Marco Antonio Casanova

arXiv2026

Publication and Maintenance of Relational Data in Enterprise Knowledge Graphs (Revised Version)

Enterprise knowledge graphs (EKGa) are a novel paradigm for consolidating and semantically integrating large numbers of heterogeneous data sources into a comprehensive dataspace. The main goal of an EKG is to provide a data layer that is semantically connected to enterprise data, so that applications can have integrated access to enterprise data sources through that semantic layer. To make legacy relational data sources accessible through the organization's knowledge graph, it is necessary to create an RDF view of the underlying relational data (RDB2RDF view). An RDB2RDF view can be materialized to improve query performance and data availability. However, a materialized RDB2RDF view must be continuously maintained to reflect updates over the relational database. This article proposes a formal framework for constructing the materialized data graph for an RDB2RDF view and for incrementally maintaining the view's data graph. The article also presents an architecture and algorithms for implementing the proposed framework.

cs.DB↗

arXiv2018

Computing Entity Semantic Similarity by Features Ranking

This article presents a novel approach to estimate semantic entity similarity using entity features available as Linked Data. The key idea is to exploit ranked lists of features, extracted from Linked Data sources, as a representation of the entities to be compared. The similarity between two entities is then estimated by comparing their ranked lists of features. The article describes experiments with museum data from DBpedia, with datasets from a LOD catalog, and with computer science conferences from the DBLP repository. The experiments demonstrate that entity similarity, computed using ranked lists of features, achieves better accuracy than state-of-the-art measures.

cs.IR↗