The construction of a knowledge graph (KG) can be performed manually. Nevertheless, ensuring minimal coverage of a KG often requires the automatic data extraction from multiple sources. However, sources and extraction algorithms often vary in quality, may provide conflicting data with different levels of specificity or even contradict each other for the same entity. To reconcile these conflicting data and integrate them consistently within the KG, numerous fusion models can be adopted that simultaneously evaluate both the quality of the sources and the data provided. However, most of these models are usually evaluated on datasets that do not specifically represent differences in specificity, the heterogeneity of data types, or the presence of long-tail entities. These three challenges are frequently encountered in KG construction, making the data fusion process more complex. In this paper, we propose to overcome these limitations by introducing WikiConflict, a dataset built from the Wikidata revision history and designed for KG construction.

To build a knowledge graph, knowledge can be extracted from multiple data sources. However, for a given topic, multiple data sources rarely provide a unified view of the data. The data may differ in unit scales, levels of specificity, or even be contradictory. To jointly find the most trustworthy data and evaluate the reliability of the sources, data fusion approaches are usually applied. Although existing tools implement such approaches, they often lack essential functionalities such as a template for developing data fusion approaches, evaluation metrics, or a user-friendly visualization of the fused results. To overcome these limitations, we introduce TrustFuse, a comprehensive testbed that supports experimentation with fusion models, their evaluation, and the visualization of datasets as graphs or tables within a unified user interface.

Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating them as a whole may entail irrelevant content and scalability issues. We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities. We empirically show that our analogy-based approach outperforms LSTM, Random Forest, SVM, and MLP, with a drastically lower number of parameters. We also evaluate its generalization potential in a transfer learning setting. These results advocate for the further integration of analogy-based inference in tasks related to the KG lifecycle.

Analogical proportions are statements of the form “A is to B as C is to D” that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them.

Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML. In this paper, we address process fairness of ML models that consists in reducing the dependence of models on sensitive features, without compromising their performance. We revisit the framework FixOut that is inspired in the approach “fairness through unawareness” to build fairer models. We introduce several improvements such as automating the choice of FixOut's parameters. Also, FixOut was originally proposed to improve fairness of ML models on tabular data. We also demonstrate the feasibility of FixOut's workflow for models on textual data. We present several experimental results that illustrate the fact that FixOut improves process fairness on different classification settings.

A current challenge in graph clustering is to tackle the issue of complex networks, i.e , graphs with attributed vertices and/or edges. In this paper, we present GraphTrees, a novel method that relies on random decision trees to compute pairwise dissimilarities between vertices in a graph. We show that using different types of trees, it is possible to extend this framework to graphs where the vertices have attributes. While many existing methods that tackle the problem of clustering vertices in an attributed graph are limited to categorical attributes, GraphTrees can handle heterogeneous types of vertex attributes. Moreover, unlike other approaches, the attributes do not need to be preprocessed. We also show that our approach is competitive with well-known methods in the case of non-attributed graphs in terms of quality of clustering, and provides promising results in the case of vertex-attributed graphs. By extending the use of an already well established approach – the random trees – to graphs, our proposed approach opens new research directions, by leveraging decades of research on this topic.