CHEKG: a collaborative and hybrid methodology for engineering modular and fair domain-specific knowledge graphs

Abstract

AbstractOntologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios.