75 citations · 157 across the 11 of their papers we have counts for
11 papers
Hakken: Predicting future discoveries to fill the gaps in today's knowledge
Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez +21
We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e., growing scientific knowledge by establishing novel relationships, ones…
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Autoregressive Models for Knowledge Graph Generation
Thiviyan Thanapalasingam, Antonis Vozikis, Peter Bloem +1
Knowledge Graph (KG) generation requires models to learn complex semantic dependencies between triples while maintaining domain validity constraints. Unlike link prediction, which…
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Daniel Daza, Emile van Krieken +2
Graph neural networks (GNNs) learn to represent nodes by aggregating information from their neighbors. As GNNs increase in depth, their receptive field grows exponentially, leading…
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem +1
Knowledge Graph Embedding (KGE) models are used to learn continuous representations of entities and relations. A key task in the literature is predicting missing links between enti…
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak +2
We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perfo…
Prompting as Probing: Using Language Models for Knowledge Base Construction
Dimitrios Alivanistos, Selene Báez Santamaría, Michael Cochez +3
Language Models (LMs) have proven to be useful in various downstream applications, such as summarisation, translation, question answering and text classification. LMs are becoming…
Relational Graph Convolutional Networks: A Closer Look
Thiviyan Thanapalasingam, Lucas van Berkel, Peter Bloem +1
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction…
The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles
Angelo A. Salatino, Francesco Osborne, Thiviyan Thanapalasingam +1
Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety o…
Ontology-Based Recommendation of Editorial Products
Thiviyan Thanapalasingam, Francesco Osborne, Aliaksandr Birukou +1
Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that…
Linked Open Data Validity -- A Technical Report from ISWS 2018
Tayeb Abderrahmani Ghor, Esha Agrawal, Mehwish Alam +68
Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially a…