24 citations · 72 across the 12 of their papers we have counts for
5 papers · 1 filter
ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz +5
The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing an…
Neural Machine Translation for Query Construction and Composition
Tommaso Soru, Edgard Marx, André Valdestilhas +3
Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural mach…
Expeditious Generation of Knowledge Graph Embeddings
Tommaso Soru, Stefano Ruberto, Diego Moussallem +4
Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddi…
Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts
Faisal Alshargi, Saeedeh Shekarpour, Tommaso Soru +1
Although there is an emerging trend towards generating embeddings for primarily unstructured data and, recently, for structured data, no systematic suite for measuring the quality…
Beyond Markov Logic: Efficient Mining of Prediction Rules in Large Graphs
Tommaso Soru, André Valdestilhas, Edgard Marx +1
Graph representations of large knowledge bases may comprise billions of edges. Usually built upon human-generated ontologies, several knowledge bases do not feature declared ontolo…