A5056690478- Semantic Web and Ontologies
- Data Quality and Management
- Advanced Database Systems and Queries
- Biomedical Text Mining and Ontologies
- Service-Oriented Architecture and Web Services
- Scientific Computing and Data Management
- Advanced Graph Neural Networks
- Graph Theory and Algorithms
- Topic Modeling
- Cognitive Computing and Networks
- Natural Language Processing Techniques
- Data Management and Algorithms
- Web Data Mining and Analysis
- Geographic Information Systems Studies
- Privacy-Preserving Technologies in Data
- Wikis in Education and Collaboration
- Blockchain Technology Applications and Security
- Rough Sets and Fuzzy Logic
- Data Mining Algorithms and Applications
- IoT and Edge/Fog Computing
- Advanced Text Analysis Techniques
- Privacy, Security, and Data Protection
- Big Data Technologies and Applications
- Machine Learning and Data Classification
- Advanced Computational Techniques and Applications
Trinity College Dublin
2019-2024
Huawei Technologies (United Kingdom)
2024
Institut national de recherche en informatique et en automatique
2016-2022
Université Côte d'Azur
2021-2022
Centre National de la Recherche Scientifique
2021-2022
Research Centre Inria Sophia Antipolis - Méditerranée
2022
Fraunhofer Society
2020
Science Foundation Ireland
2020
Fraunhofer Institute for Intelligent Analysis and Information Systems
2018-2019
Webb Institute
2016-2017
Large Language Models (LLMs) have taken Knowledge Representation -- and the world by storm. This inflection point marks a shift from explicit knowledge representation to renewed focus on hybrid of both parametric knowledge. In this position paper, we will discuss some common debate points within community LLMs (parametric knowledge) Graphs (explicit speculate opportunities visions that brings, as well related research topics challenges.
RDF reification is a data modelling solution for writing statements about statements. A set of approaches exist in the literature to express statement-level metadata, or "reified" statements, RDF. They have primarily been designed attach additional contextual information individual triples, such as provenance, spatio-temporal validity, certainty. Practically, different methods developed present metadata while complying with standard and syntax. However, when effectively stored triplestores,...
The recent achievements and availability of Large Language Models have paved the road to a new range applications use-cases. Pre-trained language models are now being involved at-scale in many fields where they were until absent from. More specifically, progress made by causal generative has open door using them through textual instructions aka. prompts. Unfortunately, performances these prompts highly dependent on exact phrasing used therefore practitioners need adopt fail-retry strategies....
Knowledge graphs are dynamic in nature, new facts about an entity added or removed over time. Therefore, multiple versions of the same knowledge graph exist, each which represents a snapshot at some point Entities within undergo evolution as removed. The problem automatically generating summary out different is long-studied problem. However, most existing approaches limited to pairwise version comparison. This limitation makes it difficult capture complete several graph. To overcome this...
Whereas the availability of data has seen a manyfold increase in past years, its value can be only shown if variety is effectively tackled ---one prominent Big Data challenges. The lack interoperability limits potential collective use for novel applications. Achieving through full transformation and integration diverse structures remains an ideal that hard, not impossible, to achieve. Instead, methods simultaneously interpret different types available formats have been explored. On other...
Squerall is a tool that allows the querying of heterogeneous, large-scale data sources by leveraging state-of-the-art Big Data processing engines: Spark and Presto. Queries are posed on-demand against Lake, i.e., directly on original without requiring prior transformation. We showcase Squerall's ability to query five different sources, including inter alia popular Cassandra MongoDB. In particular, we demonstrate how it can jointly heterogeneous interested developers easily extend support...
Increasing data volumes have extensively increased application possibilities. However, accessing this in an ad hoc manner remains unsolved problem due to the diversity of management approaches, formats and storage frameworks, resulting need effectively access process distributed heterogeneous at scale. For years, Semantic Web techniques addressed integration challenges with practical knowledge representation models ontology-based mappings. Leveraging these techniques, we provide a solution...
SPARQL is the standard language for querying RDF data. There exists a variety of query evaluation systems implementing different architectures distribution data and computations. Differences in coupled with specific optimizations, e.g. preprocessing indexing, make these incomparable from purely theoretical perspective. This results many implementations solving problem while exhibiting very behaviors, not all them being adapted any context. We provide new perspective on distributed...
Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable prediction of missing links inside graphs. More specifically, latent distance approaches model relationships among entities via a between representations. Translating embedding (e.g., TransE) are most which use one function to learn multiple relation patterns. However, they mostly inefficient in symmetric relations since representation vector norm all becomes...
Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the development of efficient tools for allowing end users to extract useful insights from these sources knowledge. In such a context, reducing size Resource Description Framework (RDF) graph while preserving all information can speed up query engines by limiting data shuffle, especially in distributed setting. This paper presents two algorithms RDF summarization: Grouping Based Summarization (GBS) and Query (QBS). The...