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Université de Fribourg

The signed distance measure in fuzzy statistical analysis : Some theoretical, empirical and programming advances

Berkachy, Rédina ; Donzé, Laurent (Dir.)

Thèse de doctorat : Université de Fribourg, 2020.

This thesis intends to present some advances in fuzzy statistical analyses. A particular distance between fuzzy numbers called the signed distance, seems to be appealing because of its directional property. It has the ability of describing the direction of travel between two fuzzy numbers. In addition, it has been often used as a fuzzy ranking tool or a defuzzification operator. Despite the...

Université de Fribourg

Unsupervised and Parameter-Free Clustering of Large Graphs for Knowledge Exploration and Recommendation

Lutov, Artem ; Cudré-Mauroux, Philippe (Dir.)

Thèse de doctorat : Université de Fribourg, 2020 ; no. 2192.

We live in an Information Age, facing a rapid increase in the amount of information that is exchanged. This permanently growing amount of data makes the ability to store, analyze, and act upon information a primary concern (in addition to the obvious privacy, legal and ethical issues that are related), raising the question: “How can one consume Big Data and transformit into actionable...

Université de Fribourg

APCNN: Tackling Cclass imbalance in relation extraction through aggregated piecewise convolutional neural networks

Smirnova, Alisa ; Audiffren, Julien ; Cudré-Mauroux, Philippe

In: 2019 6th Swiss Conference on Data Science (SDS), 2019, p. 63–68

One of the major difficulties in applying distant supervision to relation extraction is class imbalance, as the distribution of relations appearing in text is heavily skewed. This is particularly damaging for the multi-instance variant of relation extraction. In this work, we introduce a new model called Aggregated Piecewise Convolutional Neural Networks, or APCNN, to address this problem....

Université de Fribourg

Nodesketch: highly-efficient graph embeddings via recursive sketching

Yang, Dingqi ; Rosso, Paolo ; Li, Bin ; Cudré-Mauroux, Philippe

In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, p. 1162–1172

Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix factorization. However, these techniques...

Université de Fribourg

Non-parametric class completeness estimators for collaborative knowledge graphs — the case of wikidata

Luggen, Michael ; Difallah, Djellel ; Sarasua, Cristina ; Demartini, Gianluca ; Cudré-Mauroux, Philippe

In: The Semantic Web – ISWC 2019, 2019, p. 453-469

Collaborative Knowledge Graph platforms allow humans and automated scripts to collaborate in creating, updating and interlinking entities and facts. To ensure both the completeness of the data as well as a uniform coverage of the different topics, it is crucial to identify underrepresented classes in the Knowledge Graph. In this paper, we tackle this problem by developing statistical techniques...

Université de Fribourg

A Fuzzy-Based User Privacy Framework and Recommender System : Case of a Platform for Political Participation

Kaskina, Aigul ; Meier, Andreas (Dir.)

Thèse de doctorat : Université de Fribourg, 2018 ; no. 2098.

When entering a new era of digital societies, a vast number of digital footprints left by users becomes a new source of the economic good. A heavy exploitation of Big data, artificial intelligence, cognitive computing, amongst others, makes the data more valuable implying that the risk to people’s privacy will be ever-increasing. Especially, when their privacy decisions are confronted with...

Université de Fribourg

Authorship attribution and profiling in Spanish and English language

Miculicich, Lesly ; Savoy, Jacques (Dir.)

Mémoire de master : Université de Fribourg, 2014.

The authorship attribution is the practice of inferring the author of a given text based on the analysis of her/his writing style. It has been largely used in literature work disputes but it has other interesting applications such as forensics and plagiarism detection. The purpose of this project is to experiment and present a solution that can identify the authors of a given corpora. We have two...