TY - GEN
T1 - Personalized page rank on knowledge graphs
T2 - 23rd International Conference on Extending Database Technology, EDBT 2020
AU - Gallo, Denis
AU - Lissandrini, Matteo
AU - Velegrakis, Yannis
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Graphs are everywhere. Personalized Page Rank (PPR) is a particularly important task to support search and exploration within such datasets. PPR computes the proximity between query nodes and other nodes in the graph. This is used, among others, for entity exploration, query expansion, and product recommendation. Graph databases are used for storing knowledge graphs. Unfortunately, the exact computation of PPR is computationally expensive. While different solutions have been proposed to compute PPR values with high precision, these are extremely complex to implement, and in some cases require heavy preprocessing. In this work, we sustain that a better approach exists: particle filtering. Particle filtering methods produce ranks with sufficient precision while exploiting what graph databases architectures are already optimized for: navigating local connections. We present the implementation of such an approach in a popular commercial database and show how this outperforms the already implemented functionality. With this, we aim to motivate future research to optimize and improve upon this research direction.
AB - Graphs are everywhere. Personalized Page Rank (PPR) is a particularly important task to support search and exploration within such datasets. PPR computes the proximity between query nodes and other nodes in the graph. This is used, among others, for entity exploration, query expansion, and product recommendation. Graph databases are used for storing knowledge graphs. Unfortunately, the exact computation of PPR is computationally expensive. While different solutions have been proposed to compute PPR values with high precision, these are extremely complex to implement, and in some cases require heavy preprocessing. In this work, we sustain that a better approach exists: particle filtering. Particle filtering methods produce ranks with sufficient precision while exploiting what graph databases architectures are already optimized for: navigating local connections. We present the implementation of such an approach in a popular commercial database and show how this outperforms the already implemented functionality. With this, we aim to motivate future research to optimize and improve upon this research direction.
UR - http://www.scopus.com/inward/record.url?scp=85084184941&partnerID=8YFLogxK
U2 - 10.5441/002/edbt.2020.54
DO - 10.5441/002/edbt.2020.54
M3 - Article in proceeding
AN - SCOPUS:85084184941
T3 - Advances in Database Technology - EDBT
SP - 447
EP - 450
BT - Advances in Database Technology - EDBT 2020
A2 - Bonifati, Angela
A2 - Zhou, Yongluan
A2 - Vaz Salles, Marcos Antonio
A2 - Bohm, Alexander
A2 - Olteanu, Dan
A2 - Fletcher, George
A2 - Khan, Arijit
A2 - Yang, Bin
PB - OpenProceedings.org
Y2 - 30 March 2020 through 2 April 2020
ER -