24 citations · 24 across the 4 of their papers we have counts for
10 papers
Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition
Anand Panchbhai, Tommaso Soru, Edgard Marx
A booming amount of information is continuously added to the Internet as structured and unstructured data, feeding knowledge bases such as DBpedia and Wikidata with billions of sta…
Where is Linked Data in Question Answering over Linked Data?
Tommaso Soru, Edgard Marx, André Valdestilhas +3
We argue that "Question Answering with Knowledge Base" and "Question Answering over Linked Data" are currently two instances of the same problem, despite one explicitly declares to…
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…