Multi-layer ontology based information fusion for situation awareness

Pai, Fang-Ping; Yang, Lee-Jang; Chung, Yeh‐Ching

doi:10.1007/s10489-016-0834-7

Cited by 22 publications

(11 citation statements)

References 29 publications

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“…Reference [28] proposed an improved reinforcement learning algorithm, which added human action to boost performance. In [29] , a multi-layer ontology was built for information fusion, which is a topdown method for data fusion.…”

Section: Data Fution Of Semantics and Ontologymentioning

confidence: 99%

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

Chen

Wang

et al. 2020

IEEE Access

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With the development of industrial Internet and artificial intelligence, data fusion in crossdomains and cross-layers have become an inevitable trend. Most of the data fusion involved in the production process of hot rolling are concentrated on the level of sensors, Internet of Things (IoT) and the Internet; but human data are not well integrated. In order to avoid the human factor from becoming the bottleneck of the entire production schedule, this paper proposes a ternary data fusion model based on reinforcement learning algorithm. The related data source from human-cyber-physical space includes: social network, Internet and IoT. By merging the ternary data, a variety of data (including humans') can be quickly calculated to obtain better and faster decisions. In order to achieve automated fusion from ternary data, this paper proposes a method based on reinforcement learning: firstly, the domain ontology used for associating ternary data is reduced and tessellated (dimension reduction), and then the reinforcement learning model is used to form "the new ontology". Compared with resource-intensive global calculations (which may cost a few days), the new method can complete the calculations in minutes. This means that the new method optimizes the data source required for decision-making and improves the efficiency. Finally, the production scheduling of hot rolled steel is used as an example to verify the feasibility of the proposed method. INDEX TERMS human-cyber-physical data fusion, ternary data fusion, cyber-physical system, domain ontology, reinforcement learning

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Section: Data Fution Of Semantics and Ontologymentioning

confidence: 99%

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

Chen

Wang

et al. 2020

IEEE Access

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“…Due to the continuity of material data, certain data ranges and heterogeneity, it is preliminarily judged that CRH (Conflict Resolution on Heterogeneous data) [23] may be a suitable framework for material data. Other researchers proposed to perform material data integration based upon semantic recognition [24,25] and ontology technologies [26], such as lexical semantic similarity calculation, ontology alignment, semantics recognition with models like CRF (Conditional Random Field), and rule-based ontology matching. Data integration with semantic-index-enabled knowledge bases was also proposed in the literature [27].…”

Section: Integration Of Big Materials Datamentioning

confidence: 99%

Information Fusion for Multi-Source Material Data: Progress and Challenges

2019

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The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion.

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“…To assist humans to analyse data, new techniques are needed to automatically fuse and reason over the data. Ontology based information fusion [1] is a promising approach for automatically integrating and reasoning about heterogeneous information sources to support situational awareness. This involves constructing a knowledge graph (expressed using concepts from an ontology) describing known information about a scenario, linking it to other knowledge graphs of complementary information, performing inference to derive new facts, and then querying the results for high-level information of interest.…”

Section: Introductionmentioning

confidence: 99%

Signal Knowledge Graph

Simmons¹,

Vasa²

2022

Preprint

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This paper presents an knowledge graph to assist in reasoning over signals for intelligence purposes. We highlight limitations of existing knowledge graphs and reasoning systems for this purpose, using inference of an attack using combined data from microphones, cameras and social media as an example. Rather than acting directly on the received signal, our approach considers attacker behaviour, signal emission, receiver characteristics, and how signals are summarised to support inferring the underlying cause of the signal.

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Multi-layer ontology based information fusion for situation awareness

Cited by 22 publications

References 29 publications

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

Information Fusion for Multi-Source Material Data: Progress and Challenges

Signal Knowledge Graph

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