Periodic Reporting for period 1 - KnowGraphs (Knowledge Graphs at Scale)
Período documentado: 2019-10-01 hasta 2021-09-30
ESR 2 focuses on representation techniques for KGs that allow to query them efficiently. The technical challenge he faced is the design and use of a universal data model able to cater for the different instantiations of the KG paradigm. He was able to show that the 5D tensor commonly assumed for property graphs can be flattened to a 3D tensor with a significant improvement in the runtime of GraphQL queries.
ESR 3 focuses on the formal representation of constraints for reasoning and querying over (distributed) KGs. Her work aims at developing formal semantics for policy profiles using the ODRL Regulatory Compliance Profile.
ESR 4 works on Multilingual approaches to exploit and expand information in KGs and use them in downstream NLP tasks, such as Entity Linking and Relation Extraction. He has designed and published an autoregressive approach, based on BART model, to tackle the Relation Extraction task, obtaining remarkable results.
ESR 5 developed a new method for hyperparameter optimization for KG embeddings with factorial designs and developed a semi-Riemannian graph neural network for graph representation learning. He conducted research on box embeddings focussing on ontology embeddings in the EL++ description logic.
ESR 6’s research aims at the novel definition, parsing and exploitation of sentence-level KGs. He worked to define an enhanced version of AMR to obtain a truly semantic representation of sentences. The BMR work resulted in a “Blue Sky” paper, accepted to 2022 AAAI conference.
ESR 7 targets the development of methods able to exploit evidence within an input knowledge graph as well as evidence external to the KG (especially large text corpora) to compute the probability that a particular fact is true while circumventing feature engineering. His preliminary implementation already achieves results close to the state of the art.
ESR 8 focuses on the evolution/change of KGs within the scheme of graph embedding methods and FAIR. She is implementing automatic rewriting techniques for SPARQL queries that consider changes as well as entity alignment with graph embeddings on evolving KGs to capture the changes between versions of knowledge graphs.
ESR 9 carries out research on Data Provenance Models for Relational and Linked Data. Her research goals include the creation of a provenance-aware main memory database for the aforementioned provenance models and the redesign of the query evaluation algorithm of an existing RDF database that takes into account provenance information.
ESR 10 works on discrete knowledge graph embedding methods and carried out a state-of-the-art survey of binary and non-binary discrete methods. He is looking into using graph neural networks for improving the state-of-the-art in managing and querying polymorphic knowledge graphs.
ESR 11 tackles the problem of ante-hoc explainable machine learning (ML) on large KGs. He developed a new family of explainable ML techniques for KGs based concept synthesis, showing that simple implementations of this paradigm are more time-efficient and as effective as the state of the art.
ESR 12 scrutinizes the necessary features of a dynamic consent model that can reconcile the various rights and interests (individual and societal) related to the processing of personal health data for biomedical research using KGs. In his work on biomedical research, the author analyses consent withdrawal in processing personal data that is hardly ever discussed in literature.
ESR 13 studies the exploitation of KGs in the economic domain. She is preparing a paper on constructing a knowledge graph of crowdfunding business proposal data that can be used as a baseline for idea generation tasks for new product and service development and enhance human creativity. This core idea of her research was presented at the PhD Symposium of the 13th ACM Web Science Conference 2021.
ESR 14's work focuses on defining a methodology to generate metadata for KGs that enable their efficient discovery and reuse for use cases in medicine and finance. Her contributions include an in-depth analysis of methods for the (semi-) automated construction of metadata and an investigation into the tools and techniques used to analyse query logs.
ESR 15’s research pertains to whether data protection principles can be embedded into computer software so that the regulatory aims of data protection are achieved. He has submitted two articles: 1) The challenge of incorporating legal rules into digital applications (accepted) and 2) Data Protection by Design and by Default and the Certification Scheme of the GDPR.