A High-Level Ontology Network for ICT Infrastructures

Abstract

The ICT infrastructures of medium and large organisations that offer ICT services (infrastructure, platforms, software, applications, etc.) are becoming increasingly complex. Nowadays, these environments combine all sorts of hardware (e.g., CPUs, GPUs, storage elements, network equipment) and software (e.g., virtual machines, servers, microservices, services, products, AI models). Tracking, understanding and acting upon all the data produced in the context of such environments is hence challenging. Configuration management databases have been so far widely used to store and provide access to relevant information and views on these components and on their relationships. However, different databases are organised according to different schemas. Despite existing efforts in standardising the main entities relevant for configuration management, there is not yet a core set of ontologies that describes these environments homogeneously, and which can be easily extended when new types of items appear. This paper presents an ontology network created with the purpose of serving as an initial step towards an homogeneous representation of this domain, and which has been already used to produce a knowledge graph for a large ICT company.

Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

“…transformations are executed using query languages like SPARQL 18 or Gremlin. 19 This analysis emphasizes the significance of this topic within both the database and semantic web communities. SDM-RDFizer also facilitates the transformation of data from diverse sources represented in various formats, including JSON, XML, CSV, or relational databases.…”

“…Consequently, these declarative specifications of KGs as RML DISs promote modularity, supporting maintenance, testing, debugging, and enabling collaborative definition and reusability. RML is currently being used by several companies and public organizations [19,20,50,60] to create their KGs, also including the Google Enterprise Knowledge Graph. 9 Fig.…”

Morph-KGC: Scalable knowledge graph materialization with mapping partitions

“…We have compiled information on mapping partitioning for several well-known benchmarks (namely, NPD [27], BSBM [2], GTFS-Madrid-Bench [6] and LSLOD [18]), the DevOps ICT knowledge graph [7], and other real uses cases from the KGC W3C Community Group 2 in Table 1. We have included whether all the predicate maps are constant-valued, so that the third condition in Definition 6 applies.…”