Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.
YOU MAY ALSO LIKE:
- Mining and Searching text with Graph Databases (SkillsCast recorded in August 2016)
- ML Pipelines from the Get Go (Without Tears) (SkillsCast recorded in August 2022)
- Keynote — Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation (SkillsCast recorded in November 2021)
Graph-Powered Machine Learning
Dr. Alessandro Negro
Alessandro has been a long-time member of the graph community and he is the main author of the first-ever recommendation engine based on Neo4j. At GraphAware, he specialises in recommendation engines, graph-aided search, and NLP. He has recently built an application using Neo4j and Elasticsearch aimed at personalising search results, utilizing several machine learning algorithms, natural language processing and ontology hierarchy. Before joining the team, Alessandro has gained over 10 years of experience in software development and spoke at many prominent conferences, such as JavaOne. Alessandro holds a Ph.D. in Computer Science from University of Salento.