Contributo in atti di convegno, 2022, ENG

Towards unsupervised machine learning approaches for knowledge graphs

Minutella F.; Falchi F.; Manghi P.; De Bonis M.; Messina N.

Larus Business Automation, Mestre, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy

Nowadays, a lot of data is in the form of Knowledge Graphs aiming at representing information as a set of nodes and relationships between them. This paper proposes an efficient framework to create informative embeddings for node classification on large knowledge graphs. Such embeddings capture how a particular node of the graph interacts with his neighborhood and indicate if it is either isolated or part of a bigger clique. Since a homogeneous graph is necessary to perform this kind of analysis, the framework exploits the metapath approach to split the heterogeneous graph into multiple homogeneous graphs. The proposed pipeline includes an unsupervised attentive neural network to merge different metapaths and produce node embeddings suitable for classification. Preliminary experiments on the IMDb dataset demonstrate the validity of the proposed approach, which can defeat current state-of-the-art unsupervised methods.

IRCDL 2022 - 18th Italian Research Conference on Digital Libraries, Padua, Italy, 24-25/02/2022

Keywords

Knowledge graphs, Unsupervised machine learning, Neural network

CNR authors

De Bonis Michele, Messina Nicola, Manghi Paolo, Falchi Fabrizio

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 468960

Year: 2022

Type: Contributo in atti di convegno

Creation: 2022-07-06 15:18:02.000

Last update: 2022-11-04 10:35:44.000

External links

OAI-PMH: Dublin Core

OAI-PMH: Mods

OAI-PMH: RDF

URL: http://ceur-ws.org/Vol-3160/

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:468960

Scopus: 2-s2.0-85134256958