFP / Tenured Junior Researchers / Assistant Professors:

- Victor DAVID, INRIA, AI, KRR, Argumentation, K. Graphs

- Aline MENIN, UCA, Human-Computer Interaction, Web, K. Graphs

Research engineer:

- Rémi CERES, Inria, Linked Data, Semantic Web, Triple Store, K. Graphs

- Franck MICHEL, CNRS, Linked Data, Integration, DB, K. Graphs

3IA Associate Professors:

- Pierre MONNIN, UCA, AI, Neuro-symbolic, KRR, K. Graphs

- Anais OLLAGNIER, UCA, NLP, Linguistics, Text analysis

Retired / Emeritus:

- Olivier CORBY, Inria, CR, KR, AI, Sem. Web, Programming, K. Graphs

- Alain GIBOIN, Inria, CR, Interaction Design, KE, User & Task, K. Graphs

External Collaborators:

- Andrei Ciortea (University of St. Gallen) Agents, WoT, Sem. Web, K. Graphs

- Nicolas DELAFORGE (Probabl) Sem. Web, KM, Integration, K. Graphs

- Freddy LECUE (JP Morgan, NY) AI, Logics, Mining, Big Data, S. Web, K. Graphs

- Ştefan SARKADI (King's College, London) Agent Simulation, Deceptive AI, K. Sharing

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# knowledge graphs
(Directed, Labels, Multi-Graphs)

« Paris »

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string

+ named entity

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+ linked entity

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+ named relations
# knowledge graphs

typically support

* **associative** data (sets) and object-oriented structures

* **flexible** extensions, changes, integration and enrichment in data and schemas (loosely coupled to schema)

* **complex** queries on relations

* **validation** and **inferential** schemata

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# Knowledge Graphs @ ORACLE®

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OCI

About

Services

Solutions

Pricing

Partners

Resources

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Sign in to Oracle Cloud

## Graph database topics

* Graph database defined

* Graph database types

* How graphs and graph databases work

* **Advantages of graph databases**

* How graph databases and graph analytics work

* Graph database use case: money laundering

* Graph database use case: social media analysis

* Graph database use case: credit card fraud

* The future of graph databases

## Advantages of graph databases

The graph format provides a more flexible platform for finding distant connections or analyzing data based on things like strength or quality of relationship. Graphs let you explore and discover connections and patterns in social networks, IoT, big data, data warehouses, and also complex transaction data for multiple business use cases including fraud detection in banking, discovering connections in social networks, and customer 360. Today, graph databases are increasingly being used as a part of data science as a way to make connections in relationships clearer.

Because graph databases explicitly store the relationships, queries and algorithms utilizing the connectivity between vertices can be run in subseconds rather than hours or days. Users don't need to execute countless joins and the data can more easily be used for analysis and machine learning to discover more about the world around us.

Graph databases are an extremely flexible, extremely powerful tool. Because of the graph format, complex relationships can be determined for deeper insights with much less effort. Graph databases generally run queries in languages such as Property Graph Query Language (PGQL). The example below shows the same query in PGQL and SQL.

### PGQL:

```sql
PATH sharesMovie_with AS (from) <- (acted_in) -> (to)
SELECT y.name
MATCH (x:Actor) -/[:sharesMovie*]/-(y:Actor)
WHERE x.name = 'Iron Man'
AND x <> y
```

### SQL Equivalent:

```sql
WITH temp actor_id, actor_name AS (
  --Anchor member:
  SELECT actor_id, name
  FROM Devices
  WHERE name = 'Iron Man'
  UNION ALL
  --Recursive member:
  SELECT Actors.actor_id, Actors.name
  FROM temp, Actors, Connections conn1,
    Connections conn2, Movies
  WHERE temp.actor_id = conn1.to_actor_id
    AND conn1.from_acted_in_id = Connectors movies_id
    AND Connectors movie_id = conn2.fromMovie_id
    AND conn2.to_actor_id = Devices.actor_id
    AND temp.actor_id != Actors.actor_id)
CYCLE actor_id SET cycle TO 1 DEFAULT 0
SELECT DISTINCT actor_name
FROM temp
WHERE cycle = 0
AND actor_name <> 'Iron Man'
```
KGs are adopted in every domain and are a key to access...

large datasets, private and public, every domain, general and specialized data, ML benchmarks, ...

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What is in Wikidata?

(71,611,020)

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<table><tr><td></td><td>human: 6,376,879 (8.9%)</td></tr><tr><td></td><td>taxon: 2,726,046 (3.8%)</td></tr><tr><td></td><td>administrative division: 1,943,285 (2.7%)</td></tr><tr><td></td><td>architectural structure: 3,159,472 (4.4%)</td></tr><tr><td></td><td>occurrence: 3,898,674 (5.4%)</td></tr><tr><td></td><td>chemical compound: 1,188,724 (1.7%)</td></tr><tr><td></td><td>film: 294,370 (0.4%)</td></tr><tr><td></td><td>thoroughfare: 630,794 (0.9%)</td></tr><tr><td></td><td>astronomical object: 4,601,733 (6.4%)</td></tr><tr><td></td><td>Wikimedia list article: 404,454 (0.6%)</td></tr><tr><td></td><td>Wikimedia disambiguation page: 1,358,230</td></tr><tr><td>(1.9%)</td><td></td></tr><tr><td></td><td>Wikinews article: 195,900 (0.3%)</td></tr><tr><td></td><td>scholarly article: 22,574,314 (31.5%)</td></tr><tr><td></td><td>other P31/P279: 18,284,676 (25.5%)</td></tr><tr><td></td><td>no P31/P279: 3,973,469 (5.5%)</td></tr></table>

Module: Statistical data/by project/classes, 2020-02-16

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City of Los Angeles / Census Data by Council District / data.lacity.org

RDF File

URL: https://data.lacity.org/data/owsل-rturwows.rdfAccessType=DOWNLOAD

From the dataset abstract

Census 2010 population data approximated from block groups to LA Council Districts using East software

Source: Census Data by Council District

Resources

Comma Separated Values File

RDF File

JSON File

XML File

Share on Social Sites

Twitter

Facebook

About this Resource

Last updated: unknown

Created: unknown

Format: RDF File

License: other license specified

Show more

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DATA.GOV

About | Open Government

Website Policies

Sitemap

PERFORMANCE.GOV | Login

KD nuggets

Blog

Topics

Datasets

Education

Resources

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europeana pro

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Linked Open Data FAQ

This page addresses the most important points of Linked Open Data in Europeana.

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DBpedia

Since the emergence of the LOD Cloud in 2007 DBpedia constitutes the main resource of Linked Open Data on the Web containing more than 228 million entities to date. With the rapid growth of published LOD-datasets DBpedia evolved to the most interconnected freely available knowledge graph on the Web. Out of its popularity, numerous further resources emerged and have been created by and for research and industry for various purposes. As of today, a large community of Linked Data enthusiasts is continuously contributing

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GENEONTOLOGY

Unifying Biology

Ontology

<table><thead><tr><td>Property</td><td>Value</td></tr></thead><tbody><tr><td>Valid terms</td><td>42442 (Δ = -327)</td></tr><tr><td> Obsoleted terms</td><td>5287 (Δ = 397)</td></tr><tr><td>Merged terms</td><td>2440 (Δ = 0)</td></tr><tr><td>Biological process terms</td><td>27186</td></tr><tr><td>Molecular function terms</td><td>11198</td></tr><tr><td>Cellular component terms</td><td>4058</td></tr></tbody></table>

Annotations

<table><thead><tr><td>Property</td><td>Value</td></tr></thead><tbody><tr><td>Number of annotations</td><td>7,655,937</td></tr><tr><td>Annotations for biological process</td><td>2,833,885</td></tr><tr><td>Annotations for molecular function</td><td>2,459,729</td></tr><tr><td>Annotations for cellular component</td><td>2,362,323</td></tr><tr><td>Annotations for evidence PHYLO</td><td>3,819,045</td></tr><tr><td>Annotations for evidence IEA</td><td>1,714,824</td></tr></tbody></table>

Gene products and species

<table><thead><tr><td>Property</td><td>Value</td></tr></thead><tbody><tr><td>Annotated gene products</td><td>1,537,348</td></tr><tr><td>Annotated species</td><td>5,387</td></tr><tr><td>Annotated species with over 1,000 annotations</td><td>188</td></tr></tbody></table>

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GeoNames

The GeoNames geographical database covers all countries and contains over eleven million placenames that are available for download free of charge.

OceanGraph RDF dataset

Published: 16 March 2020 | Version 1 | DOI: 10.17632/9t5xkt9wwk.1

Contributors: Marcos Zárate, Carlos Buckle, Renato Mazzanti, Pablo Fillottrani, Claudio Delrieux, Mirtha Lewis

Description

RDF dataset generated by the OceanGraph project (DOI https://doi.org/10.1007/978-3-030-21395-4_3). It contains approximately 4.7M of triplets extracted from different data sets belonging to OBIS, GBIF, SN SciGraph. It also contains information on oceanographic campaigns of the Argentine initiative Pampa Azul extracted from Sistema Nacional de datos del Mar (SNDM).

Total number of entries in this release of UniProtKB

<table><thead><tr><td>Section</td><td>Number of entries in total</td><td>Number of entries with an annotation update</td><td>Number of entries with a sequence update</td></tr></thead><tbody><tr><td>UniProtKB</td><td>251,702,059</td><td>58,607,318</td><td>12,399</td></tr></tbody></table>

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OKG
FIBO

Financial Industry Business Ontology
# Hall of fame
of knowledge graphs

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Knowledge Graphs @

**Billions of real world entities** : people, places, things, businesses, etc.

Used to power Bing **Knowledge Panels as well as traditional search** results

Access through Bing Entity Search **API** to get primary **details and sources** about any entity.

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Serves relevant information **infoboxes** beside search results.

Access Knowledge Graph Search **API** to find entities and details.

The API uses **standard schema.org** types and is compliant with the JSON-LD specification.
Knowledge Graphs (KGs) everyday

Google

daft punk

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All

Images

News

Videos

Books

Web

Finance

Tools

Daft Punk

Electronic duo

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Pinterest

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Overview

Songs

Albums

Members

Videos

Listen

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TSUGI

Daft Punk prépare un cinquième
album, c'est son batteur qui ...

Quinn Smith, le batteur de RAM, a révélé
dans une interview que Daft Punk aurait u...

Feb 1, 2024

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Euronews.com

One more time?
Daft Punk are back...

Sort of -
Euronews.com

Feb 1, 2024

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daftpunk.com

https://www.daftpunk.com

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Daft Punk

Daft Punk (1993 - 2021) - Official website, music, merch, videos and more.

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Wikipedia

https://en.wikipedia.org › wiki › Daft_Punk

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Daft Punk

Daft Punk ; Thomas Bangalter · Guy-Manuel de Homem-Christo · daftpunk.com ...

Discography Discovery (Daft Punk album) Get Lucky (Daft Punk song) Electroma

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Listen

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YouTube

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Spotify

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Deezer

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YouTube
Music

About

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daftpunk.com

Daft Punk were a French electronic music duo formed in 1993 in Paris by Thomas Bangalter and Guy-Manuel de Homem-Christo. They achieved early popularity in the late 1990s as part of the French house movement, combining elements of house music with funk, disco, techno, rock and synth-pop. Wikipedia

Origin: Paris

Genres: French house; dance; electronic; disco

Active from: 1993

Labels: Daft Life; Columbia; Virgin; Soma; Parlophone;
Warner; Walt Disney; ADA France

Members

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Bangalter</figcaption></figure>

Guitar

<figure><img src="image_69.png" /><figcaption>Guy-Manuel
de Homem-...</figcaption></figure>

Drum

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Wikipedia
# Gartner Emerging Tech Impact Radar 2024

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Knowledge graphs, were named a **Critical Enabler** on Gartner's 2024 Emerging Tech Radar.

The Gartner Emerging Tech Impact Radar highlights more than 650 emerging technologies and trends with the most potential to disrupt a broad cross-section of markets.
academic publications about knowledge graphs

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Publications (total)

Source: https://app.dimensions.ai
Exported: August 21, 2024
Criteria: 'knowledge graph' in title and abstract.

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Dimensions

© 2024 Digital Science and Research Solutions Inc. All rights reserved. Non-commercial redistribution / external re-use of this work is permitted subject to appropriate acknowledgement. This work is sourced from Dimensions® at www.dimensions.ai.
# KGs' Research Questions

* Formal models and representation

* Efficient storage, indexing, access

* Validation, verification, quality

* Enrichment, augmentation, reasoning

* Extraction from heterogeneous data

* Security, privacy, access control

* Traceability, provenance,

* Decentralization, distribution

* Analytics, mining, learning

* Interaction design, visualization etc.

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open standards for knowledge graphs

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W3C® OPEN STANDARDS FOR PUBLIC & PRIVATE KGs
RDF is a model for directed labeled multigraphs

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http://ns.inria.fr/fabien.gandon#me

http://inria.fr/schema#author

http://inria.fr/rr/doc.html

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http://inria.fr/rr/doc.html

http://inria.fr/schema#topic

http://inria.fr/schema#keyword

"Music"
RDFS to declare classes of resources and properties, of your knowledge graph and organize their hierarchy

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# OWL in one...

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union

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disjunction

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intersection

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complement

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restriction

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cardinality

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equivalence

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enumeration

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disjoint union

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keys

...
SPARQL
Protocol
And
RDF
Query
Language

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$$ \text{SHACL shape} = \text{targets} + \text{constraints} $$

**Targets**: nodes in the graph that must be validated

**Constraints**: rules on how to validate a node
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# CORESE platform

open source software implementing & extending W3C standards for data on the Web: to create, manipulate, parse, serialize, query, reason and validate RDF data and knowledge graphs

- **Corese-Library**: library to process RDF and use it in your software.

- **Corese-Server**: create, configure and manage endpoints.

- **Corese-GUI**: graphical interface for visual/manual use

- **Corese-Command**: command line version for scripts, etc.

- **Corese-Python**: Python API to CORESE library

**Keywords**: Knowledge Graphs, Artificial Intelligence, Querying, Reasoning, Web Open Standards, Semantic Web, Linked Data, Ontologies

**Standards**: RDF, SPARQL, SHACL, RDFS, OWL, etc.

**Git**: https://github.com/Wimmics/corese/

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# Knowledge Graphs @ :Probabl.

## Open Source for
common good

Creating impact together

Develop, maintain, and sustain open source
projects and communities in data science.

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### our-philosophy

We believe technology serves as a catalyst for societal progress, adopting various models, mostly open source, sometimes closed source, fully managed or on-premises solutions, each playing its role in a complex system.

Our approach and commitment is to provide a fully open source, reversible, and transparent contribution, catering to the needs of the data science and machine learning community.

corese

Software platform implementing and extending the standards of the Semantic Web. It allows to create, manipulate, parse, serialize, query, reason and validate RDF data.

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a Web approach to data publication

« http://fr.dbpedia.org/resource/Paris »

???...

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a Web approach to data publication

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a Web approach to data publication

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a Web approach to data publication

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document and data growth on the Web

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# web servers

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# linked open datasets on the Web
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DBPEDIA.FR @ Inria

(extraction, end-point since 2012)

300 million of extracted
triples; second chapter
in size ; processed

1.8+ billion queries
over the year ;

3.86 million daily average

32.5 million daily max.

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Liberté • Égalité • Fraternité

RÉPUBLIQUE FRANÇAISE

Ministère
Culture
Communication

Inria

Informatics Mathematics

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WIKIMEDIA

FRANCE

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Linking Open Data cloud diagram, 2022. J.P. McCrae, A. Abele, P. Buitelaar, R. Cyganiak, A. Jentzsch, V. Andryushechkin, J. Debattista, J. Nasir.

http://lod-cloud.net/
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<figure><img src="image_123.png" /><figcaption>Photographiez le patrimoine vivant en France et participez au concours organisé du 15 août au 27 septembre !</figcaption></figure>

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# Daft Punk

Sommaire masquer

Article Discussion

Lire

Modifier

Modifier le code

Voir Histoire

Outils

Appearance masquer

Taille du texte

○ Petite

○ Standard

○ Grande

Largement

○ Standard

○ Large

Couleur (binaire)

○ Automatique

○ Clair

○ Sombre

## Début

### Biographie

Débuts (1987-1997)

Années/ Homework
(1997-1999)

Discovery et gain de notoriété
(1999-2003)

Human After All (2004-2007)

Tron : L'Héritage (2008-2010)

Random Access Memories
(2013-2014)

Autres projets et séparation
(2014-2021)

Sorties post-séparation (depuis
2022)

### Concerts

Tournée en 2006-2007

Apparitions sur scène après
2007

Honneur

Membres

Discographie

Filographie

Notes et références

### Vol aussi

Bibliographie

Articles connexes

Liens externes

Bases de données et
dictionnaires

<figure><img src="image_125.png" /></figure>

64 langues v

## Daft Punk

<figure><img src="image_126.png" /><figcaption>Photo promotionnelle des Daft Punk pour l'album
(Randor Access Memories (2013))</figcaption></figure>

### Informations générales

Pays d'origine

France

Genre musical

House, musique électronique, dance, disco

Instruments

Roland TB-303, Roland TR-909, Roland SVC 350, EMS 2000, Roland VR-320, Roland Juno 106, Moog Minimoog Voyager, BOSS Metal Zone MT-2, Roland TR-205

Années actives

1993-2021

Labels

Daft Life Ltd, Virgin, EMI (1996-2013), Columbia, Sony Music (2013-2021), Warner Music Group (2022)

Site officiel

daffunk.com>

### Composition du groupe

Anciens membres

Thomas Bangalter
Guy-Manuel de Homem-Christo

Ils engagèrent leur premier single homologue, *Darlin* sur le label discographique indépendant anglais Duophonic. Ce 45 jours contient deux titres: *Darlin* et *Cindy so Loud*. Le succès n'est pas au rendez-vous, le titre se vend à un millier d'exemplaires, et le groupe riaquem asucme incidence sur la scène française. En mai 1993, une critique du magazine britannique *Melody Maker* qualifia la musique du groupe de « daft punky thrash » ce qui donnera à Thomas et Guy-Manuel l'idée de nommer leur futur duo « Daft Punk », littéralement « punk idiot ».

Site officiel

daffunk.com

<figure><img src="image_127.png" /><figcaption>Logo de Daft Punk.</figcaption></figure>

En 1993, Thomas Bangalter et Guy-Manuel de Homem-Christo fondent le duo Daft Punk<sup>8</sup>. Lors d'une *rare party* sur le toit du Centre Beaubourg, Daft Punk rencontre les responsables du label éossais Soma<sup>9</sup>. En parallèle, Thomas Bangalter et Guy-Manuel de Homem-Christo commencent à travailler sur du matériel électronique tel que l'échantillonneur. Le duo sort un un plus tard, sous le nom Daft Punk, le maximum trois titres. The New Wave. A défaut de succès retentissant, le « techno adolescent français » de Daft Punk retient l'attention de quelques journalistes et critiques anglais. L'année 1995 est calée de leur premier succès, le groupe sort le même électro-rock *Da Funk / Rellin' and Scratchin'*. Le titre rencontre le succès et se répand petit à petit dans les clubs d'Europèen 1995. Thomas Bangalter crée son label *Rouvé* sur lequel il sort le titre *Trax On Da Rocks*. Daft Punk fait alors la première partie des Chemical Brothers à Londres. En France, le groupe participe à la 17<sup>ème</sup> édition des Trans Musicales de Rennes<sup>10</sup>, fune de ses très rares appariements à visages découverts<sup>12</sup>, et est repéré par la maison de disques Virgin<sup>11</sup> : c'est ainsi qu'il fait une apparition sur la compilation Sourcevol 2 du label Source (qui fait partie de Virgin) avec le titre Musique, en 2009. Source est dès lors le premier label à diffuser Daft Punk en France.

### Années Homework (1997-1999) [modifier / modifier le code]

Le 7 janvier 1997, à la suite de la sortie single *Da Funk*, Daft Punk sort son premier album *Homework* caractérisé par la bataille des les successes techno inspirés de la scène de Chicago. Celui-ci rencontre le succès, deux millions d'exemplaires se ventendent dans trente-cinq pays différents en l'espace de deux mois<sup>13</sup>. La presse généraliste et spécialisée fait réfugié de faubourg qui conquiert la jeunesse internationale. Le titre *Around the World* entre dans les charts européens et nord-américains. La réalisation du clip, dans lequel on voit plusieurs groupes de personnages danser au rythme des différents instruments, est signée par le réalisateur Michel Gondry, et la chorégraphie par Blanca Li<sup>14</sup>.

Le groupe acquiert dès lors une notorité internationale, et entame sa première tournée mondiale de quarante dates d'octobre à décembre 1997. Le spectacle y est aléatoire, le concert durant parfois jusqu'à cinq heures. L'album de musique électronique *Homework* émbalène de la French touch démocratisée la « house filtrée », et ses conditions de mixage parfois aléatoires imposées ne permettent pas d'obtenir le son Daft Punk. Le succès de cet album constitue un tremplin qui permet à d'autres groupes de la scène électronique française et mondiale de se révéler. C'est aussi en 1997 que Guy-Manuel de Homem-Christo lance son propre label *Crydamoure*<sup>15</sup>.

### Discovery et gain de notoriété (1999-2003) [modifier / modifier le code]

En 2001, Daft Punk sort son premier album live Intitulé *Alive* 1997 enregistré à Birmingham en Angleterre<sup>16</sup>, contenant une

<figure><img src="image_128.png" /></figure>

<figure><img src="image_129.png" /></figure>

“http://dbpedia.org/resource/Hot_Chip”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Virgin_Records”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/European_Hot_100_singles”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Phaser_(effect)”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Mexico_ingles_Airplay”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Nile_Rodgers”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/37th_Saturn_Awards”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Shake_it_off”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Royals_(Song)”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/1975_in_musique”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Matthew_Williams_(designer)”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Chic_(band)”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/MTV_Video_Music_Award_japan_for_Best_DanceVideo”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Billboard_Music_Award_for_Top_Dance”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Bill_Nye”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Bart_Baker”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Essential_Mix”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Sander_kleinenberg”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Grammy_Award_for_Best_Dance/Electronic_Recording”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Cars_(Song)”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Lacey_Schwimmer”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }

“http://dbpedia.org/resource/Timeline_ofMusical_events”: { "http://dbpedia.org/ontology/wikiPageWikiLink": [ { "type": "uri", "value": "http://dbpedia.org/resource/Daft_Punk" } ] }
# COVID ON THE WEB

[Corby, Michel, Gazzotti, Winckler, et al. 2019]

* integrate **multiple datasets** in heterogeneous formats

* perform **information extraction** to enrich

* perform **inferences and validation** to improve

* provide a **public end-point** for reuse

* provide **querying and visualization** services

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>
<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /></figure>

# WASABI

## Web-augmented music interactions

<figure><img src="image_135.png" /><figcaption>Figure 2: Convolutional Neural Network-based model inferring lyrics segmentation (Fell et al. 2018).</figcaption></figure>
<figure><img src="image_136.png" /></figure>

# ALOOF: RDF dataset about objects

- common sense knowledge about objects: classification, prototypical locations and actions

- knowledge extracted from natural language parsing, crowdsourcing, distributional semantics, keyword linking, ...

<figure><img src="image_137.png" /></figure>

[Cabrio, Basile et al.]
<figure><img src="image_138.png" /></figure>

Linked Open Vocabularies (LOV)

<figure><img src="image_139.png" /></figure>

<figure><img src="image_140.png" /></figure>

<figure><img src="image_141.png" /></figure>

<figure><img src="image_142.png" /></figure>

<figure><img src="image_143.png" /></figure>

864 Vocabularies in LOV

<figure><img src="image_144.png" /></figure>

Category Tags

Methods

Metadata

Industry

IoT

Services

Support

Geography

Catalogs

Society

Environment

API

RDF

People

Quality

General & Upper

Vocabularies

Geometry

Events

Multimedia

Biology

Time

Government

W3C Rec

Academy

Tag

FRBR

eBusiness

Contracts

Travel

Health

Latest insertion

**gtm** - Gouda Time Machine Ontology
2024-12-10

**intro** - INTRO: the intertextual, interpictorial, and
intermedial relations ontology
2024-11-17

**psn** - Product Supply Network Vocabulary
2024-10-02

**vair** - Vocabulary of AI Risks
2024-09-17

**foo** - Forest Observatory Ontology (FOO)
2024-07-18

Latest Updates

**gtm** - Gouda Time Machine Ontology
2024-12-10

**intro** - INTRO: the intertextual, interpictorial, and
intermedial relations ontology
2024-11-18

**IloT** - Ontology for Industrial Internet of Things
systems
2024-10-23

**psn** - Product Supply Network Vocabulary
2024-10-02

**m4i** - Metadata4ing: An ontology for describing
the generation of research data within a scientific
activity
2024-10-02

<figure><img src="image_145.png" /></figure>

Linked Open Vocabularies

DOCUMENTATION

About

API documentation

Source code

Contact

PUBLICATION

Semantic Web Journal '16

ERCIM News '14

Library Hi Tech '13

<figure><img src="image_146.png" /></figure>

Hosted by the Ontology Engineering
Group - UPM

<figure><img src="image_147.png" /></figure>

LOV, LOV, LOV...
all you need is LOV
<figure><img src="image_148.png" /></figure>

## mUnc Vocabulary Specification

### Namespace Document, 01 februaury 2019

**This version:**
http://ns.inria.fr/munc/v2 (owl, tB)

**Latest version:**
http://ns.inria.fr/munc/ (owl, tB)

**Status:**
Work in progress

**Author:**
DJEBRI (Ahmed El Amrane) University Côte d'Azur, CNRS, INRIA, I3S, France

**Advisers:**
Andra G.B. Tettamanzi University Côte d'Azur, CNRS, INRIA, I3S, France
Fabien Gandon INRIA, University Côte d'Azur, CNRS, I3S, France

**IRI:**
http://ns.inria.fr/munc/

mUnc uses OWL's open and freely available Web standards RDF, OWL. It also bases on LDStamp language implemented in Core to present uncertainty calculus.

## Abstract

mUnc (Metadata with UNCertainty) is an OWL ontology for describing Uncertainty information. It aims to offer a standard representation for uncertainty theories and values with respect to the current Semantic Web standards. It also offers the possibility to represent and exchange calculation involved in the evaluation of uncertainty.

## Table of Content

1. An ontology for Uncertainty Representation. mUnc

2. Examples

3. Classes

4. Open Properties

5. Namespace Declarations

## An ontology for uncertainty representation: mUnc

The open nature of the Web allow the existence of fuzzy, vague, or uncertain information for which exists no standard on the Semantic Web. The aim from mUnc is to offer a formal, publishable and reusable representation of uncertainty over the web of linked data, and allow the integration of many theories [1-4] about "uncertainty reasoning". Moreover, this vocabulary offer a way to link between the theories and their calculus.

The current version enables uncertainty representation at different levels of granularity. Uncertainty can be linked to a sentence or to a world (context).

The figure below shows the core classes and properties of mUnc.

<figure><img src="image_149.png" /></figure>

In the version 2 of mUnc, an extension allowing the representation of translatability of uncertainty theories, as shown in the figure below.

<figure><img src="image_150.png" /></figure>

## PWKSO - Possible Worlds and Kripke Structures Ontology

IRI:

http://ns.inria.fr/pwkso/

Formats:

TurtleN3, XML, N.Triple, JSON

## Table of Contents

1. Classes

2. Object properties

3. Namespaces

<figure><img src="image_151.png" /></figure>

# Normative Requirements Vocabulary

IRI:

http://ns.inria.fr/nrv#

Authors:

Fabien Gandon

http://ns.inria.fr/fabien_gandon/foaf.html#me

Contributors:

Fabien Gandon

Guido Governori

Publisher:

Inria

Other visualisation:

Turtle/N3, RDF/XML, Onto-Doc

## Abstract

An OWL vocabulary for describing normative requirements.

## Table of Content

1. Classes

2. Object Properties

3. Named Individuals

4. Namespace Declarations

## Classes

<table><tr><td>achievement</td><td>atomic formula</td><td>co-occurent punctual requirement</td><td>compensable requirement</td><td>compensated requirement</td><td>compensation</td><td>compliance</td><td>compliant requirement</td><td>deontic modality</td><td>deontic specification</td><td>logical formula</td><td>maintenane</td></tr><tr><td>modality</td><td>non co-occurent punctual requirement</td><td>non compensable requirement</td><td>non perdurant requirement</td><td>non persistent requirement</td><td>non preemptive achievement requirement</td><td>prescriptive statement</td><td>prohibition</td><td>non violable requirement</td><td>Normative Requirement</td><td>obligation</td></tr><tr><td>penalty statement</td><td>perdurant achievement requirement</td><td>permission</td><td>persistent requirement</td><td>preemptive achievement requirement</td><td>prescriptive statement</td><td>prohibition</td><td>punctual</td><td>reparation</td><td>right</td><td>suborder list</td><td>term</td><td>violable requirement</td></tr><tr><td>violated requirement</td><td>violation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></table>

achievement<sup>c</sup>

back to ToC or Clas

IRI: http://ns.inria.fr/nrv#Achievement

an obligation for which achieving the content at least once is enough to fulfill it.

has super-classes

obligation<sup>c</sup>

has sub-classes

non perdurant requirement<sup>c</sup>, non preemptive achievement requirement<sup>c</sup>, perdurant achievement requirement<sup>c</sup>, preemptive achievement requirement<sup>c</sup>

atomic formula<sup>c</sup>

back to ToC or Class ToC

IRI: http://docs.oasis-open.org/legalruleml/ns/v1.0/rule-metamodel#AtomicFormula

<figure><img src="image_152.png" /></figure>

Powered by
complex networks: KG designing,
debugging, maintaining, etc.

<figure><img src="image_153.png" /><figcaption>Berenice Abbot, A woman wiring an IBM computer, 1948</figcaption></figure>
# OLIVAW [Nicolas Robert et al.]

* Github repositories for ontologies, schemata, vocabularies...

* command line tool & github workflow for agile collaborative ontology development.

* composite actions that can directly be called in workflows

* pre-commit hook to prevent the biggest mistakes to be pushed

* SHACL & EARL & markdown for reporting

<figure><img src="image_154.png" /></figure>

## Statistic summary

Here is a short overview for this test report

214 Outcomes

3 MajorFail, 46 MinorFail, 0 CannotTell, 30 NotTested, 135 Pass

<figure><img src="image_155.png" /></figure>

The different status types are an extension of the **EARL** vocabulary where the nextended terms can be found in the **olivaw-earl dataset**, each outcome type means:

* MajorFail: This is an error that is critical and consider as blocking for production

* MinorFail: This is an error that should be fixed, but it is cannot be considered as critical error

* CannotTell: It is unclear if the subject passed or failed the test. This happens when an automated test requires human judgement to make a definite decision.

* NotTested: The test has not been carried out. Here this is because a previous test that was mandatory to be passed did not end up as Pass.

* Pass: The subject passed the test.

https://github.com/Wimmics/olivaw

https://github.com/Wimmics/Olivaw-Template

## Outcome Detail

<figure><img src="image_156.png" /></figure>

## Outcome Detail

<figure><img src="image_157.png" /></figure>

<figure><img src="image_158.png" /></figure>
Cataloguing & Indexing data
... and the world

<figure><img src="image_159.png" /></figure>
<figure><img src="image_160.png" /></figure>

# Car configurations as linked data

10²⁵ car configurations but only 10²⁰ coherent (1/100 000)
from [François-Paul Servant *et al.* ESWC 2012]

1 (partial) Configuration = 1 URI

<figure><img src="image_161.png" /></figure>

publish data and computations
# Knowledge Graphs @ VOLVO

<figure><img src="image_162.png" /></figure>

* Vehicles, configurations, customizations, offers, demands, ... are getting more and more complex

* More and more dependencies between, features, functions, actors, ...

## Integrated graph & services improving collaboration

* To **manage connected data**

* To transform **complex data** in a unified graph

* To **analyze** data for useful and actionable insights

* To **unlock** new value from manufacturing data

<figure><img src="image_163.png" /></figure>

## VOLVO CARS GRAPH INTELLIGENCE PLATFORM

<figure><img src="image_164.png" /></figure>

### Knowledge Graph Example

<figure><img src="image_165.png" /></figure>

Sources: "Build a car with Graphs", Fabien Batejat, Graph Technology Specialist, Volvo Cars, Neo4j GraphTour 2019, Customer Presentation; "Graphs in Automotive and Manufacturing: Unlock New Value from Your Data"; "Volvo Cars", Graph Tour 2019; "Volvo Cars: transforming complex data with a unified graph platform"; "Connecting Systems Engineering and Human Factors through MBSE" Nilsson Robert, Bligård Lars-Ola; Neo4J UseCases
<figure><img src="image_166.png" /></figure>

Open KGs for usable knowledge during the entire lifecycle of a product

For business partners and consumers about all aspects of a product

To support multiple sources (manufacturer, transporter, customers, ...) and a large percentage of the recoverable materials at the end of its life "Data economy enabling Circular economy"

<figure><img src="image_167.png" /></figure>

<figure><img src="image_168.png" /></figure>

origintrail

# GS1 Digital Link:

A New Standard Integration with a Decentralized Knowledge Graph

<figure><img src="image_169.png" /></figure>

<figure><img src="image_170.png" /></figure>

<figure><img src="image_171.png" /></figure>

About

Standards

Services

Industries

News & Events

## GS1 Digital Link

The GS1 Digital Link standard extends the power and flexibility of GS1 identifiers by making them part of the web. That means that GS1 identifiers, such as the GTIN, are now a gateway to consumer information that strengthens brand loyalty, improved supply chain traceability information, business partner APIs, patient safety information and more.

Where a URL typically points to a single, specific website, GS1 Digital Link enables connections to all types of business-to-business and business-to-consumer information. If you're adding a QR code or NFC tag to a product, using the GS1 standard means you're not only providing a URL for people to scan, you're also carrying GS1 identifiers - the same identifiers relied upon throughout industry - and following a non-proprietary, no vendor-lock system. This means the brand owner remains in complete control of the number of information sources, all from one symbol, saving space and improving efficiency.

More specifically, in Healthcare, the GS1 Digital Link standard helps reinforce the One-barcode initiative, which is a global effort to promote the use of a single, standardized, bar code that distributors and hospitals will still use the ubiquitous barcode for years to come, for many different purposes. Yet, with GS1 Digital Link, they have the opportunity to use a single barcode that about the product.

### Current Version

GS1 Digital Link Standard 1.2:

URI syntax (Jan 2021)

Resolution (coming soon)

Compression and decompression (coming soon)

Semantics (coming soon)

See previous version 1.1 for anything not covered in the published URI syntax

### Future Versions

With the publication of Release 1.2, GS1 Digital Link is expected to move to a GSMP Standard Maintainance Group which will update and improve the standard at a yet to be determined schedule.

### Find out more

GS1 Digital Link Implementation Guideline (Global Edition) (Feb 2020)

GS1 Digital Link Flyer: Connect your customers to the product info they want and need (2-pager)

GS1 Digital Link Flyer: Connect your customers to the product info they want and need (4-pager)

<figure><img src="image_172.png" /></figure>

GS1®
in Europe

<figure><img src="image_173.png" /></figure>

WORLD WIDE WEB
consortium

## A standards-based knowledge system for the circular economy

### The business case and the challenge for the circular economy

All products are designed, manufactured and sold under market constraints that evolve over time. Today, consumers are increasingly mindful of ethical considerations and the use of recycled materials and a low environmental impact are now selling points.

Providing data to business partners and consumers about these aspects of a product is both essential and challenging. Information systems are needed, for example, to support decision-making in low energy production techniques and efficient transport options, balanced by the resilience of the product and the ease and recoverable percentage of the materials at the end of its life.

It's clear that data is necessary to realize the circular economy. But data is not sufficient. We are drowning in data but starved for knowledge. We need a system that can give us *usable knowledge* during the entire lifecycle of a product.

## A standards-based solution

Two communities are willing to join forces. The business and supply chain community on the one hand, and the data exchange and engineering community on the other. These communities choose to create industry standards at GS1 and W3C respectively. Both organizations welcome the opportunity to share their ideas regarding how to meet the EU Data Strategy and Circular Economy Plan.
did you get your yellow pages?

<figure><img src="image_174.png" /></figure>

<figure><img src="image_175.png" /></figure>

<figure><img src="image_176.png" /></figure>

<figure><img src="image_177.png" /></figure>
to google: transitive verb that means using the Google search engine to obtain information on something or somebody on the World Wide Web.

Nominal Forms

Infinitive: to google

Participle: googled

Gerund: googling

Indicative

Present

I google
you google

he will have googled

we google

you google

they google

Perfect

I have googled
you have googled

he has googled

we have googled

you have googled

they have googled

Past

I googled
you googled

he googled

we googled

you googled

they googled

Pluperfect

I had googled
you had googled

he had googled

we had googled

you had googled

Future

I will google

you will google

he will google

we will google

you will google

they will google

Future perfect

I will have googled
you will have googled

he will have googled

we will have googled

you will have googled

they will have googled

Subjunctive

Present

I google

you google

he google

we google

you google

they google

Perfect

I have googled
you have googled

he have googled

we have googled

you have googled

they have googled

Imperfect

I googled

you googled

he googled

we googled

you googled

they googled

Pluperfect

I had googled
you had googled

he had googled

we had googled

you had googled

they had googled

Conditional

Present

I would google
you would google

he would google

we would google

you would google

they would google

Perfect

I would have googled
you would have googled

he would have googled

we would have googled

you would have googled

they would have googled

Imperative

you google

we Let's google

you google

Progressive (Continuous)
Forms

Indicative

Present

I am googling
you are googling

he is googling

we are googling

you are googling

they are googling

Perfect

I have been googling
you have been googling

he has been googling

we have been googling

you have been googling

they have been googling

Past

I was googling
you were googling

he was googling

we were googling

you were googling

they were googling

Pluperfect

I had been googling
you had been googling

he had been googling

we had been googling

you had been googling

they had been googling

Future

I will be googling
you will be googling

he will be googling

we will be googling

you will be googling

they will be googling

Future perfect

I will have been googling
you will have been googling

he will have been googling

we will have been googling

you will have been googling

they will have been googling

Conditional

Present

I would be googling
you would be googling

he would be googling

we would be googling

you would be googling

they would be googling

Perfect

I would have been googling
you would have been googling

he would have been googling

we would have been googling

you would have been googling

they would have been googling

<figure><img src="image_178.png" /></figure>
# CRAWLING [Huang, Gandon 2019]

<figure><img src="image_179.png" /></figure>

* Predict data availability

* Select features of URIs

* Learn crawling selection (KNN/NaiveBayes/SVM)

* Online learning w. crawling (FTRL-proximal algorithm)

<figure><img src="image_180.png" /></figure>

<figure><img src="image_181.png" /><figcaption>Table 1. Performance of the combinations of feature sets</figcaption></figure>

<table><thead><tr><th rowspan="2">Combination of Feature Sets</th><th colspan="2">KNN</th><th colspan="2">Naive Bayes</th><th colspan="2">SVM</th></tr><tr><th colspan="2">F-measure Accuracy</th><th colspan="2">F-measure Accuracy</th><th colspan="2">F-measure Accuracy</th></tr></thead><tbody><tr><td>F<sub>+t+r+p+x</sub></td><td>0.6951</td><td>0.7407</td><td>0.7154</td><td>0.7462</td><td>0.7944</td><td>0.7722</td></tr><tr><td>F<sub>+t+p+x</sub></td><td>0.6261</td><td>0.6832</td><td>0.7094</td><td>0.7413</td><td>0.7801</td><td>0.7643</td></tr><tr><td>F<sub>+t+r+x</sub></td><td>0.6773</td><td>0.7121</td><td>0.7111</td><td>0.7448</td><td>0.7829</td><td>0.7650</td></tr><tr><td>F<sub>+t+r+p</sub></td><td>0.7592</td><td>0.7731</td><td>0.7660</td><td>0.7701</td><td><strong>0.8216</strong></td><td><strong>0.7902</strong></td></tr><tr><td>F<sub>+r+p+x</sub></td><td>0.6015</td><td>0.7010</td><td>0.6328</td><td>0.7075</td><td>0.6839</td><td>0.7074</td></tr><tr><td>F<sub>+t+x</sub></td><td>0.5582</td><td>0.6912</td><td>0.6012</td><td>0.6172</td><td>0.6828</td><td>0.6277</td></tr><tr><td>F<sub>r+p</sub></td><td>0.3953</td><td>0.5810</td><td>0.4874</td><td>0.6097</td><td>0.6790</td><td>0.6424</td></tr><tr><td>F<sub>+p+x</sub></td><td>0.4392</td><td>0.5739</td><td>0.6086</td><td>0.6238</td><td>0.6689</td><td>0.6269</td></tr></tbody></table>

<table><thead><tr><th>Crawler</th><th>Percentage</th></tr></thead><tbody><tr><td>BFS</td><td>0.302</td></tr><tr><td>crawler_NB (20K)</td><td>0.341</td></tr><tr><td>crawler_NB (40K)</td><td>0.345</td></tr><tr><td>crawler_SVM (20K)</td><td>0.402</td></tr><tr><td>crawler_SVM (40K)</td><td>0.413</td></tr><tr><td>crawler_KNN (20K)</td><td>0.331</td></tr><tr><td>crawler_KNN (40K)</td><td>0.324</td></tr><tr><td>LDCOC (τ = 0.5, ε = 0.17)</td><td><strong>0.655</strong></td></tr></tbody></table>
# IndeGx: knowledge graphs about... knowledge graphs

[Maillot et al. 2022]

declarative framework with extractions rules managed on github to build a public RDF

index of RDF datasets https://github.com/Wimmics/dekalog

1 @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

2 @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

3 @prefix dcterms: <http://purl.org/dc/terms/> .

4 @prefix earl: <http://www.w3.org/ns/earl#> .

5 @prefix kgi: <http://ns.inria.fr/kg/index#> .

6 @prefix void: <http://rdfs.org/ns/void#> .

7 @prefix dcat: <http://www.w3.org/ns/dcat#> .

8 @prefix skos: <http://www.w3.org/2004/02/skos/core#> .

9 @prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

10

<> rdf:type kgi:TestQuery ;

12 dcterms:isReferencedBy <manifest.ttl> ;

13 dcterms:title "Presence of language tags in the content." ;

14 dcterms description "Check if there are language tags to make the content of the KB multilingual." ;

15 kgi:timeout "PT120S"^^xsd:duration ;

16 kgi:query """ASK {

17 SELECT DISTINCT (lang(?o) as ?lang) WHERE {

18 ?s ?p ?o . FILTER( isLiteral(?o) )

19 } LIMIT 1

20 }""" .

Exclusively based of W3C standards

<figure><img src="image_182.png" /></figure>

Used to index more than 300 SPARQL endpoints

Easily extendable with new rule declarations
# KartoGraphI: visualize what can be accessed through the endpoint of IndeGX [Maillot et al. 2022]

http://prod-dekalog.inria.fr/

<figure><img src="image_183.png" /></figure>

<figure><img src="image_184.png" /></figure>

<figure><img src="image_185.png" /></figure>
<figure><img src="image_186.png" /><figcaption>Empty diagonal...</figcaption></figure>

<figure><img src="image_187.png" /></figure>
Metadatamatic

Title
Google

Descripteurs

Endpoints URL

Creator

Publication date

Keywords

Version

License

Standards

Language

<figure><img src="image_188.png" /></figure>

Metadatamatic is a simple RDF application that allows you to create the description of a dataset. It is being provided as a quick and easy way for you to create your own dataset description. Simply search through the forms on this page and complete the fields you like to add in your description. If you enter the URL of an endpoint, you will be able to extract some of the details of the dataset automatically. The final dataset description is displayed at the bottom of the page. It can be downloaded as a file or copied and pasted into an RDF file.

<figure><img src="image_189.png" /></figure>

<figure><img src="image_190.png" /></figure>

<figure><img src="image_191.png" /></figure>

<figure><img src="image_192.png" /></figure>

<figure><img src="image_193.png" /></figure>

<figure><img src="image_194.png" /></figure>

<figure><img src="image_195.png" /></figure>

## Motivation

* < 10% of bases contain provenance metadata

* > 34 vocabularies to describe a dataset

## Supporting dataset metadata generation

* No knowledge of RDF vocabularies necessary

* Usage of existing bases for auto-completion

* Licenses, languages, etc.

* Automatic extraction of features from endpoints

* Nb. of triples, classes, properties, list of graphs, etc.
Extensible e.g. **FAIRness** mesure

Findability

Accessibility

Interoperability

Reusability

<figure><img src="image_196.png" /></figure>

reproduce **FAIR-Checker** [Gaignard et al. 2023]

specialized in bioinformatics datasets
## Evaluation based on the description provided by the dataset to answer questions defined for each step of the life cycle

<figure><img src="image_197.png" /></figure>

Life cycle level

Question level

Question

<figure><img src="image_198.png" /></figure>
# Comparing metrics

Maillot, Andersen et al. 2023

Comparison of FAIR-Checker and Accountability in same conditions over 339 active endpoints

<figure><img src="image_199.png" /></figure>
Contextualizing data,
requests, ...

<figure><img src="image_200.png" /></figure>
# Knowledge Graphs @ eBay

**extra** user-item connectivity

reveals the semantics of entities & user's interest i.e. **contextualize**.

new model to exploit knowledge graphs
for **recommendation** & support **explainable** reasoning

<figure><img src="image_201.png" /></figure>

<figure><img src="image_202.png" /></figure>
# Knowledge Graphs @ amazon

<figure><img src="image_203.png" /></figure>

**commonsense knowledge** graphs to aid product **recommendation**.

Relationships between products and the human **contexts**
(roles, functions, audiences, locations in which they're used, ...)

Large language models (LLMs) to extract the **commonsense** relationships implicit in customer interaction data.

## Building commonsense knowledge graphs to aid product recommendation

Changlong Yu, Zheng Li | May 10, 2024

<figure><img src="image_204.png" /></figure>

Using large language models to discern commonsense relationships can improve performance on downstream tasks by as much as 60%.

Source: Changlong Yu, Xin Liu, Jefferson Maia, Yang Li, Tianyu Cao, Yifan Gao, Yangqiu Song, Rahul Goutam, Haiyang Zhang, Bing Yin, and Zheng Li. 2024. COSMO: A Large-Scale E-commerce Common Sense Knowledge Generation and Serving System at Amazon. SIGMOD/PODS '24, 148–160.

https://doi.org/10.1145/3626246.3653398
# ALOOF: robots learning by reading on the Web

Annie cuts the bread in the kitchen with her knife

<figure><img src="image_205.png" /></figure>

dbp:Knife aloof:Location dbp:Kitchen

<figure><img src="image_206.png" /></figure>

<figure><img src="image_207.png" /></figure>

[Cabrio, Basile et al. 2017]

* Object Location Knowledge Base: 46212 co-mentions, 49 tools, 14 rooms, 101 "possible location" relations, 696 tuples <entity, relation, frame>

* Evaluation: 100 domestic instruments, 20 rooms, 2000 crowdsourcing judgements

* Shared between robots through a shared Web knowledge base
<figure><img src="image_208.png" /></figure>

AliCoCo is a large-scale E-commerce “Cognitive Concept Net”.

**unified data** foundation for user, knowledge, product and content understanding.

Used in **search and recommendation algorithms** of Alibaba (largest Chinese e-commerce site).

Better recognize and **contextualize** the user needs.

<figure><img src="image_209.png" /></figure>

<figure><img src="image_210.png" /></figure>

<figure><img src="image_211.png" /></figure>

<figure><img src="image_212.png" /></figure>

<figure><img src="image_213.png" /></figure>

(c)

**Constructed semi-automatically.**

Sources: Luo, Xusheng et al. “AliCoCo: Alibaba E-commerce Cognitive Concept Net.” SIGMOD 2020 ; E-commerce Knowledge Graph AliCoCo, AlibabaCloud.
RDF-Pattern Based Extraction

<figure><img src="image_214.png" /></figure>

Wikipedia article

+

<figure><img src="image_215.png" /></figure>

SHACL shape

<figure><img src="image_216.png" /></figure>

<figure><img src="image_217.png" /></figure>

Relational Graph

t

S

$E_{Db}$

g

<figure><img src="image_218.png" /></figure>
# RDF-Pattern Based Extraction

<figure><img src="image_219.png" /></figure>

Wikipedia article

+

<figure><img src="image_220.png" /></figure>

SHACL shape

<figure><img src="image_221.png" /></figure>

GRAPH
LINEARIZATION
IMPACT

<figure><img src="image_222.png" /></figure>
Ground-truth construction

<figure><img src="image_223.png" /></figure>

dbo:Person

rdfs:label, dbo:alias, dbo:birthName,

dbo:birthDate, dbo:deathDate, dbo:birthYear,

dbo:deathYear.

<figure><img src="image_224.png" /></figure>

2022-09 snapshot - EN

1 833 493 entities and 3 249 446 related triples
Linearization choice

<figure><img src="image_225.png" /></figure>

- Literature's syntaxes (Lists,Tags)

- W3C's syntaxes (XML, JSON, Ntriples, Turtle)

- Own extension (Turtle Light) even closer to English

<figure><img src="image_226.png" /></figure>
# **Small** Language Models : BART-base & T5-base FT

<figure><img src="image_227.png" /></figure>

* Prompts :

BART: "$entity URI : $Abstract"

T5: "Translate English to $Syntax: [$entity URI] $Abstract"

* Without loss change

* Without decoding strategy

* Cross entropy optimisation with patience of 3 epochs
# Evaluation Models & Linearizations

<figure><img src="image_228.png" /></figure>

## Triple Validity metrics

- Rate of Parsed triples ($R_{PT}$)

- Rate of Correct subject ($R_{CS}$)

- Rate of Shacl validated triples ($R_{SVT}$)

## Classics : (strict mode)

- RE metrics : Precision, Recall, F1

- String datatypes properties : BLEU

- Entire triple evaluation: Levenstein dist

## Ressource

- Time (minutes)

- Carbon consumption

## Training behaviors

- Velocity : when does the model saturate ?

- Stability: Is the learning process stable ?

- Divergence: Is the learning process diverging ?
<figure><img src="image_229.png" /></figure>

A light Turtle goes a long way

<table><thead><tr><th rowspan="2">Config</th><th colspan="3">Triple Validity</th><th colspan="4">RE performances ×100</th><th colspan="2">Edition m.</th><th colspan="4">Training behaviors</th><th colspan="2">Resources</th><th rowspan="2">G<sub>g</sub></th></tr><tr><th>R<sub>PT</sub></th><th>R<sub>CS</sub></th><th>R<sub>SVT</sub></th><th>F<sub>1</sub><sup>-</sup></th><th>F<sub>1</sub><sup>+</sup></th><th>P<sup>+</sup></th><th>R<sup>+</sup></th><th>B</th><th>lev</th><th>Nb<sub>epochs</sub></th><th>V<sub>RPT</sub></th><th>S<sub>RPT</sub></th><th>Σ D<sub>RPT</sub></th><th>V<sub>F<sub>1</sub><sup>-</sup></sub></th><th>S<sub>F<sub>1</sub><sup>-</sup></sub></th><th>Σ D<sub>F<sub>1</sub><sup>-</sup></sub></th><th>C<sub>c</sub></th><th>T<sub>t</sub></th></tr></thead><tbody><tr><td>T5<sub>vj</sub></td><td>1</td><td>1</td><td>1</td><td>99.75</td><td>95.63</td><td>100.00</td><td>94.37</td><td>1.00</td><td>0</td><td>13</td><td>0.2</td><td>Ø</td><td>0</td><td>0.2</td><td>Ø</td><td>2</td><td>0.252</td><td>137</td><td>96</td></tr><tr><td>B<sub>vgf</sub></td><td>1</td><td>1</td><td>1</td><td>99.69</td><td>95.47</td><td>99.29</td><td>94.28</td><td>0.97</td><td>0</td><td>15</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>1</td><td>0.042</td><td>29</td><td>95</td></tr><tr><td>B<sub>vtf1</sub></td><td>1</td><td>1</td><td>1</td><td>99.72</td><td>94.54</td><td>97.09</td><td>93.20</td><td>0.93</td><td>0</td><td>12</td><td>0</td><td>Ø</td><td>0</td><td>0.2</td><td>Ø</td><td>2</td><td>0.035</td><td>27</td><td>95</td></tr><tr><td>B<sub>vT</sub></td><td>1</td><td>1</td><td>1</td><td>99.73</td><td>94.43</td><td>96.39</td><td>93.42</td><td>0.97</td><td>11</td><td>22</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>1</td><td>0.104</td><td>75</td><td>94</td></tr><tr><td>T5<sub>vtf1</sub></td><td>1</td><td>1</td><td>1</td><td>99.51</td><td>93.94</td><td>95.48</td><td>93.13</td><td>0.96</td><td>0</td><td>14</td><td>0.2</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>3</td><td>0.099</td><td>56</td><td>94</td></tr><tr><td>T5<sub>vx</sub></td><td>1</td><td>1</td><td>1</td><td>99.58</td><td>92.86</td><td>96.81</td><td>91.91</td><td>0.95</td><td>2</td><td>18</td><td>0.4</td><td>Ø</td><td>1</td><td>0.4</td><td>Ø</td><td>2</td><td>0.324</td><td>206</td><td>93</td></tr><tr><td>B<sub>vg</sub></td><td>1</td><td>1</td><td>1</td><td>99.62</td><td>92.57</td><td>96.34</td><td>91.08</td><td>0.94</td><td>0</td><td>17</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>0</td><td>0.053</td><td>46</td><td>93</td></tr><tr><td>T5<sub>vl</sub></td><td>1</td><td>1</td><td>1</td><td>99.55</td><td>92.34</td><td>95.19</td><td>91.40</td><td>0.97</td><td>0</td><td>11</td><td>0.8</td><td>Ø</td><td>1</td><td>0.6</td><td>Ø</td><td>1</td><td>0.118</td><td>75</td><td>92</td></tr><tr><td>B<sub>vlf</sub></td><td>1</td><td>1</td><td>1</td><td>99.63</td><td>91.99</td><td>96.68</td><td>90.49</td><td>1.00</td><td>2</td><td>12</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>1</td><td>0.04</td><td>29</td><td>92</td></tr><tr><td>B<sub>vl</sub></td><td>1</td><td>1</td><td>1</td><td>99.62</td><td>92.03</td><td>94.75</td><td>90.37</td><td>0.90</td><td>18</td><td>18</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>1</td><td>0.064</td><td>54</td><td>92</td></tr><tr><td>B<sub>vtf</sub></td><td>1</td><td>1</td><td>1</td><td>99.49</td><td>90.72</td><td>95.45</td><td>89.13</td><td>0.97</td><td>0</td><td>12</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>0</td><td>0.029</td><td>26</td><td>91</td></tr><tr><td>T5<sub>vgf</sub></td><td>1</td><td>0.96</td><td>1</td><td>99.57</td><td>94.18</td><td>96.76</td><td>92.51</td><td>0.98</td><td>1</td><td>13</td><td>0.2</td><td>Ø</td><td>0</td><td>0.2</td><td>Ø</td><td>1</td><td>0.087</td><td>45</td><td>91</td></tr><tr><td>T5<sub>vtf</sub></td><td>1</td><td>1</td><td>1</td><td>99.33</td><td>90.72</td><td>95.81</td><td>88.72</td><td>0.96</td><td>1</td><td>10</td><td>0.8</td><td>Ø</td><td>1</td><td>0.4</td><td>Ø</td><td>1</td><td>0.072</td><td>44</td><td>90</td></tr><tr><td>B<sub>vj</sub></td><td>1</td><td>1</td><td>1</td><td>99.52</td><td>90.27</td><td>95.14</td><td>88.85</td><td>0.96</td><td>47</td><td>11</td><td>0.2</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>0</td><td>0.093</td><td>74</td><td>90</td></tr><tr><td>B<sub>vx</sub></td><td>1</td><td>1</td><td>1</td><td>99.46</td><td>89.87</td><td>96.69</td><td>88.85</td><td>0.97</td><td>17</td><td>14</td><td>0</td><td>Ø</td><td>0</td><td>0.2</td><td>Ø</td><td>1</td><td>0.092</td><td>75</td><td>90</td></tr><tr><td>T5<sub>vt1</sub></td><td>0.97</td><td>1</td><td>1</td><td>99.34</td><td>91.73</td><td>95.32</td><td>89.68</td><td>0.97</td><td>99</td><td>10</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>2</td><td>0.109</td><td>56</td><td>89</td></tr><tr><td>T5<sub>vlf</sub></td><td>1</td><td>0.98</td><td>1</td><td>99.32</td><td>90.32</td><td>94.28</td><td>89.43</td><td>0.88</td><td>205</td><td>11</td><td>1</td><td>Ø</td><td>1</td><td>0</td><td>Ø</td><td>4</td><td>0.081</td><td>49</td><td>88</td></tr><tr><td>B<sub>vn</sub></td><td>1</td><td>1</td><td>1</td><td>99.36</td><td>87.73</td><td>97.01</td><td>85.48</td><td>0.99</td><td>81</td><td>24</td><td>0</td><td>Ø</td><td>0</td><td>0.4</td><td>Ø</td><td>1</td><td>0.134</td><td>119</td><td>88</td></tr><tr><td>T5<sub>vg</sub></td><td>0.98</td><td>1</td><td>1</td><td>99.29</td><td>88.72</td><td>96.15</td><td>86.28</td><td>0.94</td><td>18</td><td>15</td><td>0.4</td><td>Ø</td><td>1</td><td>0</td><td>Ø</td><td>1</td><td>0.107</td><td>76</td><td>87</td></tr><tr><td>T5<sub>vt</sub></td><td>0.97</td><td>1</td><td>1</td><td>99.24</td><td>88.41</td><td>92.60</td><td>86.61</td><td>0.94</td><td>29</td><td>14</td><td>0.2</td><td>Ø</td><td>1</td><td>0.2</td><td>Ø</td><td>3</td><td>0.115</td><td>79</td><td>85</td></tr><tr><td>B<sub>vt1</sub></td><td>0.97</td><td>1</td><td>1</td><td>99.32</td><td>86.15</td><td>94.13</td><td>83.89</td><td>0.99</td><td>20</td><td>16</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>1</td><td>0.047</td><td>41</td><td>83</td></tr><tr><td>B<sub>vt</sub></td><td>0.97</td><td>1</td><td>1</td><td>99.34</td><td>85.68</td><td>93.52</td><td>83.64</td><td>0.98</td><td>13</td><td>14</td><td>0</td><td>Ø</td><td>0</td><td>0</td><td>Ø</td><td>1</td><td>0.053</td><td>43</td><td>83</td></tr><tr><td>T5<sub>vT</sub></td><td>0.82</td><td>1</td><td>1</td><td>99.25</td><td>88.41</td><td>92.60</td><td>86.61</td><td>0.97</td><td>810</td><td>15</td><td>0.8</td><td>0.5</td><td>2</td><td>0.4</td><td>Ø</td><td>3</td><td>0.221</td><td>139</td><td>72</td></tr><tr><td>T5<sub>vn</sub></td><td>0.75</td><td>1</td><td>1</td><td>99.39</td><td>90.61</td><td>97.98</td><td>88.17</td><td>0.97</td><td>137</td><td>13</td><td>1.6</td><td>0.4</td><td>3</td><td>0.2</td><td>0.5</td><td>3</td><td>0.25</td><td>160</td><td>67</td></tr><tr><td>μ</td><td>0.98</td><td>1.00</td><td>1</td><td>99.49</td><td>91.42</td><td>96.02</td><td>89.87</td><td>0.96</td><td>63</td><td>14</td><td>0.3</td><td>Ø</td><td>0.5</td><td>0.1</td><td>Ø</td><td>1.5</td><td>0.11</td><td>73</td><td>89</td></tr><tr><td>σ</td><td>0.1</td><td>0.0</td><td>0</td><td>0.2</td><td>2.7</td><td>1.7</td><td>3</td><td>0.0</td><td>163</td><td>3</td><td>0.4</td><td>Ø</td><td>0.7</td><td>0.2</td><td>Ø</td><td>1</td><td>0.1</td><td>46</td><td>6.8</td></tr></tbody></table>
Linking all forms of intelligence

<figure><img src="image_230.png" /></figure>
Knowledge Graphs @ SAMSUNG

<figure><img src="image_231.png" /></figure>

Oxford Semantic
Technologies

Samsung acquires **Oxford Semantic Technologies** and its AI reasoning engine based on RDF.
(follows investment of Samsung Ventures and collaboration since 2018)

* Enhance the understanding of product or service and services usage.

* Integrate and connect data.

* Enable rapid information retrieval and recommendation.

* Run on **any device** and where the data is.

<figure><img src="image_232.png" /></figure>

"considered one of the **key technologies for realizing more sophisticated and personalized AI solutions**"

Sources: "Samsung Electronics Announces Acquisition of Oxford Semantic Technologies, UK-Based Knowledge Graph Startup", Samsung Press Release;

"Samsung to acquire UK-based knowledge graph startup Oxford Semantic Technologies", Kate Park, July 18, 2024
# Knowledge Graphs @ NOKIA

AI and knowledge graphs to transform telecom inventory solutions.

Automation in network infrastructures and mobile network software supply chain

"billions of dollars worth of potential energy savings"

<figure><img src="image_233.png" /></figure>

Sources: "Learn how AI and graph databases transform telecom inventory solutions", Oleksandr Dmytriiev, Nokia Blog; "A Billion Dollar

Opportunity: Superscaling Knowledge at NOKIA", Chris Brockmann, eccenca, KGC 2023
# Knowledge Graphs @ BOSCH

<figure><img src="image_234.png" /></figure>

> "impossible to see the big picture, the overarching relationships, because the databases were not connected."

**ePLM Navigator:** unlocking and connecting data silos with knowledge Graphs

**Bosch Semantic Stack:** **connect** and **access** backend systems to **leverage data across product life cycle**

**Open Standards** for a Wider Range of Applications.

**Knowledge-driven machine learning:** learning visual models with knowledge graph as a trainer (robust, transferable, faster training)

**Knowledge-infused machine learning:** combining explicit and contextualized domain knowledge (symbolic AI) with data-driven machine learning methods (sub-symbolic AI).

**Neuro-symbolic AI approaches:** domain knowledge graphs to gain further knowledge via reasoning and prediction, and exploit knowledge for search, exploration, machine learning and problem solving.

<figure><img src="image_235.png" /></figure>

Sources: "ePLM Navigator", CONWEAVER, Article ; "A Survey on Visual Transfer Using Knowledge Graphs", Sebastian Monka, Bosch Center for Artificial Intelligence (BCAI) ; Learning visual models using a knowledge graph as a trainer, Bosch Research Blog, Sebastian Monka, Lavdim Halilaj and Stefan Schmid; "Bosch Semantic Stack", Product-centric digital transformation
<table><thead><tr><td>Sexe</td><td>Date</td><td>Cause</td><td>CISP2</td><td>...</td><td>History</td><td>Observations</td></tr></thead><tbody><tr><td>H</td><td>25/04/2012</td><td>vaccin-antitétanique</td><td>A44</td><td>...</td><td>Appendicite</td><td>EN CP - Bon état général - auscult pulm libre; bdc rég sans souffle - tympans ok-</td></tr></tbody></table>

<table><thead><tr><td>Element</td><td>Number</td></tr></thead><tbody><tr><td>Patients</td><td>55 823</td></tr><tr><td>Consultations</td><td>364 684</td></tr><tr><td>Past medical history</td><td>187 290</td></tr><tr><td>Biometric data</td><td>293 908</td></tr><tr><td>Semiotics</td><td>250 669</td></tr><tr><td>Diagnosis</td><td>117 442</td></tr><tr><td>Row of prescribed drugs</td><td>847 422</td></tr><tr><td>Symptoms</td><td>23 488</td></tr><tr><td>Health care procedures</td><td>11 850</td></tr><tr><td>Additional examination</td><td>871 590</td></tr><tr><td>Paramedical prescription</td><td>17 222</td></tr><tr><td>Observations/notes</td><td>56 143</td></tr></tbody></table>

# PREDICT HOSPITALIZATION

[Gazzotti, Faron et al. 2017]

- Physician's records classification to predict hospitalization

- Study enrichment and features impact and combinations on different ML methods
<table><thead><tr><td>Sexe</td><td>Date</td><td>Cause</td><td>CISP2</td><td>...</td><td>History</td><td>Observations</td></tr></thead><tbody><tr><td>H</td><td>25/04/2012</td><td>vaccin-antitétanique</td><td>A44</td><td>...</td><td>Appendicite</td><td>EN CP - Bon état général - auscult pulm libre; bdc rég sans souffle - tympans ok-</td></tr></tbody></table>

<table><thead><tr><td>Element</td><td>Number</td></tr></thead><tbody><tr><td>Patients</td><td>55 823</td></tr><tr><td>Consultations</td><td>364 684</td></tr><tr><td>Past medical history</td><td>187 290</td></tr><tr><td>Biometric data</td><td>293 908</td></tr><tr><td>Semiotics</td><td>250 669</td></tr><tr><td>Diagnosis</td><td>117 442</td></tr><tr><td>Row of prescribed drugs</td><td>847 422</td></tr><tr><td>Symptoms</td><td>23 488</td></tr><tr><td>Health care procedures</td><td>11 850</td></tr><tr><td>Additional examination</td><td>871 590</td></tr><tr><td>Paramedical prescription</td><td>17 222</td></tr><tr><td>Observations/notes</td><td>56 143</td></tr></tbody></table>

# PREDICT HOSPITALIZATION

[Gazzotti, Faron et al. 2017]

* Physician's records classification to predict hospitalization

* Study enrichment and features impact and combinations on different ML methods

<figure><img src="image_236.png" /></figure>

WIKIDATA

(1)

<figure><img src="image_237.png" /></figure>

(2)

<table><thead><tr><th>Représentation</th><th>SGD</th><th>SVC</th><th>RF</th><th>Log</th><th>Moyenne</th></tr></thead><tbody><tr><td>référence</td><td>0.7877</td><td>0.8270</td><td><strong>0.8533</strong></td><td>0.8491</td><td>0.8293</td></tr><tr><td>+t</td><td>0.8054</td><td>0.8239</td><td>0.8522</td><td><strong>0.8545</strong></td><td><strong>0.8340</strong></td></tr><tr><td>+s</td><td>0.7889</td><td>0.8221</td><td>0.8522</td><td>0.8485</td><td>0.8279</td></tr><tr><td>+s*</td><td>0.7985</td><td>0.8339</td><td>0.8449</td><td>0.8514</td><td>0.8322</td></tr><tr><td>+c<sub>1</sub></td><td>0.7859</td><td>0.8235</td><td>0.8433</td><td>0.8453</td><td>0.8245</td></tr><tr><td>+c<sub>1-2</sub></td><td>0.7871</td><td>0.8254</td><td>0.8480</td><td>0.8510</td><td>0.8279</td></tr><tr><td>+c<sub>2</sub></td><td>0.8209</td><td>0.8348</td><td>0.8522</td><td>0.8505</td><td>0.8396</td></tr><tr><td>+d<sub>prevent</sub></td><td>0.7796</td><td>0.8254</td><td>0.8506</td><td>0.8479</td><td>0.8259</td></tr><tr><td>+d<sub>treat</sub></td><td>0.7925</td><td>0.8338</td><td>0.8472</td><td>0.8481</td><td>0.8304</td></tr><tr><td>+d<sub>CI</sub></td><td>0.8108</td><td>0.8281</td><td>0.8498</td><td>0.8460</td><td>0.8337</td></tr><tr><td>+wa</td><td>0.8065</td><td>0.8223</td><td>0.8468</td><td><strong>0.8545</strong></td><td>0.8325</td></tr><tr><td>+wi</td><td>0.8137</td><td>0.8149</td><td>0.8484</td><td>0.8501</td><td>0.8318</td></tr><tr><td>+wm</td><td>0.7991</td><td>0.8221</td><td>0.8453</td><td>0.8458</td><td>0.8281</td></tr><tr><td>+t + s + c<sub>2</sub> + wa + wi</td><td>0.7782</td><td>0.8258</td><td>0.8486</td><td>0.8547</td><td>0.8268</td></tr><tr><td>+t + s* + c<sub>2</sub> + wa + wi</td><td>0.7801</td><td>0.8239</td><td>0.8494</td><td>0.8543</td><td>0.8269</td></tr><tr><td>+t + c<sub>2</sub> + wa + wi</td><td>0.7707</td><td>0.8140</td><td>0.8531</td><td><strong>0.8571</strong></td><td>0.8237</td></tr></tbody></table>

<figure><img src="image_238.png" /></figure>

DBpedia Spotlight

Named Entities
Linking

<figure><img src="image_239.png" /></figure>

DBpedia
<figure><img src="image_240.png" /></figure>

350 000 images
of artworks

RDF metadata based
on external thesauri

<figure><img src="image_241.png" /></figure>

(1)

prefix skos: <http://www.w3.org/2004/02/skos/core#>
prefix jcl: <http://jocondelab.iri-research.org/ns/jocondelab/>

select (replace(group_concat(?topCategory_label; separator="+"), "\\+$", "")) as ?label)
(sample(?noticeRepresentation) as ?repr)
(sample(?imagePath) as ?image_path)
(sample(?noticeReference) as ?ref)

where

{

values (?topCategory_label) { ("chien"@fr) ("cheval"@fr) } .

?topCategory jcl:Term;
skos:prefLabel ?topCategory_label;
skos:narrower* | skos:related/skos:narrower* ?subCategory.

?subCategory skos:prefLabel ?subCategory_label.

?notice jcl:noticeReprTerm ?subCategory;
jcl:noticeHasImage true;
jcl:noticeRef ?noticeReference.

}

group by ?noticeReference

<figure><img src="image_242.png" /></figure>

(2)

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.

@prefix jcl: <http://jocondelab.iri-research.org/ns/jocondelab/>.

@prefix t: <http://data.culture.fr/thesaurus/resource/ark:/67717/>.

@prefix ml: <http://ns.inria.fr/monalia/>.

ml:classifierRepresentedSubject a rdfs:Class; ml:vocabID "REPR".

ml:classifier40_classes rdfs:label "40_classes";
rdfs:subClassOf ml:classifierRepresentedSubject.

<https://jocondelab.iri-research.org/data/notice/00000055013> ml:imageClassifier [a
ml:classifier40_classes ;
ml:detected [ a t:T523-6519; ml:score 0.8102 ],

[ a t:T523-6209; ml:score 0.0219],

[ a t:T523-175; ml:score 0.3843],

...

[ a t:T523-2037; ml:score 0.0121].

(3)

<figure><img src="image_243.png" /></figure>

* reason & query on RDF to build training sets.

* transfer learning & CNN classifiers on targeted categories (topics, techniques, etc.)

* reason & query RDF of results to address **silence**, **noise** and explain

<table><thead><tr><th>Image</th><th>Metadata</th><th>Score ☑</th></tr></thead><tbody><tr><td></td><td>figure (saint Eloi de Noyon, évêque, en pied, bénédiction, vêtement liturgique, mitre, attribut, <strong>cheval</strong>, marteau, outil : ferronnerie)<br>000SC022652<br>C:/Joconde/joconde\0355/m079806_bsa0030101_p.jpg</td><td>cheval:<br>0.006</td></tr></tbody></table>

(4)

<table><thead><tr><th>Image</th><th>Metadata</th><th>Score ☑</th></tr></thead><tbody><tr><td></td><td>portrait<br>50350012455<br>C:\Joconde\joconde\0138\m503501_d0012455-000_p.jpg</td><td>cheval:<br>0.999</td></tr></tbody></table>
# Knowledge Graphs @ Walmart

Retail Graph : products and their related entities (material, activities, rooms, etc.)

* To help customers discover products and catalog.

* Leverage taxonomies and enrich the entities

* To power semantic search, recommendation system

* To answer questions about products and related knowledge

<figure><img src="image_244.png" /></figure>

Source: "Retail Graph — Walmart's Product Knowledge Graph", Karthik Deivasigamani, Walmart Global Tech Blog, Medium
LLMs everywhere... good and bad, like sugar

<figure><img src="image_245.png" /></figure>
# How can biochemists query this massive knowledge graph?

<figure><img src="image_246.png" /></figure>

Swiss National
Science Foundation

<figure><img src="image_247.png" /></figure>

UNIVERSITÉ
DE GENÈVE

<figure><img src="image_248.png" /></figure>

<figure><img src="image_249.png" /></figure>

Louis-Félix Nothias et al.

<figure><img src="image_250.png" /></figure>

GREEN
MISSION

Pierre Fabre

plant collection (17,000+ extracts)

<figure><img src="image_251.png" /></figure>

96 well plates

EXPANDED SET

(2,000+ extracts)

<figure><img src="image_252.png" /></figure>

## DEEP METABOLOMICS

<figure><img src="image_253.png" /></figure>

Exploris

Iterative Optimized
LC-HRMS/MS

Zuo et al, et al. 2021, *Bioinformatics*, p i231-i236
10.1093/bioinformatics/btab279

<figure><img src="image_254.png" /></figure>

## OPEN KNOWLEDGE MANAGEMENT ON NATURAL PRODUCTS

<figure><img src="image_255.png" /></figure>

LOTUS

<figure><img src="image_256.png" /></figure>

WIKIDATA

Rutz A, et al. 2022,

eLife 11:e70780,

10.7554/eLife.70780

<figure><img src="image_257.png" /></figure>

## BIOACTIVITY SCREENING

> Anti-infectious

> Pro-metabolic

<figure><img src="image_258.png" /></figure>

UNIVERSITÉ
DE GENÈVE

Pr. Thierry Soldati

ETH zürich

Pr. Christian Wolfrum

<figure><img src="image_259.png" /></figure>

<figure><img src="image_260.png" /></figure>

Experimental
Natural Products
KnowledgeGraph

Gaudry A, et al. ChemRxiv July 2023, 10.26434/chemrxiv-2023-sljbt

## SYSTEMATIC METABOLITE ANNOTATION

<figure><img src="image_261.png" /></figure>

GNPS

<figure><img src="image_262.png" /></figure>
Louis-Félix Nothias, Emma Tysinger et al.

<figure><img src="image_263.png" /></figure>
Louis-Félix Nothias, Emma Tysinger et al.

<figure><img src="image_264.png" /></figure>

Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- adduct (+/- 5ppm).

<figure><img src="image_265.png" /></figure>

<figure><img src="image_266.png" /></figure>

<figure><img src="image_267.png" /></figure>

Here are the positive ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS that have a corresponding feature in negative ionization mode detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- adduct (+/- 5ppm):

1. Positive feature and Negative feature

2. Positive feature and Negative feature

3. Positive feature and Negative feature

4. Positive feature and Negative feature

5. Positive feature and Negative feature

6. Positive feature and Negative feature

2-3 minutes
Cost: 0.3€

The SPARQL query used to retrieve this information is as follows:

PREFIX enpkg: <https://enpkg.commons-lab.org/kg/>
PREFIX enpkg_module: <https://enpkg.commons-lab.org/module/>
SELECT DISTINCT ?posFeature ?negFeature
WHERE {
?extract a enpkg:LabExtract .
FILTER(?extract = <https://enpkg.commons-lab.org/kg/VGF156_A06>) .
?extract enpkg:has_LCMS ?posLCMS .

<figure><img src="image_268.png" /></figure>
Louis-Félix Nothias et al.

<figure><img src="image_269.png" /></figure>

For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 3 seconds) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D

<figure><img src="image_270.png" /></figure>

<figure><img src="image_271.png" /></figure>

I'm sorry, but the results from the query are too long to display here. You can run the SPARQL query manually to get the results to your question.

Here is the SPARQL query:

<figure><img src="image_272.png" /></figure>

5 minutes

Cost: 0.5€

PREFIX enpkg: <https://enpkg.commons-lab.org/kg/>

PREFIX enpkg_module: <https://enpkg.commons-lab.org/module/>

SELECT DISTINCT ?feature_pos ?rt_pos ?inchikey2d_pos ?featureproneg ?rtproneg ?inchikey2dproneg

WHERE {

?extract a enpkg:LabExtract .

FILTER(?extract = <https://enpkg.commons-lab.org/kg/VGF156_A06>)

?extract enpkg:has_LCMS ?lcms_pos .

?lcms_pos a enpkg:LCMSAnalysisPos .

?lcms_pos enpkg:hasLCMSfeature_list ?fl_pos .

?fl_pos enpkg:hasLCMSfeature ?feature_pos .

?feature_pos enpkg:has_siriusAnnotation ?annotation_pos .

?annotation_pos enpkg:has_InChIKey2D ?inchikey2d_pos .

?feature_pos enpkg:hasRetention_time ?rt_pos .
# KGbot Potential for AI & Life Sciences

Louis-Félix Nothias et al.

Holobiont-wide multi-omics integration using artificial intelligence

## Chatbot

<figure><img src="image_273.png" /></figure>

Can you retrieve the metabolites matching from genome mining and mass spec analysis ?

<figure><img src="image_274.png" /></figure>

"Certainly ! Here is the corresponding graph query"

SELECT ?metabolite

WHERE {

?metabolite rdf:type mb:MicrobialMetabolite ;
mb:annotatedFrom mb:MSData ;
mb:annotatedFrom mg:GenomeMiningData ;
mb:annotatedFrom ms:ExperimentData ;

Metabolomics

Proteomics

(Meta-)
Transcriptomics

Taxonomy

Microbial profiling

Metagenomics

(Host-)Genomics

External databases
AI & HCI for human knowledge augmentation

<figure><img src="image_275.png" /></figure>
# Visual Mining of RDF Graphs [Menin, Winckler, et al. 2022]

## Co-starring network of Robert Redford (source DBPedia)

<figure><img src="image_276.png" /></figure>

```sql
select * where {
?x rdfs:label "Robert Redford"@en.
?p dbo:starring ?x, ?a1, ?a2;
    rdfs:label ?label;
    dbp:released ?date;
    dbp:genre ?type .

?a1 rdfs:label ?s .
?a2 rdfs:label ?o .
filter (?a1 != ?a2)
}
```
# Incremental Data Exploration

Follow-up Queries to dynamically import data into the visualization
during the exploratory process [Menin, Winckler, et al. 2022]

<figure><img src="image_277.png" /></figure>
# Data Quality exploration [Tikat, et al. 2022]

missing data, duplicated data, erroneous data

<figure><img src="image_278.png" /></figure>
# Temporal exploration: WASABI discography visualizations

[Tikat, et al. 2022]

<figure><img src="image_279.png" /></figure>
# Visualization of Association found in Covid dataset

[Menin, Cadorel, Tettamanzi, Winckler]

<figure><img src="image_280.png" /><figcaption>e) Circular Paginated View of Subsets</figcaption></figure>

<figure><img src="image_281.png" /><figcaption>f) Exploratory Graph View of Items</figcaption></figure>

<figure><img src="image_282.png" /></figure>
Conclusion:
the growing potential
of knowledge graphs

<figure><img src="image_283.png" /></figure>
<figure><img src="image_284.png" /></figure>

"Knowledge is power" *Sir Francis Bacon*
You will read this first

Then you will read this...

Then this...
target useful and beneficial...

<figure><img src="image_285.png" /><figcaption>ONTOLOGIES</figcaption></figure>

<figure><img src="image_286.png" /><figcaption>(META)DATA</figcaption></figure>

<figure><img src="image_287.png" /></figure>
benevolent & beneficial AI by design
and not the opposite

<figure><img src="image_288.png" /></figure>
# Pay Attention: a Call to Regulate the Attention Market and Prevent Algorithmic Emotional Governance

Franck Michel<fnref n="1" />, Fabien Gandon<fnref n="2" />

franck.michel@inria.fr, fabien.gandon@inria.fr

## Abstract

Over the last 70 years, we, humans, have created an economic market where attention is being captured and turned into money thanks to advertising. During the last two decades, leveraging research in psychology, sociology, neuroscience and other domains, Web platforms have brought the process of capturing attention to an unprecedented scale. With the initial commonplace goal of making targeted advertising more effective, the generalization of attention-capturing techniques and their use of cognitive biases and emotions have multiple detrimental side effects such as polarizing opinions, spreading false information and threatening public health, economies and democracies. This is clearly a case where the Web is not used for the common good and where, in fact, all its users become a vulnerable population. This paper brings together contributions from a wide range of disciplines to analyze current practices and consequences thereof. Through a set of propositions and principles that could be used to drive further works, it calls for actions against these practices competing to capture our attention on the Web, as it would be unsustainable for a civilization to allow attention to be wasted with impunity on a world-wide scale.

* Question multiple disciplines

* Analyse practices and consequences

* Provide propositions & principles

* Call for actions

* KG have a role to play in tracing, documenting, explaining,... AI

<figure><img src="image_289.png" /></figure>

<footnote n="1">¹University Côte d'Azur, CNRS, Inria</footnote>

<footnote n="2">²University Côte d'Azur, Inria, CNRS</footnote>
<figure><img src="image_290.png" /></figure>

"Knowledge is power" Sir Francis Bacon

<figure><img src="image_291.png" /></figure>

"Code is law" Lawrence Lessig
Terms & Conditions vs...

<figure><img src="image_292.png" /></figure>

11 195 words

< 15 352 words

<figure><img src="image_293.png" /></figure>

< 18 301 words

<figure><img src="image_294.png" /></figure>

< 19 972 words

<figure><img src="image_295.png" /></figure>

iTunes

< 32 241 words

<figure><img src="image_296.png" /></figure>

< 36 275 words

PayPal™

http://conversation.which.co.uk/technology/length-of-website-terms-and-conditions/

...Ontological Commitment

- ontological commitment vs introduction of bias vs informed consent

- an ontological commitment includes an informed consent

- tension : complexity of a commitment vs ergonomic interaction
<figure><img src="image_297.png" /></figure>

50 shades of commitment:
extend, same As, related to, complex alignment, ...

Knowledge-level Coupling is Complex
<figure><img src="image_298.png" /></figure>

"Knowledge is power" *Sir Francis Bacon*

<figure><img src="image_299.png" /></figure>

"Code is law" *Lawrence Lessig*

<figure><img src="image_300.png" /></figure>

"Knowledge encoding is a power law" *me 😊*
# How to perform Sustainable/Frugal/Affordable Knowledge Augmentation ?

* Should Knowledge acquisition be designed to be both for the machine _and_ for us ?

* Can we reconcile user-centric and reusable knowledge engineering?

* How to avoid unknown/low quality or spammed knowledge bases?

* Should we work on knowledge lossy compression, aggregation, summary, forgetting?

...

<figure><img src="image_301.png" /></figure>
<figure><img src="image_302.png" /></figure>

Knowledge without intelligence is a highly perishable resource.

<figure><img src="image_303.png" /></figure>

Intelligence without knowledge is doomed to stagnation.

<figure><img src="image_304.png" /></figure>

Intelligence, knowledge and their evolutions, have to be considered jointly

position
AI for **Artificial Intelligence** (McCarthy et al., 1955)

AI & IA

IA for **Intelligence Amplification** (Ashby, 1956) and
**Intelligence Augmentation** (Engelbart, 1962)

<figure><img src="image_305.png" /><figcaption>4</figcaption></figure>

<figure><img src="image_306.png" /><figcaption>3A</figcaption></figure>

<figure><img src="image_307.png" /><figcaption>3</figcaption></figure>

<caption>3</caption>

<caption>2A</caption>
# A WEB OF KGS IDENTIFYING EVERYTHING

## IRI & RESOURCES

identify on the **networks** anything we want

<figure><img src="image_308.png" /></figure>
# A WEB OF KGS LINKING ALL (META)DATA

<figure><img src="image_309.png" /></figure>

## IRI & RESOURCES

identify on the **networks** anything we want

## RDF & LINKED DATA

decentralized (meta-)data **networks**
# A WEB OF KGS LINKING ALL COMPUTATIONS

<figure><img src="image_310.png" /></figure>

## IRI & RESOURCES

identify on the **networks** anything we want

## RDF & LINKED DATA

decentralized (meta-)data **networks**

## REST & LINKED COMPUTATIONS

decentralized computation **networks**
# A WEB OF KGS LINKING ALL (COMPLEX) NETWORKS

<figure><img src="image_311.png" /></figure>

## IRI & RESOURCES

identify on the **networks** anything we want

## RDF & LINKED DATA

decentralized (meta-)data **networks**

## REST & LINKED COMPUTATIONS

decentralized computation **networks**

## ONTOLOGIES & LINKED SEMANTICS

extensible **network** of models
A WEB OF KGS LINKING ALL KINDS OF INTELLIGENCE

<figure><img src="image_312.png" /></figure>

IRI & RESOURCES

identify on the **networks** anything we want

RDF & LINKED DATA

decentralized (meta-)data **networks**

REST & LINKED COMPUTATIONS

decentralized computation **networks**

ONTOLOGIES & LINKED SEMANTICS

extensible **network** of models

AFFORDANCES & LINKED INTERFACES

extensible **network** of agents
he who controls metadata, controls the web
and through the *world-wide* web many things in our world.

<figure><img src="image_313.png" /></figure>

Fabien Gandon - @fabien_gandon - http://fabien.info

Web-instrumented man-machine interactions, communities and semantics
Technical details: http://bit.ly/wimmics-papers

<figure><img src="image_314.png" /></figure>

<figure><img src="image_315.png" /></figure>

wimmics

<figure><img src="image_316.png" /></figure>

UNIVERSITÉ
CÔTE D'AZUR

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cnrs

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i3S
sophia antipolis