Recommending Semantic Concepts for Improving the Process of Semantic Modeling

Paulus, Alexander; Pomp, André; Poth, Lucian; Lipp, Johannes; Meisen, Tobias

doi:10.1007/978-3-030-26169-6_17

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…Second, consider the issue of semantic annotation or metadata. It (semantic annotation or metadata) has become a crucial issue due to the advent of web technology, and many authors have contributed toward semantic annotation or metadata [37][38][39][40][41][42][43][44][45]. The fact of the matter is that the new web technology called Semantic Web (SW) [46] is in the process of replacing its predecessor.…”

Section: Preparing Datasets Of Surface Roughness For Bdmentioning

confidence: 99%

Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

Fattahi

Okamoto

Ura

2021

BDCC

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In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study.

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Section: Preparing Datasets Of Surface Roughness For Bdmentioning

confidence: 99%

Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

Fattahi

Okamoto

Ura

2021

BDCC

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“…Some other authors (eg, consider the work in 48 ) reckon that both data-bases and knowledge-bases must populate the embedded systems, where the demarcation lines between "data" and "knowledge" are not drawn. Some authors consider that knowledge is semantically annotated data, [13][14][15] as already described in Section 1. Thus, in the literature of Industry 4.0, the concept of knowledge remains ambiguous.…”

Section: Engineering Design and Manufacturingmentioning

confidence: 99%

“…11 It is emphasized that the underlying knowledge management framework must incorporate some mechanisms that semantically classify engineering design and manufacturing knowledge. 12 Pomp et al 13 and Paulus et al 14 developed a system (ESKAPE) where the semantically annotated data structures (or knowledge graph) are utilized to deal with the computational complexity of knowledge representation and knowledge sharing within the constituents of Industry 4.0. Nevertheless, when a digitized engineering design and manufacturing knowledge management systems are constructed, the systems become domain specific and web technology specific.…”

mentioning

confidence: 99%

“…In general, knowledge makes a vast ecosystem integrating human learning, machine learning, logical inferences (deduction, induction, and abduction), experimental data, analytical results, simulations, algorithms, creative thinking, and cognitive reflections. In most case, data and knowledge are used interchangeably (eg, compare the articles 5,8,9,[13][14][15][16] ). The myriad proximal and distal interactions of knowledge among the abovementioned entities result in heavy computation.…”

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confidence: 99%

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What is knowledge in Industry 4.0?

Ullah

2020

Engineering Reports

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Industry 4.0 relevant systems (eg, cyber‐physical systems, digital twins, and alike) need digitized knowledge to function. Before digitizing knowledge, a fundamental question arises: What is knowledge? In order to answer this question, this study first reviews the definitions of knowledge found in the extant literature of epistemology, engineering design, manufacturing, organization science, information science, and education science. Since the definitions reported so far are not succinct and suffer circularity, this study overcomes this by introducing a three‐element‐based definition of knowledge—a piece knowledge consists of three elements defined as claim, provenance, and inference. This results in four types of knowledge defined as definitional, deductive, inductive, and creative knowledge, and each type of knowledge is again divided into some categories. Some real‐life scenarios relevant to engineering design and manufacturing are used to clarify the proposed knowledge types/categories; the relevant pieces of knowledge are represented by knowledge graphs (concept maps) for the sake of digitization. The myriad proximal and distal relationships between knowledge and other relevant entities (human/machine learning, logical inferences, experimental data, analytical results, creative thinking, and cognitive reflections) become succinct and transparent due to the proposed definition of knowledge. Consequently, this study establishes the fundamentals of developing sophisticated methods and tools for the advancement of Industry 4.0.

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“…The only way to adequately interpret the data attributes would be to infer the hidden correlations and semantics by a comprehensive analysis [3]. As [7] already noted, data owners often expect specific domain knowledge when creating their datasets, which is not mappable in some common data formats, such as CSV. Table 1 contains an exemplary dataset.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Link Prediction Approaches in Few-Shot Scenarios

Braken,

Paulus,

Pomp

et al. 2023

Electronics

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Semantic models are utilized to add context information to datasets and make data accessible and understandable in applications such as dataspaces. Since the creation of such models is a time-consuming task that has to be performed by a human expert, different approaches to automate or support this process exist. A recurring problem is the task of link prediction, i.e., the automatic prediction of links between nodes in a graph, in this case semantic models, usually based on machine learning techniques. While, in general, semantic models are trained and evaluated on large reference datasets, these conditions often do not match the domain-specific real-world applications wherein only a small amount of existing data is available (the cold-start problem). In this study, we evaluated the performance of link prediction algorithms when datasets of a smaller size were used for training (few-shot scenarios). Based on the reported performance evaluation, we first selected algorithms for link prediction and then evaluated the performance of the selected subset using multiple reduced datasets. The results showed that two of the three selected algorithms were suitable for the task of link prediction in few-shot scenarios.

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Recommending Semantic Concepts for Improving the Process of Semantic Modeling

Cited by 3 publications

References 17 publications

Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

What is knowledge in Industry 4.0?

An Evaluation of Link Prediction Approaches in Few-Shot Scenarios

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