Working Groups of KGA

KGA aims to advance the models, methods, and tools for SKG to achieve intra- and cross-domain data interoperability, reduce the complexity of SKG adoption, and help industries increase the maturity and readiness of their knowledge management practices. After the careful study of the OntoCommons Roadmap, KGA creates four different themes for grouping the scope and purpose of its broad agenda.

KGA Working groups are organized and governed by the Procedure Manual (work in progress), which will be edited by the steering committee set up during the first general assembly (Article V.1. § 2). The steering committee, which is formed by the Executive Council (Article IV.5 § 9), will meet yearly two times to form new working groups, assign tasks or modify the structure of working groups. Working groups report to the Executive Council, primarily to the CTO while keeping the CEO in the loop, who then reports the summary of the progress of the WGs in the ordinary general assembly (Article IV.4 § 22).

The Steering Committee forms new working groups based on the applications for WGs received.

KGA hosts several working groups in the service of SKG as a medium to enable intra- and cross-domain data interoperability. Currently, the working groups are classified under four different themes as described below.

Description of KGA Working Groups Themes

With the burgeoning interest in knowledge graphs, standardization activities for semantic models, methodologies and tools are being undertaken by international standardization bodies (e.g., ISO, IEEE), and public and private organizations. The tasks under this theme will aim at:

  1. review and categorize standards related to semantic knowledge graph-related models, methods, and tools across diverse domains, including information science, cloud computing, communication, IoT, multimedia, manufacturing, robotics, smart cities, energy, construction, bioscience, and agriculture.
  2. conduct comprehensive gap analyses to identify areas where existing SKG models, methods and tools may need enhancement or where new ontologies are required to address emerging challenges in the specified domains.
  3. Develop a roadmap for future development of SKG models, methods and tools, focusing on demand forecast and policy proposals, guiding standardization efforts, and ensuring the long-term sustainability of recognized outcomes to benefit diverse communities and sectors.

Examples of such tasks may include continuous observations on the new efforts and outcomes related to semantics and knowledge engineering by organizations such as IOF, EMMC, PCA, SAREF-ETSI, AIOTI, OneM2M; survey of new steering committee and release of SKG standards by ISO, IEEE, IEC, and other standardization bodies, landscape survey and comparative analysis of SKG tools and techniques, including commercial graph database, knowledge editors, and supporting tools for various knowledge engineering activities), state of the art initiatives and international projects that involves SKG, such as IDSA, GAIA-X, Catena-X, AMI 2030, and Open Knowledge Network, and marketplaces.

The tasks under this theme should publish reports on landscape surveys, roadmap, forecast, and analysis of standards in SKG models, methods, tools, and applications periodically. The roadmaps provided by the tasks of this theme will help in decision-making in the verticals of research, product, business, education and service.

This theme aims to address the pressing need for formalizing upper-level knowledge models (ontology, taxonomy, vocabulary) using a rich semantic-based framework that is easily comprehensible by human agents and machines alike. Focused on industrial innovation through multi-disciplinary interactions, the tasks under this theme should target to enhance the accuracy and coverage of the existing and new reference ontologies. The tasks will also endeavour to enhance the interoperability among existing upper or reference-level Ontologies by overcoming barriers such as a lack of cross-disciplinary understanding and the absence of professional tools for managing upper-level or domain reference ontologies. Additionally, the tasks will be undertaken to charter clear adoption routes of rich semantic-based semantic models in a scalable approach and promote communication between stakeholders in a multi-disciplinary environment by bringing together diverse expertise from philosophy, formal ontology, computer science, applied science, engineering, and industrial sectors. Some of the concrete tasks that may be performed under this theme include:

  1. Alignment, enrichment, and quality enhancement of existing top-level, mid-level and domain reference ontologies.
  2. Development of new domain reference ontologies for various disciplines, subject areas, and industrial sectors.
  3. Building strategy for maintenance and sustainability measures for the interoperability among evolving landscapes of semantic models.
  4. Research on tools and methods for making the adoption of upper-level rich semantic models in practical applications and other AI tools more convenient and scalable.
  5. Preparation of best practices for developing upper-level and reference semantic models, awareness campaign on its potential benefit in transforming industrial data management, and training materials for adoption and use of popular reference models in different industries.

The tasks under this theme should publish reports on landscape surveys, roadmap, forecast, and analysis of standards in SKG models, methods, tools, and applications periodically. The roadmaps provided by the tasks of this theme will help in decision-making in the verticals of research, product, business, education and service.

This theme focuses on the harmonization of methods and tools associated with Semantic Knowledge Graphs (SKG) to address prevalent challenges in the practical use of semantic models and knowledge graphs within industries. The overarching scope includes not only standardizing the ontology engineering lifecycle, covering activities such as requirement specifications, implementation, publication, maintenance, and usage of semantic models but also providing standardized guidelines to enterprises to evaluate their maturity in knowledge management, ensuring FAIRness of their data, and create a roadmap for enterprise knowledge strategies, including risk management, profit translation, ROI estimation for SKG projects. The goal is to create a comprehensive and integrated environment that streamlines the workflow associated with semantic knowledge graph management and builds confidence in companies regarding the adoption of SKG in their operations.

A key component of the project also involves developing robust toolkits that span all stages of the knowledge engineering lifecycle, especially for the functions lacking support of tools in the current landscape, such as concept identification, constraint specification, test specification, visual drafting, ontology navigation, and visualization.  In addition, the project will evaluate existing ontology editors, triple stores, and data transformers, with a focus on integrating these tools. The aim is to bridge existing gaps in tool support, fostering a more cohesive ecosystem for managing semantic models. This integrated approach seeks to overcome the current challenges associated with tool fragmentation and lack of benchmarking, providing users with a clearer understanding of tool capabilities, and aiding in their selection process.

The working groups primarily aim to assist domain experts and industrial stakeholders in navigating the intricacies of semantic-based ontology models, providing methodological and tool support for complex tasks of data transformation, consistency checking, and querying in diverse and complex languages, thereby empowering users to effectively utilize semantic knowledge graphs in their operations with confidence by reducing the learning curve.

Some of the concrete tasks may include:

  1. Standardization of SKG lifecycle methodology, e.g., LOT4OCES, CFIHOS etc.
  2. Methods and tools for management and evaluation of FAIR SKG for industries, e.g., IndustryPortal, O’Faire, FAIR-Explorer, Foops!. 
  3. Evaluation of SKG artifacts and organizational maturity in knowledge management.
  4. SKG engineering toolkits. e.g., SKG editors, data transformer, SKG lifecycle toolchain, triple stores with integrated query engines, consistency checkers, reasoners, and various linked data services (mapping, repair, automation, analytics).
  5. Enterprise knowledge strategies, including risk assessment, profit translation, and ROI estimation for SKG projects.

The outcome of the working groups under this theme will generate concrete products for KGA to enhance its business and service.

This theme is driven by the aspiration to propel the applications of Semantic Knowledge Graphs (SKG) to new heights while delving into unexplored realms within emerging technologies. The overarching scope encompasses a holistic knowledge-driven transformation across business, environment, and society. Within organizations, the project aims to enhance the interoperability of data by optimizing SKG applications, facilitating the seamless exchange and utilization of information, e.g., exploring the potential of enterprise knowledge graph as a Single Source of Truth (SSOT), and ensuring centralized and comprehensive knowledge management.

Apart from the primary purpose of SKG, the exploitation of SKG extends into various emerging technologies, including Digital Twin/Thread, Product Passport, Asset Administration Shell, and others. The goal is to leverage SKG to its fullest potential in areas such as the Internet of Things (IoT), Cyber-Physical Systems, Hybrid AI, Explainable AI, Generative AI, Model-Based Engineering, and Large Language Models. Moreover, the theme recognizes the need to address industry-specific challenges. By deploying SKG, it aims to tackle issues related to poor inventory and supply chain management, the absence of robust demand forecasting, the growing Return on Investment (ROI) in automation, labour shortages, shifting consumer attitudes, material scarcity, risk consolidation, and environmental concerns.

Beyond these challenges, the project seeks to showcase compelling and inspiring use cases of SKG applications across diverse industrial sectors. The intention is to illuminate the practical benefits and transformative potential of SKG, fostering a deeper understanding and appreciation for its role in driving positive change.

In essence, the purpose of this project is to be a catalyst for knowledge-driven transformation. By advancing SKG applications, exploring emerging technologies, addressing industry challenges, and showcasing real-world use cases, the project aims to contribute to a future where knowledge is a dynamic force, propelling innovation, sustainability, and positive societal impact.

Although the working groups under this theme may delve into extremely diverse topics, some of the example tasks may include:

  1. Role of SKG in the establishment of enterprise knowledge graph as Single Source of Truth (SSoT).
  2. Ontology and knowledge graph curation using large language model, NLP, and deep learning techniques.
  3. Use of SKG in hybrid AI (neurosymbolic system) for augmented data retrieval, analysis of multi-modal data, and explainability based on reasoning over fuzzy, real-valued, interval, and temporal logics.

Role of SKG in modelling digital twin/thread, model-based engineering, Product Passport, and Asset Administration Shell for solving specific industrial problems.

For any other information regarding working groups, please contact