This course is a quick overview of the fundamentals of graph databases and graph queries, with a focus on RDF and SPARQL. It includes both simple and challenging hands-on exercises to practice and test your understanding.
The material for this course can be downloaded form the following link: https://github.com/paolo7/Introduction-to-Graph-Databases
Web open standards for linked data and knowledge graphs as enablers of EU dig...Fabien Gandon
Web open standards for linked data and knowledge graphs as enablers of EU digital sovereignty
ENDORSE Keynote by Fabien GANDON, 19/03/2021
https://op.europa.eu/en/web/endorse
This course is a quick overview of the fundamentals of graph databases and graph queries, with a focus on RDF and SPARQL. It includes both simple and challenging hands-on exercises to practice and test your understanding.
The material for this course can be downloaded form the following link: https://github.com/paolo7/Introduction-to-Graph-Databases
Web open standards for linked data and knowledge graphs as enablers of EU dig...Fabien Gandon
Web open standards for linked data and knowledge graphs as enablers of EU digital sovereignty
ENDORSE Keynote by Fabien GANDON, 19/03/2021
https://op.europa.eu/en/web/endorse
Wimmics Research Team 2015 Activity ReportFabien Gandon
Extract of the activity report of the Wimmics joint research team between Inria Sophia Antipolis - Méditerranée and I3S (CNRS and Université Nice Sophia Antipolis). Wimmics stands for web-instrumented man-machine interactions, communities and semantics. The team focuses on bridging social semantics and formal semantics on the web.
One Web of pages, One Web of peoples, One Web of Services, One Web of Data, O...Fabien Gandon
Keynote Fabien GANDON, at WIM2016: One Web of pages, One Web of peoples, One Web of Services, One Web of Data, One Web of Things…and with the Semantic Web bind them.
JURIX talk on representing and reasoning on the deontic aspects of normative rules relying only on standard Semantic Web languages.
The corresponding paper is at https://hal.inria.fr/hal-01643769v1
Development of Semantic Web based Disaster Management SystemNIT Durgapur
Semantic Web model In the field of disaster management to structurise the data such that any information needed during emergency will be easily available.
a shift in our research focus: from knowledge acquisition to knowledge augmen...Fabien Gandon
EKAW 2022 keynote by Fabien GANDON: "a shift in our research focus: from knowledge acquisition to knowledge augmentation"
While EKAW started in 1987 as the European Knowledge Acquisition Workshop, in 2000 it transformed into a conference where we advance knowledge engineering and modelling in general. At the time, this transition also echoed shifts of focus such as moving from the paradigm of expert systems to the more encompassing one of knowledge-based systems. Nowadays, with the current strong interest for knowledge graphs, it is important again to reaffirm that our ultimate goal is not the acquisition of bigger siloed knowledge bases but to support knowledge requisition by and for all kinds of intelligence. Knowledge without intelligence is a highly perishable resource. Intelligence without knowledge is doomed to stagnation. We will defend that intelligence and knowledge, and their evolutions, have to be considered jointly and that the Web is providing a social hypermedia to link them in all their forms. Using examples from several projects, we will suggest that, just like intelligence augmentation and amplification insist on putting humans at the center of the design of artificial intelligence methods, we should think in terms of knowledge augmentation and amplification and we should design a knowledge web to be an enabler of the futures we want.
Wimmics Research Team 2015 Activity ReportFabien Gandon
Extract of the activity report of the Wimmics joint research team between Inria Sophia Antipolis - Méditerranée and I3S (CNRS and Université Nice Sophia Antipolis). Wimmics stands for web-instrumented man-machine interactions, communities and semantics. The team focuses on bridging social semantics and formal semantics on the web.
One Web of pages, One Web of peoples, One Web of Services, One Web of Data, O...Fabien Gandon
Keynote Fabien GANDON, at WIM2016: One Web of pages, One Web of peoples, One Web of Services, One Web of Data, One Web of Things…and with the Semantic Web bind them.
JURIX talk on representing and reasoning on the deontic aspects of normative rules relying only on standard Semantic Web languages.
The corresponding paper is at https://hal.inria.fr/hal-01643769v1
Development of Semantic Web based Disaster Management SystemNIT Durgapur
Semantic Web model In the field of disaster management to structurise the data such that any information needed during emergency will be easily available.
a shift in our research focus: from knowledge acquisition to knowledge augmen...Fabien Gandon
EKAW 2022 keynote by Fabien GANDON: "a shift in our research focus: from knowledge acquisition to knowledge augmentation"
While EKAW started in 1987 as the European Knowledge Acquisition Workshop, in 2000 it transformed into a conference where we advance knowledge engineering and modelling in general. At the time, this transition also echoed shifts of focus such as moving from the paradigm of expert systems to the more encompassing one of knowledge-based systems. Nowadays, with the current strong interest for knowledge graphs, it is important again to reaffirm that our ultimate goal is not the acquisition of bigger siloed knowledge bases but to support knowledge requisition by and for all kinds of intelligence. Knowledge without intelligence is a highly perishable resource. Intelligence without knowledge is doomed to stagnation. We will defend that intelligence and knowledge, and their evolutions, have to be considered jointly and that the Web is providing a social hypermedia to link them in all their forms. Using examples from several projects, we will suggest that, just like intelligence augmentation and amplification insist on putting humans at the center of the design of artificial intelligence methods, we should think in terms of knowledge augmentation and amplification and we should design a knowledge web to be an enabler of the futures we want.
An Introduction to Information Retrieval and Applicationssathish sak
An Introduction to Information Retrieval and Applications The score you get depends on the functions, difficulty and quality of your project
For system development:
System functions and correctness
For academic paper presentation
Quality and your presentation of the paper
Major methods/experimental results *must* be presented
Papers from top conferences are strongly suggested
E.g. SIGIR, WWW, CIKM, WSDM, JCDL, ICMR, …
Proposals are *required* for each team, and will be counted in the score
How the Web can change social science research (including yours)Frank van Harmelen
A presentation for a group of PhD students from the Leibniz Institutes (section B, social sciences) to discuss how they could use the Web, and even better the Web of Data, as an instrument in their research.
Talk about Exploring the Semantic Web, and particularly Linked Data, and the Rhizomer approach. Presented August 14th 2012 at the SRI AIC Seminar Series, Menlo Park, CA
EgoSystem: Presentation to LITA, American Library Association, Nov 8 2014James Powell
The Internet represents the connections among computers and devices, the world wide web is a network of interconnected documents, and the semantic web is the closest thing we have today to a network of interconnected facts. Noticeably absent from these global networks is any sort of open, formal representation for an online global social network. Each users' online presence, and its immediate social network, are isolated and typically only available within the confines of the social networking site that hosts it. Discovery across explicit online social networks and implicit social networks such as those that can be inferred from co-authorship relationships and affiliations is, for all practical purposes, impossible. And yet there are practical and non-nefarious reasons why an organization might be interested in exploring portions of such a network. Outreach is one such interest. Los Alamos National Laboratory (LANL) prototyped EgoSystem to harvest and explore the professional social networks of post doctoral students. The project's goal is to enlist past students and other Lab alumni as ambassadors and advocates for LANL's ongoing mission. During this talk we will discuss the various technologies that support the EgoSystem and demonstrate some of its capabilities.
Lecture at the advanced course on Data Science of the SIKS research school, May 20, 2016, Vught, The Netherlands.
Contents
-Why do we create Linked Open Data? Example questions from the Humanities and Social Sciences
-Introduction into Linked Open Data
-Lessons learned about the creation of Linked Open Data (link discovery, knowledge representation, evaluation).
-Accessing Linked Open Data
A talk about the gap between theory and practice with W3C Semantic Web and Dublin Core standards, and how the DC Tools Community can help collectively reduce the cost of that gap.
Given as part of the DC Tools Community workshop at LIDA2009 in Zadar, Croatia.
This slide deck has been prepared for a workshop on Linked Data Publishing and Semantic Processing using the Redlink platform (http://redlink.co). The workshop delivered at the Department of Information Engineering, Computer Science and Mathematics at Università degli Studi dell'Aquila aimed at providing a general understanding of Semantic Web Technologies and how these can be used in real world use cases such as Salzburgerland Tourismus.
A brief introduction has been also included on MICO (Media in Context) a European Union part-funded research project to provide cross-media analysis solutions for online multimedia producers.
Facets and Pivoting for Flexible and Usable Linked Data ExplorationRoberto García
The success of Open Data initiatives has increased the amount of data available on the Web. Unfortunately, most of this data is only available in raw tabular form, what makes analysis and reuse quite difficult for non-experts. Linked Data principles allow for a more sophisticated approach by making explicit both the structure and semantics of the data. However, from the end-user viewpoint, they continue to be monolithic files completely opaque or difficult to explore by making tedious semantic queries. Our objective is to facilitate the user to grasp what kind of entities are in the dataset, how they are interrelated, which are their main properties and values, etc. Rhizomer is a tool for data publishing whose interface provides a set of components borrowed from Information Architecture (IA) that facilitate awareness of the dataset at hand. It automatically generates navigation menus and facets based on the kinds of things in the dataset and how they are described through metadata properties and values. Moreover, motivated by recent tests with end-users, it also provides the possibility to pivot among the faceted views created for each class of resources in the dataset.
Walking Our Way to the Web - Fabien Gandon
The Web: Scientific Creativity, Technological Innovation and Society
XXVIII Conference on Contemporary Philosophy and Methodology of Science
9 and 10 March 2023
University of A Coruña
The prospect of Walking our Way to the Web may sound strange to contemporary readers of this article for whom the Web is omnipresent. However, the slogan of the World Wide Web Consortium (W3C) has been, for years, and remains today, to lead “the Web to its full potential” meaning we haven’t reached that potential yet, whatever it is. The first architect of the Web himself, Tim Berners-Lee, said in an interview in 2009: “The Web as I envisaged it, we have not seen it yet. The future is still so much bigger than the past”. And he is still very active, together with the W3C members and Web experts world-wide, in proposing evolutions of the Web architecture to improve its growing usages and applications. In this article we will review the path that led us to the actual Web, the shape it is taking now and the possible evolutions, good and bad, we can identify today. This will lead us to consider the distance that we witness between the initial vision and the reality of the Web today, and to reflect on the possible divergence between the potential we see in the Web and the directions it could take. Our goal in this article is to reflect on how we could walk the delicate path to the full potential of the Web, finding the missing links and avoiding the one too many links.
A Never-Ending Project for Humanity Called “the Web”Fabien Gandon
A Never-Ending Project for Humanity Called "the Web"
Fabien Gandon, Wendy Hall
https://hal.inria.fr/WIMMICS/hal-03633526
In this paper we summarized the main historical steps in making the Web, its foundational principles and its evolution. First we mention some of the influences and streams of thought that interacted to bring the Web about. Then we recall that its birthplace, the CERN, had a need for a global hypertext system and at the same time was the perfect microcosm to provide a cradle for the Web. We stress how this invention required to strike a balance between the integration of and the departure from the existing and emerging paradigms of the day. We then review the pillars of the Web architecture and the features that made the Web so viral compared to competitors. Finally we survey the multiple mutations the Web underwent no sooner it was born, evolving in multiple directions. We conclude on the fact the Web is now an architecture, an artefact, a science object and a research and development object, and of which we haven't seen the full potential yet.
CovidOnTheWeb : covid19 linked data published on the WebFabien Gandon
The Covid-on-the-Web project aims to allow biomedical researchers to access, query and make sense of COVID-19 related literature. To do so, it adapts, combines and extends tools to process, analyze and enrich the "COVID-19 Open Research Dataset" (CORD-19) that gathers 50,000+ full-text scientific articles related to the coronaviruses. We report on the RDF dataset and software resources produced in this project by leveraging skills in knowledge representation, text, data and argument mining, as well as data visualization and exploration. The dataset comprises two main knowledge graphs describing (1) named entities mentioned in the CORD-19 corpus and linked to DBpedia, Wikidata and other BioPortal vocabularies, and (2) arguments extracted using ACTA, a tool automating the extraction and visualization of argumentative graphs, meant to help clinicians analyze clinical trials and make decisions. On top of this dataset, we provide several visualization and exploration tools based on the Corese Semantic Web platform, MGExplorer visualization library, as well as the Jupyter Notebook technology. All along this initiative, we have been engaged in discussions with healthcare and medical research institutes to align our approach with the actual needs of the biomedical community, and we have paid particular attention to comply with the open and reproducible science goals, and the FAIR principles.
from linked data & knowledge graphs to linked intelligence & intelligence graphsFabien Gandon
ISWC Vision track talk "from linked data & knowledge graphs to linked intelligence & intelligence graphs or the potential of the semantic Web to break the walls between semantic networks and computational networks"
Retours sur le MOOC "Web Sémantique et Web de données"Fabien Gandon
Présentation des caractéristiques et résultats de la première session en 2015 du MOOC "Web Sémantique et Web de données" par Inria, Université de Nice, FUN et UNIT.
Nous lisons régulièrement que le Web révolutionne notre monde et provoque des évolutions dans toutes les dimensions de notre société. Mais le Web lui-même, ses usages et la compréhension que nous en avons n’ont pas cessé d’évoluer depuis la proposition à l’origine de sa création en 1989. C’est un espace en perpétuelle recréation qui nous demande sans cesse de nouvelles explorations et reconsidérations. Ce sont certains de ces changements passés, actuels, et à venir du Web que nous allons regarder ensemble en insistant sur la complexité de cet artefact qui en fait un objet de recherches pluridisciplinaires.
On Youtube: https://youtu.be/jNjHdqS-1Ko
on the ontological necessity of the multidisciplinary development of the webFabien Gandon
Talk on the ontological necessity of the multidisciplinary development of the web at the panel CLOSER/WEBIST 2014 on "social, political and economic implications of cloud and web"
Données de la culture et culture des donnéesFabien Gandon
Présentation "Données de la culture et culture des données" ou le web sémantique et les données liées sur le web dans le domaine de la culture à l'occasion de la conférence "Transmettre la culture à l’ère du numérique" dans le programme Automne Numérique du ministère de la Culture et de la Communication.
La vidéo de la conférence est ici:
http://www.dailymotion.com/video/x17i1g6_conference-transmettre-la-culture-a-l-age-du-numerique-fabien-gandon_tech
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. WIMMICS TEAM
▪ Inria
▪ CNRS
▪ University Côte D’Azur (UCA)
I3S
Web-Instrumented Man-Machine Interactions,
Communities and Semantics
3. MULTI-DISCIPLINARY TEAM
▪ 35~55 members
▪ ~15 nationalities
▪ 1 DR, 4 Professors
▪ 3CR, 3 Assistant professors
DR/Professors:
▪ Fabien GANDON, Inria, AI, KRR, Semantic Web, Social Web, K. Graphs
▪ Nhan LE THANH, UCA, Logics, KR, Emotions, Workflows, K. Graphs
▪ Peter SANDER, UCA, Web, Emotions
▪ Andrea TETTAMANZI, UCA, AI, Logics, Evo, Learning, Agents, K. Graphs
▪ Marco WINCKLER, UCA, Human-Computer Interaction, Web, K. Graphs
CR/Assistant Professors:
▪ Michel BUFFA, UCA, Web, Social Media, K. Graphs
▪ Elena CABRIO, UCA, NLP, KR, Linguistics, Q&A, Text Mining, K. Graphs
▪ Olivier CORBY, Inria, KR, AI, Sem. Web, Programming, K. Graphs
▪ Catherine FARON-ZUCKER, UCA, KR, AI, Semantic Web, K. Graphs
▪ Damien GRAUX, Inria, Linked Data, Sem. Web, Querying, K. Graphs
▪ Serena VILLATA, CNRS, AI, Argumentation, Licenses, Rights, K. Graphs
Research engineer: Franck MICHEL, CNRS, Linked Data, Integration, DB, K. Graphs
External:
▪ Andrei Ciortea (University of St. Gallen) Agents, WoT, Sem. Web, K. Graphs
▪ Nicolas DELAFORGE (Mnemotix) Sem. Web, KM, Integration, K. Graphs
▪ Alain GIBOIN, (Retired CR Inria), Interaction Design, KE, User & Task, K. Graphs
▪ Freddy LECUE (Thales, Montreal) AI, Logics, Mining, Big Data, S. Web , K. Graphs
5. CHALLENGES
typed graphs to analyze,
model, formalize and
implement social semantic
web applications for
epistemic communities
multidisciplinary approach for analyzing and modeling
▪the many aspects of intertwined information systems
▪communities of users and their interactions
formalizing and reasoning on these models using typed graphs
▪new analysis tools and indicators
▪new functionalities and better management
6. WEB GRAPHS
(meta)data of
the relations
and the
resources of the
web
…sites …social …of data …of services
+ + + +…
web…
= +
…semantics
+ + + +…
= +
typed
graphs
web
(graphs)
networks
(graphs)
linked data
(graphs)
workflows
(graphs)
schemas
(graphs)
7. URI, IRI, URL, HTTP URI
CONTRIBUTE TO DATA AND SCHEMATA STANDARDS ON THE WEB
JSON
RDF
JSON LD
N-Triple
N-Quad
Turtle/N3
TriG
RDFS
OWL
SPARQL
XML
HTML
RDF XML
HTTP
Linked Data
CSV-LD R2RML
GRDDL
RDFa
SHACL
LDP
9. The four research axes of Wimmics
-
contributing to research in AI and
Semantic Web along 4 axes:
1. Web-based user modeling and interaction
design
2. Social interactions and content analysis on
the Web
3. Knowledge extraction and representation
for and by linked data on the Web
4. Web-oriented and Web-dedicated artificial
intelligence algorithms
G2 H2
G1 H1
<
Gn Hn
12. PUBLISHING
▪ extract data (content, activity…)
▪ provide them as linked data
DBPEDIA.FR (extraction, end-point)
180 000 000 triples
models
Web architecture
[Cojan, Boyer et al.]
13. PUBLISHING
DBpedia.fr usage
number of queries per day
70 000 on average
2.5 millions max
185 377 686 RDF triples extracted and mapped
public dumps, endpoints, interfaces, APIs…
22. DBPEDIA & STTL
declarative transformation
language from RDF to text
formats (XML, JSON, HTML,
Latex, natural language, GML,
…) [Cojan, Corby, Faron-Zucker et al.]
23. 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
24. vs. use cases…
• Scenario 1: Help clinicians analyze clinical trials and take evidence-based decisions
• Scenario 3: Help missions heads from Cancer Institute elaborate research programs
to study the links between cancer and coronavirus
121
[Giboin, et al.]
25. COVID ON THE WEB
RDF
translator
Jupyter Notebook
Python, R & analytics
Corese
engine
query
&
infer
ACTA Web application
visualization of argument graphs
Corese portal
Data browsing
MGExplorer
Data visualization
Open Data publication
Zenodo, Github, Virtuoso
Named
Entities
extractor
s
Covid-on-the-Web
dataset
LOD
ACTA pipeline
extraction of argument graphs
1
1
2
2
1
3
3
3
vocabularies
& datasets
3
3
3
2
Covid-19
Open
Research
Dataset
Process data &
derive “smarter” data
Means to exploit data
[Michel, Gazzotti, Gandon et al.]
26. COVID ON THE WEB
Biomedical
researchers
& managers
Data analysts
…
RDF
translator
Jupyter Notebook
Python, R & analytics
Corese
engine
query
&
infer
ACTA Web application
visualization of argument graphs
Corese portal
Data browsing
MGExplorer
Data visualization
Open Data publication
Zenodo, Github, Virtuoso Applications
Named
Entities
extractor
s
dereference, query, download
query, browse, analyze, make sense
Covid-on-the-Web
dataset
LOD
ACTA pipeline
extraction of argument graphs
1
1
2
2
1
3
3
3
vocabularies
& datasets
3
3
3
2
Covid-19
Open
Research
Dataset
Process data &
derive “smarter” data
Means to exploit data Biomedical research
[Michel, Gazzotti, Gandon et al.]
27. Dataset description No. RDF triples
dataset description + definition of a few properties 170
articles metadata (title, authors, DOIs, journal etc.) 3 722 381
named entities identified by Entity-fishing in articles titles/abstracts 35 049 832
named entities identified by Entity-fishing in articles bodies 1 156 611 321
named entities identified by Bioportal Annotator in articles titles/abstracts 104 430 547
named entities identified by DBpedia Spotlight in articles titles/abstracts 65 359 664
argumentative components and PICO elements by ACTA from articles titles/abstracts 7 469 234
Total 1 361 451 364
125
31. PREDICT STUDENTS
▪ a model of the students' learning
▪ predict success or failure to questions
▪ features from KG representations
▪ Logistic Regression (LR) / Factorization Machines
(FM) / Deep Factorization Machines (DeepFM)
[Rodriguez-Rocha, Faron, Ettorre, Michel et al. 2020]
Answers
Questions
s: students identifiers
q: questions identifiers
r: responses identifiers
a: number of attempts
w: number of wins
T: questions text embeddings
Q: graph embeddings of the questions
R: graph embeddings of the answers
e: extra group of calculated features:
question_difficulty,student_ability,
student_ability_progressive,
student_ability_progressive_question_difficulty
Features
32. EDUMICS
▪ Ontology EduProgression: OWL modeling of scholar program
▪ Ontology RefEduclever: new education referential for Educlever
▪ Migration and persistence in graph databases
▪ Reasoning, query, interactions, recommendation
[Fokou, Faron et al. 2017]
34. QUESTION ROUTING
▪ emails to the customer service (eg 350000/day “Crédit Mutuel”)
▪ detect topics in order to “understand” a question
▪ 3 humans annotate 142 questions (Krippendorff’s Alpha 0,70)
▪ NLP and semantic processing for features extraction
▪ ML performance comparison for question classification
Naive Bayes, Sequential Minimal Optimisation (SMO),
Random Forest, RAndom k-labELsets (RAkEL)
[Gazzotti, et al. 2017]
NE
recognition
(L,T)
Removing
special
characters
Tokenization
(L,T)
Spell
Checking
(L,T)
Lemmatization
(L)
Vector
generation
BOW/N-gram
Replacement in documents
Consider as feature
Input
Document
ML
workflow
L: Language dependent - T: Text dependent
Unbalanced Topics
Metrics uni uni⨁bi uni+bi+tri uni⨁NE syn syn⨁hyper syn⨁NE
Hamming Loss 0,0381 0,0370 0,0374 0,0373 0,0399 0,0412 0,0405
36. SCIENTIFIC HERITAGE
▪ TAXREF Vocabulary
▪ Data extraction and
publication
[Tounsi, Callou, Michel, Pajo, Faron Zucker et al.]
37. rr:objectMap
1
1
0-1
0-1
1
0-1
0-1
0-1
0-1
1
1
rr:GraphMap
rr:graphMap
0-1
xrr:logicalSource
xrr:LogicalSource
xrr:query
Query String
rml:iterator Iteration pattern
rr:IRI, rr:BlankNode,rr:Literal,
xrr:RdfList, xrr:RdfBag,
xrr:RdfSeq, xrr:RdfAlt
reference expr.
xrr:nestedTermMap
xrr:NestedTermMap
rr:inverseExrpression
xrr:reference
reference expr.
reference expr.
rr:ObjectMap
HETEROGENEITY
xR2RML mapping language
and SPARQL query rewriting
[Michel et al.]
<AbstractQuery> ::= <AtomicQuery> | <Query> |
<Query> FILTER <SPARQL filter> | <Query> LIMIT <integer>
<Query> ::= <AbstractQuery> INNER JOIN <AbstractQuery> ON {v1, … vn} |
<AtomicQuery> AS child INNER JOIN <AtomicQuery> AS parent
ON child/<Ref> = parent/<Ref> |
<AbstractQuery> LEFT OUTER JOIN <AbstractQuery> ON {v1, … vn} |
<AbstractQuery> UNION <AbstractQuery>
<AtomicQuery> ::= {From, Project, Where, Limit}
<Ref> ::= a valid xR2RML data element reference
38. µSERVICES
Linked Data access to Web APIs.
[Michel et al.]
SPARQL Client
Service Logics
Web API
JSON-LD
Profile
SPARQL
INSERT/CONSTR
HTTP
query
JSON
response
Triple
store
SPARQL Micro-Service
(1)
(4) (2)
(3)
LD Client Web Server
(1’)
(4’)
http://example.org/photo/472495
39. LD µSERVICES
APIs as linked data
[Michel , et al.]
SPARQL micro-
service
SPARQL SD
graph
Shapes
graph
SPARQL engine
Web
API
(5)
40. LD µSERVICES
APIs as linked data
[Michel , et al.]
HTML
JDON-LD
</>
SPARQL micro-
service
(1)
(4) SPARQL query
LD-based
application
SPARQL SD
graph
Shapes
graph
SPARQL engine
Web
API
(5)
41. LD µSERVICES
APIs as linked data
[Michel , et al.]
HTML
JDON-LD
</>
SPARQL micro-
service
(1)
(4) SPARQL query
LD-based
application
SPARQL SD
graph
Shapes
graph
SPARQL engine
Web
API
(5)
&
43. UNCERTAINTY
▪ Representing uncertainty theories
▪ Publishing it with linked data
▪ Negotiating the theory over HTTP
▪ Combining uncertainty statements
[Djebri, Tettamanzi, Gandon, 2019]
44. UNCERTAINTY
publishing theories and calculi as linked data
[Djebri, Tettamanzi, Gandon, 2019]
prob:Probability a munc:UncertaintyApproach;
munc:hasUncertaintyFeature prob:probabilityValue;
munc:hasUncertaintyOperator prob:and.
prob:probabilityValue prob:and prob:multiplyProbability.
prob:Probability prob:probabilityValue
prob:and
ex:multiplyProbability
munc:hasUncertainty
Feature
munc:hasUncertainty
Operator
46. UNCERTAINTY
translate and negotiate theories
[Djebri et al 2019]
• Specify uncertainty in parameter linked to the format
• GET /some/resource HTTP/1.1
Accept:
text/turtle;uncertainty="http://example.com/Probability";q=0.8,
text/turtle;uncertainty="http://example.com/Possibility";q=0.2;
• Use uncertainty as a profile : prof-Conneg
• GET /some/resource HTTP/1.1
Accept: text/turtle;q=0.8;profile="prob:Probability",
text/turtle;q=0.2;profile="poss:Possibility"
• HEAD /some/resource HTTP/1.1
Accept: text/turtle;q=0.9,application/rdf+xml;q=0.5
Link: <http://example.com/Probability>; rel="profile" (RFC 6906)
• GET /some/resource HTTP/1.1
47. MoReWAIS Mobile Read Write Access and Intermittent to Semantic Web
France (Wimmics, Inria) – Senegal (LANI, UGB Saint-Louis) Project
explore the specificities (advantages and constraints) of mobile P2P knowledge
sharing and addressing its limitations (e.g. intermittent access, limited resources)
[Toure et al.]
&
48. MoRAI: Geographic and Semantic Overlay Network
• Three-level P2P architecture : mobile peers, super-peers and remote sources
• Random Peer Sampling (RPS) overlay +
Semantic Overlay Network (SON) +
Geographic Overlay Network (GON)
• Experimental validation/simulation
[Toure et al.]
49. CRAWLING
▪ Predict data availability
▪ Select features of URIs
▪ Learn crawling selection
(KNN/NaiveBayes/SVM)
▪ Online learning w. crawling
(FTRL-proximal algorithm)
[Huang, Gandon 2019]
50. QUERY
• automatically suggest relevant data sources to solve a query
• sets of path features: star, sink, chain
• approximate containment search: locality sensitive hashing
[Huang, Gandon 2020]
65. INTERACTION
design and evaluation
Favoris
Nouvelle recherche TEMPS
Debut test Free Jazz 24s
Free improvisation 33s
(fiche) Avant-garde 47s
John Coltrane (vidéo) 1min 28
Marc Ribot 2min11
(fiche) experimental music 2min18 2min23
Krautrock 2min31
(fiche) Progressive rock 2min37 2min39
Red (King Crimson album) 2m52 2min59
King
Crimson 3min05
(fiche) Jazz fusion 3min18
(fiche) Free Jazz 3min32 3min54
Sun Ra 4min18
(fiche) Hard bop 4min41 4min47
Charles
Mingus (vidéo) 5min29
(fiche) Third Stream (vidéo) 6min20
Bebop 7min19
Modal jazz 7min26
(fiche) Saxophone 7min51 7min55
Mel Collins
21st CenturySchizoid Band
Crimson Jazz Trio
(fiche)
King
Crimson
(fiche)
Robert
Fripp
Miles Davis
Thelonious Monk
(fiche) Blue Note Record
McCoy Tyner
(fiche) Modal Jazz
(fiche) Jazz
Chick Corea
(fiche) Jazz Fusion
Return to Forever
MahavishnuOrchestra
Shakti (band)
U.Srinivas
Bela Fleck
Flecktones
John McLaughlin (musician)
Dixie Dregs
FICHE Dixie Degs
T Lavitz
Jordan Rudess
Behold… The Arctopus
(fiche) Avant-garde metal
Unexpected
FICHE unexpected
Dream Theater
King
Crimson
(fiche) Jazz fusion
King
Crimson
TonyLevin
(fiche) Anderson Bruford Wakeman Howe
(fiche) Rike Wakeman (vidéo)
Fin test
[Palagi, Marie, Giboin et al.]
67. METHODS & CRITERIA
▪ interaction design and evaluation
▪ exploratory search process model
[Palagi, Giboin et al. 2018]
A. Define the search space
B. Query (re)formulation
C. Information gathering
D. Put some information aside
E. Pinpoint search
F. Change of goal(s)
G. Backward/forward steps
H. Browsing results
I. Results analysis
J. Stop the search session
Previous features Feature Next features
NA A B ; J
A ; F B G ; H ; I ; J
D ; E ; I C D ; E ; F ; G ; H ; J
E ; I D C ; F ; G ; J
G ; H ; I E C ; D ; F ; G ; J
C ; D ; E ; G ; H ; I F B ; H ; I ; J
B ; D ; E ; H ; I G E ; F ; H ; I ; J
B ; F ; G ; I H E ; F ; G ; ; I ; J
B ; F ; G ; H I C ; D ; E ; F ; G ; H ; J
all J NA
70. “
« a Web-Augmented Interaction (WAI) is a
user’s interaction with a system that is
improved by allowing the system to
access Web resources »
[Gandon, Giboin, WebSci17]
72. ALOOF: Web and Perception
[Cabrio, Basile et al.]
Semantic Web-Mining and Deep Vision for Lifelong Object Discovery (ICRA 2017)
Making Sense of Indoor Spaces using Semantic Web Mining and Situated Robot Perception (AnSWeR 2017)
73. ALOOF: robots learning by reading on the Web
Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen
[Cabrio, Basile et al.]
74. ALOOF: robots learning by reading on the Web
First Object Relation Knowledge Base:
46.212 co-mentions gave 49 tools, 14
rooms, 101 “possible location” relations,
Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen
[Cabrio, Basile et al.]
75. ALOOF: robots learning by reading on the Web
▪ First Object Relation 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
Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen
[Cabrio, Basile
et al. 2017]
76. ALOOF: RDF dataset about objects
[Cabrio, Basile et al.]
▪ common sense knowledge about objects: classification, prototypical locations
and actions
▪ knowledge extracted from natural language parsing, crowdsourcing,
distributional semantics, keyword linking, ...
82. SENTIMENTS
▪ sentiment recognition in search results
▪ Automatic detection of affect
▪ Automatically interplay emotions to sentiment polarity
▪ Broader sense of sentiment ( Valence, Arousal, Dominance)
▪ Use case: Brexit scenario
▪ Propaganda detection based on argumentative techniques
[Vorakitphan, Cabrio, Villata 2020]
83. OPINIONS
NLP, ML and arguments
to monitor online image
[Villata, Cabrio, et al.]
84. ARGUMENT MINING ON
CLINICAL TRIALS
▪ NLP, ML and arguments
▪ assist evidence-based medicine
▪ support doctors and clinicians
▪ identify doc. for certain disease
▪ analyze argumentative content
and PICO elements
[Mayer, Cabrio, Villata]
85. ARGUMENT MINING ON
POLITICAL SPEECHES
▪ NLP and Machine Learning.
▪ Support historians/social science scholars
▪ Analyze arguments in political speeches
▪ DISPUTool : 39 political debates,
last 50 years of US presidential
campaigns (1960-2016)
[Mayer, Cabrio, Villata]
94. FO → R GF GR
mapping modulo an ontology
car
vehicle
car(x)vehicle(x)
GF
GR
vehicle
car
O
RIF-BLD SPARQL RIFSPARQL
?x ?x
C C
List(T1. . . Tn) (T1’. . . Tn’)
OpenList(T1. . . Tn T)
External(op((T1. . . Tn))) Filter(op’ (T1’. . . Tn’))
T1 = T2 Filter(T1’ =T2’)
X # C X’ rdf:type C’
T1 ## T2 T1’ rdfs:subClassOf T2’
C(A1 ->V1 . . .An ->Vn)
C(T1 . . . Tn)
AND(A1. . . An) A1’. . . An’
Or(A1. . . An) {A1’} …UNION {An’}
OPTIONAL{B}
Exists ?x1 . . . ?xn (A) A’
Forall ?x1 . . . ?xn (H)
Forall ?x1 . . . ?xn (H:- B) CONSTRUCT { H’}
WHERE{ B’}
restrictions
equivalence no equivalence
extensions
95. FO → R GF GR
mapping modulo an ontology
car
vehicle
car(x)vehicle(x)
GF
GR
vehicle
car
O
truck
car
=
1
2
1 ,
, )
(
2
1
2
1
2
2
1
2
1
)
,
(
let
;
)
,
( t
t
t
t
t t
depth
H
c t
t
l
t
t
H
t
t c
( )
)
,
(
)
,
(
min
)
,
(
let
)
,
( 2
1
,
2
1
2
2
1 2
1
t
t
l
t
t
l
t
t
dist
H
t
t c
c H
H
t
t
t
t
c +
=
vehicle
car
O
truck
t1(x)t2(x) → d(t1,t2)< threshold
98. FO → R GD GQ
mapping modulo an ontology
Lymphoma
Cancer
Lymphoma(x) Cancer(x)
GD
GQ
Cancer
Lymphoma
O
[Corby et al.]
AI methods: knowledge graphs, ontology-based formalisms, querying, validating and reasoning
218
101. LDSCRIPT
a Linked Data Script Language
FUNCTION us:status(?x) {
IF (EXISTS { ?x ex:hasSpouse ?y }||EXISTS { ?y ex:hasSpouse ?x },
ex:Married, ex:Single) }
[Corby, Faron Zucker, Gandon, ISWC 2017]
102. SPARQL ENDPOINT ACCESS CONTROL
Protect SPARQL endpoint from hostile actions
Set of protected Features:
SPARQL Update, Load RDF data, Service clause
Set of Access Rights:
PUBLIC, PROTECTED, PRIVATE
Assign Access Rights to Features:
SPARQL Update -> PRIVATE
Service <http://fr.dbpedia.org> -> PROTECTED
Assign Access Right to User Action:
User query -> PUBLIC
PUBLIC action cannot access PRIVATE Feature
[Corby, 2021]
103. RDF & SPARQL ACCESS CONTROL
Assign Access Right to RDF triples and SPARQL
Queries
• SPARQL Query has access to subset of RDF triples
• RDF Graph extended with Access Rights
• SPARQL Interpreter extended with Access Rights
e.g.
Assign Access Rights to RDF triples according to URIs or namespaces
URI foaf:address -> PRIVATE
Namespace foaf: -> PUBLIC
select * where { ?x ?p ?y } -> PUBLIC
Query can access PUBLIC foaf:name
Query cannot access PRIVATE foaf:address
[Corby, 2021]
106. This project has received funding from the European Union's Horizon 2020
research and innovation programme under grant agreement 825619.
ONTOLOGY FOR AI ITSELF
▪ ontology and metadata of AI resources
▪ SHACL to validate AI4EU these RDF graphs
▪ online endpoint http://corese.inria.fr
▪ predefined SPARQL queries, SHACL shapes, display
[Corby et al., 2019]
107. mining interesting association rules
AI methods: clustering + community detection + dimensionality
reduction (auto-encoder) + Frequent Pattern Growth
[Cadorel, Tettamanzi]
241
[WI-IAT 2020]
108. mining interesting association rules
AI methods: clustering + community detection + dimensionality
reduction (auto-encoder) + Frequent Pattern Growth
• hidden patterns to enrich the dataset
• novel hypotheses for biomedical research
[Cadorel, Tettamanzi]
242
[WI-IAT 2020]
109. mining interesting association rules
AI methods: clustering + community detection + dimensionality
reduction (auto-encoder) + Frequent Pattern Growth
• hidden patterns to enrich the dataset
• novel hypotheses for biomedical research
• error detection in the dataset
• relevant clusters & communities for navigation
[Cadorel, Tettamanzi]
243
[WI-IAT 2020]
112. DEONTICS
Legal Rules on the Semantic Web
OWL + Named Graphs + SPARQL Rules
Named Graph (state of affair) Subject Predicate Object
http://ns.inria.fr/nrv-inst#StateOfAffairs1 Tom http://ns.inria.fr/nrv-inst#activity driving at 100km/h
http://ns.inria.fr/nrv-inst#StateOfAffairs1 Tom http://www.w3.org/2000/01/rdf-schema#label Tom
http://ns.inria.fr/nrv-inst#StateOfAffairs1 can't drive over 90km http://www.w3.org/1999/02/22-rdf-syntax-ns#type violated requirement
http://ns.inria.fr/nrv-inst#StateOfAffairs1 can't drive over 90km has for violation http://ns.inria.fr/nrv-inst#StateOfAffairs1
http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://ns.inria.fr/nrv-inst#speed 100
http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://ns.inria.fr/nrv-inst#Driving
http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://www.w3.org/2000/01/rdf-schema#label "driving at 100km/h"@en
Named Graph (state of affair) Subject Predicate Object
http://ns.inria.fr/nrv-inst#StateOfAffairs2 Jim http://ns.inria.fr/nrv-inst#activity driving at 90km/h
http://ns.inria.fr/nrv-inst#StateOfAffairs2 Jim http://www.w3.org/2000/01/rdf-schema#label Jim
http://ns.inria.fr/nrv-inst#StateOfAffairs2 can't drive over 90km http://www.w3.org/1999/02/22-rdf-syntax-ns#type compliant requirement
http://ns.inria.fr/nrv-inst#StateOfAffairs2 can't drive over 90km has for compliance http://ns.inria.fr/nrv-inst#StateOfAffairs2
http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://ns.inria.fr/nrv-inst#speed 90
http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://ns.inria.fr/nrv-inst#Driving
http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://www.w3.org/2000/01/rdf-schema#label "driving at 90km/h"@en
[Gandon et al.]
114. PREDICT HOSPITALIZATION
▪ Predict hospitalization from
Physician’s records classification
▪ Augment records data with
Web knowledge graphs
▪ Study impact on prediction
[Gazzotti, Faron, Gandon et al. 2020]
Sexe Date Cause CISP2 ... History Observations
H 25/04/2012 vaccin-antitétanique A44 ... Appendicite EN CP - Bon état général - auscult
pulm libre; bdc rég sans souffle -
tympans ok-
Element Number
Patients
Consultations
Past medical history
Biometric data
Semiotics
Diagnosis
Row of prescribed drugs
Symptoms
Health care procedures
Additional examination
Paramedical prescription
Observations/notes
55 823
364 684
187 290
293 908
250 669
117 442
847 422
23 488
11 850
871 590
17 222
56 143
(1)
(2)
PRIMEGE
115. Image Metadata Score
portrait
50350012455
C:Jocondejoconde0138m503501_d0012455-000_p.jpg
cheval:
0.999
Image Metadata Score
figure (saint Eloi de Noyon, évêque, en pied, bénédiction,
vêtement liturgique, mitre, attribut, cheval, marteau, outil :
ferronnerie)
000SC022652
C:/Joconde/joconde0355/m079806_bsa0030101_p.jpg
cheval:
0.006
MonaLIA
▪ 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
350 000 images
of artworks
RDF metadata based
on external thesauri
Joconde database from French museums
(1)
(3)
[Bobasheva, Gandon, Precioso, 2021]
(2)
116. MonaLIA 2.0 Approach
• SPARQL+RDFS+SKOS on metadata to extract training and
test subsets of images
• create labeled training and test sets including the “narrower”
categories according to Garnier Thesaurus
• create “missing” links between some categories
• balance number of training images per class
• filter out certain categories and images
• Train Multi-Label Deep Learning classifier
• select state-of-the-art pre-trained CNN model
• adapt the model to multi-label classification
• fine-tune model on artwork images
• optimize model hyperparameters for best performance
• Apply trained model and extend metadata
• run all the images through the trained classifier
• record the prediction score as RDF triples
• SPARQL on extended metadata to search the database
(Maasai & Wimmics)
117. Detecting “noise”
By querying the extended metadata for the objects with low scores we
can detect the “noise” in the represented subject annotation
Image Metadata Score
figure (saint Eloi de Noyon, évêque, en pied, bénédiction, vêtement
liturgique, mitre, attribut, cheval, marteau, outil : ferronnerie)
000SC022652
C:/Joconde/joconde0355/m079806_bsa0030101_p.jpg
cheval: 0.006
figures bibliques (Vierge à l'Enfant, à mi-corps, assis, Enfant Jésus : nu,
livre);fond de paysage (colline, cours d'eau, barque, cavalier)
000PE027041
C:/Joconde/joconde0001/m503604_90ee1719_p.jpg
cheval: 0.009
scène (satirique : Bismarck Otto von : Gargantua, repas, cheval, boisson :
vin)
5002E006121
C:/Joconde/joconde0074/m500202_atpico-g70128_p.jpg
cheval: 0.011
118. Detecting “silence”
By querying the extended metadata for the object with high scores and
without object mentioned in annotation we can detect the “silence” in the
annotation
Image Metadata Score
portrait
50350012455
C:Jocondejoconde0138m503501_d0012455-000_p.jpg
cheval: 0.999
scène historique (guerre de siège : Lawfeld, Louis XV, Saxe maréchal de,
bataille rangée)
000PE004371
C:Jocondejoconde0634m507704_79ee519_p.jpg
cheval: 0.999
figure (sainte Jeanne d'Arc, jeune fille, équestre passant, armure,
asque, épée)
M0301000355
C:Jocondejoconde0617m030106_007305_p.jpg
cheval: 0.997
119. Ranking of search results
Running the same query on the Extended Joconde database and sorting by
score gives a better result putting the image in the second place
Image Metadata Score
représentation animalière (épagneul, debout)
M0341003743
C:Jocondejoconde0534m034186_006932_p.jpg
chien: 0.994
scène (chasse : lévrier, lièvre)
M0810001165
C:Jocondejoconde0466m081003_028491_p.jpg
chien: 0.993
représentation animalière (mise à mort, gros gibier : sanglier, chasse à
courre, chien)
00000105149
C:Jocondejoconde0107m505206_oa817_p.jpg
chien: 0.990
120. Hypermedia MAS
▪ Bridging Web architecture and Multi-Agent Systems architecture
▪ Hypermedia Communities of People and Autonomous Agents
▪ Define an architectural style for Hypermedia MAS
▪ Define declarative languages and mechanisms for specifying, enacting, and
regulating interactions among people and autonomous agents in
Hypermedia MAS
▪ Develop an open-source software infrastructure for Hypermedia MAS that
enables the deployment of hybrid communities on the Web
▪ Demonstrate the deployment of prototypical hybrid communities in two
application areas: (i) Industry 4.0 and (ii) tackling online disinformation.
http://hyperagents.gitlab.emse.fr/#
125. Toward a Web of Programs
“We have the potential for every HTML document to be a
computer — and for it to be programmable. Because the thing
about a Turing complete computer is that … anything you can
imagine doing, you should be able to program.”
(Tim Berners-Lee, 2015)
128. Make the Web AI-friendly
content, links, metadata, etc.
data, knowledge, etc.
AI Web bots: chat bots, recommenders, facilitators, etc.
configuration, parameters, embeddings, services,
communication, etc.
140. WIMMICS
1. user & interaction design
2. communities & social networks
3. linked data & semantic Web
4. reasoning & analyzing
epistemic hybrid communities
linked data
usages and introspection
contributions and traces
142. WIMMICS
Web-instrumented man-machine interactions, communities and semantics
Fabien Gandon - @fabien_gandon - http://fabien.info
he who controls metadata, controls the web
and through the world-wide web many things in our world.
Technical details: http://bit.ly/wimmics-papers