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Found 13 papers, 2 papers with code
A Large Interlinked Knowledge Graph of the Italian Cultural Heritage
Knowledge is the lifeblood for a plethora of applications such as search, recommender systems and natural language understanding.
LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study
The recent advancements in Deep Learning (DL) research have notably influenced the finance sector.
Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis
Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades.
Agnostic Visual Recommendation Systems: Open Challenges and Future Directions
Visualization Recommendation Systems (VRSs) are a novel and challenging field of study aiming to help generate insightful visualizations from data and support non-expert users in information discovery.
AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes
The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database.
A network-based analysis of disease modules from a taxonomic perspective
Proximity relationships of disease modules (hereafter DMs) in the human interactome network are now increasingly used in diagnostics, to identify pathobiologically similar diseases and to support drug repurposing and discovery.
Multiple Knowledge GraphDB (MKGDB)
MKGDB, thanks the versatility of the Neo4j graph database manager technology, is intended to favour and help the development of open-domain natural language processing applications relying on knowledge bases, such as information extraction, hypernymy discovery, topic clustering, and others.
What to Write? A topic recommender for journalists
In this paper we present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest.
Hashtag Sense Clustering Based on Temporal Similarity
Hashtags are creative labels used in micro-blogs to characterize the topic of a message/discussion.
A New Method for Evaluating Automatically Learned Terminological Taxonomies
Our method assigns a similarity value B{\textasciicircum}i{\_}(l, r) to the learned (l) and reference (r) taxonomy for each cut i of the corresponding anonymised hierarchies, starting from the topmost nodes down to the leaf concepts.
