SousLeSens – a Comprehensive Suite for the Industrial Practice of Semantic Knowledge Graphs

Over recent decades, the advancement of semantic web technologies has underscored the increasing importance of tools dedicated to developing and managing the foundational components of the semantic web stack. Addressing the evolving needs, a variety of tools have emerged from the research and development projects from academia as well as commercial software vendors. These tools offer a diverse range of services tailored to the management of various aspects of semantic knowledge graphs. Despite this proliferation, feedback from stakeholders involved in public and privately funded projects has highlighted notable shortcomings in existing tools. These gaps become evident in two key areas: firstly, the user experience struggles to scale up to meet industriallevel data practices and knowledge engineering methodologies. Secondly, a lack of interoperability and compatibility among the existing taskspecific tools leads to elevated costs and efforts. This paper introduces a novel semantic knowledge management ecosystem embodied in a suite of tools collectively known as ’SousLeSens’. Unlike its counterparts, SLS not only provides comprehensive coverage of typical knowledge engineering tasks while adhering to best practices for ensuring quality but also boasts a purely visual (no to minimum-code) interface. This feature is particularly well-suited for handling large-scale, industry-grade semantic data models. The paper delves into the establishment of requirements for knowledge engineering tools and services, derived from recent stakeholder surveys. It proceeds to present the SLS toolkit, elucidating its architecture and operational protocols. Finally, the paper validates the toolkit’s capabilities by comparing it with existing tools against predefined requirements and illustrating various use cases.