arXiv subjects

Fajar J. Ekaputra

Publications and source records attributed to Fajar J. Ekaputra.

5 recordsLinked to original sources

arXiv2026

A Semantic Encoding of Object Centric Event Data

The Object-Centric Event Data (OCED) is a novel meta-model aimed at providing a common ground for process data records centered around events and objects. One of its objectives is to foster interoperability and process information exchange. In this context, the integration of data from different providers, the combination of multiple processes, and the enhancement of knowledge inference are novel challenges. Semantic Web technologies can enable the creation of a machine-readable OCED description enriched through ontology-based relationships and entity categorization. In this paper, we introduce an approach built upon Semantic Web technologies for the realization of semantic-enhanced OCED, with the aim to strengthen process data reasoning, interconnect information sources, and boost expressiveness.

cs.IR↗

arXiv2025

SigSPARQL: Signals as a First-Class Citizen When Querying Knowledge Graphs

Purpose: Cyber-Physical Systems (CPSs) integrate computation and physical processes, producing time series data from thousands of sensors. Knowledge graphs can contextualize these data, yet current approaches that are applicably to monitoring CPS rely on observation-based approaches. This limits the ability to express computations on sensor data, especially when no assumptions can be made about sampling synchronicity or sampling rates. Methodology: We propose an approach for integrating knowledge graphs with signals that model run-time sensor data as functions from time to data. To demonstrate this approach, we introduce SigSPARQL, a query language that can combine RDF data and signals. We assess its technical feasibility with a prototype and demonstrate its use in a typical CPS monitoring use case. Findings: Our approach enables queries to combine graph-based knowledge with signals, overcoming some key limits of observation-based methods. The developed prototype successfully demonstrated feasibility and applicability. Value: This work presents a query-based approach for CPS monitoring that integrates knowledge graphs and signals, alleviating problems of observation-based approaches. By leveraging system knowledge, it enables operators to run a single query across different system instances within the same domain. Future work will extend SigSPARQL with additional signal functions and evaluate it in large-scale CPS deployments.

cs.DB↗

arXiv2024

AuditMAI: Towards An Infrastructure for Continuous AI Auditing

Artificial Intelligence (AI) Auditability is a core requirement for achieving responsible AI system design. However, it is not yet a prominent design feature in current applications. Existing AI auditing tools typically lack integration features and remain as isolated approaches. This results in manual, high-effort, and mostly one-off AI audits, necessitating alternative methods. Inspired by other domains such as finance, continuous AI auditing is a promising direction to conduct regular assessments of AI systems. The issue remains, however, since the methods for continuous AI auditing are not mature yet at the moment. To address this gap, we propose the Auditability Method for AI (AuditMAI), which is intended as a blueprint for an infrastructure towards continuous AI auditing. For this purpose, we first clarified the definition of AI auditability based on literature. Secondly, we derived requirements from two industrial use cases for continuous AI auditing tool support. Finally, we developed AuditMAI and discussed its elements as a blueprint for a continuous AI auditability infrastructure.

cs.CY↗

arXiv2024

A maturity model for catalogues of semantic artefacts

This work presents a maturity model for assessing catalogues of semantic artefacts, one of the keystones that permit semantic interoperability of systems. We defined the dimensions and related features to include in the maturity model by analysing the current literature and existing catalogues of semantic artefacts provided by experts. In addition, we assessed 26 different catalogues to demonstrate the effectiveness of the maturity model, which includes 12 different dimensions (Metadata, Openness, Quality, Availability, Statistics, PID, Governance, Community, Sustainability, Technology, Transparency, and Assessment) and 43 related features (or sub-criteria) associated with these dimensions. Such a maturity model is one of the first attempts to provide recommendations for governance and processes for preserving and maintaining semantic artefacts and helps assess/address interoperability challenges.

cs.DL↗

arXiv2023

Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community - Semantic Web Machine Learning (SWeML for short). Due to its rapid growth and impact on several communities in the last two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the last decade in this area, where we focused on evaluating architectural, and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this paper is a classification system for SWeML Systems which we publish as ontology.

cs.AI↗