A5008611990- Semantic Web and Ontologies
- Rough Sets and Fuzzy Logic
- Natural Language Processing Techniques
- Biomedical Text Mining and Ontologies
- Data Management and Algorithms
- Data Mining Algorithms and Applications
- Advanced Database Systems and Queries
- Sphingolipid Metabolism and Signaling
- Service-Oriented Architecture and Web Services
- Advanced Graph Neural Networks
- Topic Modeling
- Data Quality and Management
- Bayesian Modeling and Causal Inference
- Cytokine Signaling Pathways and Interactions
- PI3K/AKT/mTOR signaling in cancer
- Neural Networks and Applications
- ATP Synthase and ATPases Research
- Evolutionary Algorithms and Applications
- Logic, Reasoning, and Knowledge
- Advanced Clustering Algorithms Research
- Multi-Agent Systems and Negotiation
- Scientific Computing and Data Management
- Time Series Analysis and Forecasting
- Complex Network Analysis Techniques
- Data Stream Mining Techniques
University of Bari Aldo Moro
2015-2024
Polytechnic University of Bari
2020
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry academia. Knowledge graphs are founded on the
The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports collaborative effort performed by ten teams of students, each guided senior researcher as their mentor, attending ISWS 2023. Each team provided different perspective topic creative AI, substantiated set research questions main subject investigation. 2023 edition focuses on intersection technologies and Creative AI. explored various...
This work presents a dissimilarity measure for Description Logics that are the theoretical counterpart of standard representations ontological knowledge. The focus is on definition ALC concept descriptions, based both syntax and semantics descriptions. An extension proposed involving individuals then evaluating their dissimilarity.
A totally semantic measure is presented which able to calculate a similarity value between concept descriptions and also description individual or individuals expressed in an expressive logic. It applicable on symbolic although it uses numeric approach for the calculus. Considering that Description Logics stand as theoretic framework ontological knowledge representation reasoning, proposed can be effectively used agglomerative divisional clustering task applied web domain.
Nowadays, building ontologies is a time consuming task since they are mainly manually built. This makes hard the full realization of Semantic Web view. In order to overcome this issue, machine learning techniques, and specifically inductive learn
Machine Learning methods have been introduced in the Semantic Web for solving problems such as link and type prediction, ontology enrichment completion (both at terminological assertional level). Whilst initially mainly focussing on symbol-based solutions, recently numeric-based approaches received major attention, motivated by need to scale very large of Data. In this paper, most representative proposals, belonging aforementioned categories are surveyed, jointly with analysis their main...
In the Semantic Web context, OWL ontologies represent conceptualization of domains interest while corresponding assertional knowledge is given by heterogeneous resources referring to them. Being strongly decoupled, and assertion can be out-of-sync. An ontology incomplete, noisy sometimes inconsistent with regard actual usage its conceptual vocabulary in assertions. Data mining support discovery hidden patterns data, enrich ontologies. We present a method for discovering multi-relational...
Knowledge Graphs (KGs) are a widely used formalism for representing knowledge in the Web of Data. We focus on problem link prediction, i.e. predicting missing links large graphs, so to discover new facts about world. Representation learning models that embed entities and relation types continuous vector spaces recently were achieve state-of-the-art prediction results. A limiting factor these is process optimal embedding vectors can be really time-consuming, might even require days...