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.

Extracting structured knowledge from text in the form of (subject, predicate, object) triples is a key task for many artificial intelligence applications, in particular for knowledge graphs (KGs) construction. In closed information extraction (cIE) where the extracted triples are constrained by a predefined KG schema, most existing approaches rely on Wikidata for entities and relation extraction. As a result, they often lack the flexibility to adapt to other KGs without prior retraining and costly data annotation. In this paper, to address these limitations, we propose FlexCIE, an approach for cIE which leverages Large Language Models (LLMs) and a KG completeness analysis tool. Given an input text, it identifies a list of entity mentions, links them to entities in the target KG using embedding techniques combined with LLMs, and constructs triples from these extracted entities using relevant properties retrieved from the KG. This enables the use of LLMs for cIE, while ensuring that the generated triples are accurate and compliant with the KG schema. Therefore, our work contributes to making cIE more practical and flexible, particularly for domain specific or enterprise KGs.

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.