Knowledge Graph aware Recommendation System with DBpedia - Alvaro Lopes
- Mentors
- Edgard Marx, Paulo do Carmo
- Organization
- DBpedia
- Technologies
- python, numpy, scikit-learn, sparql, pandas, YAML, NetworkX, DBpedia
- Topics
- framework, data integration, Recommender Systems, DBpedia, Entity Linking, Data Enriching, Knowledge-aware RS, Node embedding based RS
DBpedia proves to be a way of ”Bringing Order to the Web” with structured information
as Open Knowledge Graph, contributing towards making quality data more accessible.
The main goal of this project is to explore DBpedia’s potential at enriching the data
provided to Recommender Systems (RS) on different standard datasets, such as MovieLens
and LastFM. A framework, for running reproducible experiments with only the model
implementation and a simple .yaml configuration file, will be implemented. Through the
framework, this project allows practitioners to easily evaluate and compare their proposed
RS algorithms with existing approaches, enabling future benchmarks on enriched and
non-enriched datasets. With access to enriched standard RS datasets sourced primarily
from DBpedia, this project aims to demonstrate DBpedia’s applicability to RS area and
other areas of ML, potentially promoting the adoption of DBpedia and increasing its active
community. The main steps of the project are:
• Entity linking: between DBpedia and standard RS datasets.
• Data Enriching: Build SPARQL queries to enrich RS datasets with useful DBpedia‘s
properties.
• Framework Implementation: for reproducible experiments.