Opendata, web and dolomites


Principles of Graph Data Integration

Total Cost €


EC-Contrib. €






Project "GraphInt" data sheet

The following table provides information about the project.


Organization address
address: AVENUE DE L EUROPE 20
postcode: 1700

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Project website
 Total cost 1˙998˙339 €
 EC max contribution 1˙998˙339 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-CoG
 Funding Scheme ERC-COG
 Starting year 2016
 Duration (year-month-day) from 2016-08-01   to  2021-07-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITE DE FRIBOURG CH (FRIBOURG) coordinator 1˙998˙339.00


 Project objective

The present proposal tackles fundamental problems in data management, leveraging expressive, large-scale and heterogeneous graph structures in order to integrate both unstructured (e.g., text) and structured (e.g., relational) content. Integrating heterogeneous content has become a key hurdle in the deployment of Big Data applications, due to the meteoric rise of both machine and user-generated data storing information in a variety of formats. Traditional integration techniques cleaning up, fusing and then mapping heterogeneous data onto rigid abstractions fall short of accurately capturing the complexity and wild heterogeneity of today’s information. Having closely followed the emergence of heterogeneous information sources online, I am convinced that only an interdisciplinary approach drawing both from classical data management and from large-scale Web information processing techniques can solve the formidable data integration challenges that they pose. The following project proposes an ambitious overhaul of information integration techniques embracing the scale and heterogeneity of today’s data. I propose the use of expressive and heterogeneous graphs of entities to continuously and dynamically interrelate disparate pieces of content while capturing their idiosyncrasies. The following project focuses on three core issues related to large-scale and heterogeneous information graphs: i) the effective extraction of fined-grained information from unstructured sources and their proper integration into large-scale heterogeneous and probabilistic graphs, ii) the creation of novel physical storage structures and primitives to durably and efficiently manage the profusion of data considered by such graphs using clusters of commodity machines, and iii) the development of logical data abstraction mechanisms facilitating the effective and efficient resolution of complex analytic and data integration queries on top of the physical layer.


year authors and title journal last update
List of publications.
2019 Michael Luggen, Djellel Difallah, Cristina Sarasua, Gianluca Demartini, and Philippe Cudre-Mauroux
Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs — The Case of Wikidata
published pages: , ISSN: , DOI:
International Semantic Web Conference (ISWC) 2019 2019-10-01
2019 Artem Lutov, Mourad Khayati, Philippe Cudré-Mauroux
Accuracy Evaluation of Overlapping and Multi-resolution Clustering Algorithms on Large Datasets
published pages: , ISSN: , DOI:
BigComp 2019 2019-04-13
2019 Alisa Smirnova, Philippe Cudré-Mauroux
Relation Extraction Using Distant Supervision
published pages: 1-35, ISSN: 0360-0300, DOI: 10.1145/3241741
ACM Computing Surveys 51/5 2019-04-13
2019 Alberto Lerner, Rana Hussein, Philippe Cudré-Mauroux
The Case for Network Accelerated Query Processing
published pages: , ISSN: , DOI:
CIDR 2019 2019-04-13
2019 Natalia Ostapuk, Jie Yang, Philippe Cudré-Mauroux
ActiveLink: Deep Active Learning for Link Prediction in Knowledge Graphs.
published pages: , ISSN: , DOI:
The Web Conf (WWW 2019) 2019-04-13
2019 Dingqi Yang, Bingqing Qu, Philippe Cudre-Mauroux
Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation
published pages: 507-520, ISSN: 1041-4347, DOI: 10.1109/tkde.2018.2840974
IEEE Transactions on Knowledge and Data Engineering 31/3 2019-04-13
2018 Leye Wang, Gehua Qin, Dingqi Yang, Xiao Han, Xiaojuan Ma
Geographic Differential Privacy for Mobile Crowd Coverage Maximization
published pages: 200-207, ISSN: , DOI:
AAAI18 2019-04-13
2019 Dingqi Yang, Bin Li, Laura Rettig, Philippe Cudre-Mauroux
D2 HistoSketch: Discriminative and Dynamic Similarity-Preserving Sketching of Streaming Histograms
published pages: 1-1, ISSN: 1041-4347, DOI: 10.1109/tkde.2018.2867468
IEEE Transactions on Knowledge and Data Engineering 2019-04-13
2019 Jie Yang, Alisa Smirnova, Dingqi Yang, Gianluca Demartini, Yuan Lu, Philippe Cudre-Mauroux
Scalpel-CD: Leveraging Crowdsourcing and Deep Probabilistic Modeling for Debugging Noisy Training Data
published pages: , ISSN: , DOI:
The Web Conf (WWW 2019) 2019-04-13
2019 Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudre-Mauroux
Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach
published pages: , ISSN: , DOI:
The Web Conf (WWW 2019) 2019-04-13

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "GRAPHINT" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email ( and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "GRAPHINT" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.1.)

Photopharm (2020)

Photopharmacology: From Academia toward the Clinic.

Read More  


The Mass Politics of Disintegration

Read More  


The Enemy of the Good: Towards a Theory of Moral Progress

Read More