# A Semantic Web Technology Index

arXiv: https://arxiv.org/abs/2201.07034 · HTML: https://mlsanity.com/paper/2201.07034/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 64 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (2020)
- [Improving Knowledge Graph Embedding Using Simple Constraints](https://mlsanity.com/paper/1805.02408/) (2018)
- [An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice](https://mlsanity.com/paper/2003.00344/) (2020)
- [Efficient Knowledge Graph Accuracy Evaluation](https://mlsanity.com/paper/1907.09657/) (2019)
- [Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph](https://mlsanity.com/paper/1910.01116/) (2019)
- [FOCA: A Methodology for Ontology Evaluation](https://mlsanity.com/paper/1612.03353/) (2016)
- [An Ontology-based Context Model in Intelligent Environments](https://mlsanity.com/paper/2003.05055/) (2020)

## Built on by (most cited first, 16 of 16)

- [Intelligent Perception System for Vehicle-Road Cooperation](https://mlsanity.com/paper/2208.14052/) (2022)
- [Exploration of the Assessment for AVP Algorithm Training in Underground Parking Garages Simulation Scenario](https://mlsanity.com/paper/2311.08410/) (2023)
- [Active Data Acquisition in Autonomous Driving Simulation](https://mlsanity.com/paper/2306.13923/) (2023)
- [Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey](https://mlsanity.com/paper/2311.06043/) (2023)
- [Automatic parking planning control method based on improved A* algorithm](https://mlsanity.com/paper/2406.15429/) (2024)
- [Vehicle-road Cooperative Simulation and 3D Visualization System](https://mlsanity.com/paper/2208.07304/) (2022)
- [Development of Real-time Rendering Technology for High-Precision Models in Autonomous Driving](https://mlsanity.com/paper/2302.00291/) (2023)
- [PCG-based Static Underground Garage Scenario Generation](https://mlsanity.com/paper/2307.03988/) (2023)
- [Real-time pedestrian recognition on low computational resources](https://mlsanity.com/paper/2309.01353/) (2023)
- [Standardizing Knowledge Engineering Practices with a Reference Architecture](https://mlsanity.com/paper/2404.03624/) (2024)
- [Automated Parking Planning with Vision-Based BEV Approach](https://mlsanity.com/paper/2406.15430/) (2024)
- [Acceleration method for generating perception failure scenarios based on editing Markov process](https://mlsanity.com/paper/2407.00980/) (2024)
- [Deep Reinforcement Learning for Adverse Garage Scenario Generation](https://mlsanity.com/paper/2407.01333/) (2024)
- [Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots](https://mlsanity.com/paper/2407.02197/) (2024)
- [Development of Occupancy Prediction Algorithm for Underground Parking Lots](https://mlsanity.com/paper/2409.00923/) (2024)
- [Manboformer: Learning Gaussian Representations via Spatial-temporal Attention Mechanism](https://mlsanity.com/paper/2503.04863/) (2025)

## Abstract (arXiv)

Semantic Web (SW) technology has been widely applied to many domains such as medicine, health care, finance, geology. At present, researchers mainly rely on their experience and preferences to develop and evaluate the work of SW technology. Although the general architecture (e.g., Tim Berners-Lee's Semantic Web Layer Cake) of SW technology was proposed many years ago and has been well-known, it still lacks a concrete guideline for standardizing the development of SW technology. In this paper, we propose an SW technology index to standardize the development for ensuring that the work of SW technology is designed well and to quantitatively evaluate the quality of the work in SW technology. This index consists of 10 criteria that quantify the quality as a score of 0 ~ 10. We address each criterion in detail for a clear explanation from three aspects: 1) what is the criterion? 2) why do we consider this criterion and 3) how do the current studies meet this criterion? Finally, we present the validation of this index by providing some examples of how to apply the index to the validation cases. We conclude that the index is a useful standard to guide and evaluate the work in SW technology.