Knowledge Integration in Smart Factories
Definition
:1. Introduction
2. Smart Factory
2.1. Smart Factory Environment
2.2. Multi-Dimensional Knowledge Integration in Smart Factories
3. Knowledge Integration
3.1. Knowledge Integration on Organizational Level: Horizontal and Vertical Integration
3.2. Knowledge Integration: Employee Level
3.3. Knowledge Integration: Technological Level
3.3.1. Data Computing/Processing in Smart Factories
3.3.2. Types of Data Analytics in Smart Factories
3.3.3. Simulation and Decision Making in Smart Factories Using Digital Twins
3.3.4. Semi- and Unstructured Data Integration
Application of Text Mining and Text Generation in Smart Factories
- Information extraction and retrieval: The information extraction process can be described as turning the unstructured textual data into structured and usable information. Since textual data are prone to including inconsistencies and human errors or potentially irrelevant data, a pre-processing phase takes place before the information extraction. After the raw text is pre-processed, several approaches have been addressed in the literature for the extraction of knowledge from the text, including Named Entity Recognition (NER) [53] and Relation Extraction (RE) [54]. Both methods are directly relevant for a semantic representation of data, since entities and relations can be identified in a meaningful way to define and populate the nodes and edges, in graph based representations of texts, as, e.g., knowledge graphs, while utilizing additional pre-existing domain knowledge [55].
- Classification, clustering and topic modelling: Classification is one of the important methods that allow creating a connection between a document and a search query. It is utilized in several applications, including the medical, commercial and industrial design processes. In the latter, Jiang et al. (2017) have developed an algorithm that predicts the importance of a product feature based on written user reviews [56]. Traditional text classification methods include Naïve Bayes, Support Vector Machines (SVM), and k-Nearest Neighbor. All those algorithms depend on the features of a document and the predefined labels that classify this document into a certain category. Unlike traditional approaches, which use Term Frequency–Inverse Document Frequency (TF-IDF), cosine similarities or probability functions, machine and deep learning algorithms learn to map document features to the available labels through creating a matching function that enables the system of categorizing a new document to one or more of the predefined classes based on its features.
- Acquisition and pre-indexing: In the acquisition and indexing phase, all relevant knowledge assets—including experts and extracted content from the knowledge base —are considered for extraction. Domain specific information such as textual information from design and process documentation is considered and subsequently identified through text analysis.
- Initialization: In the initialization phase, relevant knowledge sources are collected on the basis of the acquisition and indexing results, and metadata is assigned to documents. To sustain the extraction context and metadata of extraction algorithms, such results are stored using a graph-based knowledge representation concept, called Multidimensional Knowledge Representation (MKR) [59]. Based on the graph-based MKR of each document, other entities and semantically related content from other documents can be referenced [59]. Each extracted document is processed using text mining techniques to feed into the meta-structure of the MKR.
- Process Enrichment: In the process enrichment phase, relevant content is collected from the MKR structure, based on the relationships between elements and the underlying texts, and put into the format of an intelligent document. Each generated, intelligent document is a composition of identified knowledge assets. Based on the interaction of users, links to further resources can be followed and the document can be modified on demand and further enriched by additional asset recommendations.
- Automated Update Cycle: The automatic enrichment of the intelligent documents runs iteratively and continuously and modifies documents or adds content to new assets when new or changed source texts or extraction results are available (e.g., new metadata such as keywords, entity links, related process contexts, new incident reports).
Semantic Knowledge Representation in Smart Factories
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Entry Link on the Encyclopedia Platform
References
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Knowledge Processing | Realization in Smart Factory | Benefits | Exemplary Technologies 1 |
---|---|---|---|
Data Computing/ Processing | Cloud Computing | Software as a Service (SaaS), central software applications without local installation | Amazon AWS, Microsoft Azure |
Fog Computing | Enhance low-latency, mobility, network bandwidth, security and privacy | Cisco IOx | |
Edge Computing | Offload network bandwidth, shorter delay time (latency) | Cisco IOx, Intel IOT solutions, Nvidia EGX | |
Data Analytics | Descriptive Analytics | Status and usage monitoring, reporting, anomaly detection and diagnosis, modeling, or training | RapidMiner, RStudio Server, Tableau |
Stream Analytics: Real-Time Analysis | Apache Kafka/Flink, Elasticsearch and Kibana | ||
Batch Analytics: Monitoring/Reporting | Apache Spark/Zeppelin, Cassandra, Tensorflow, Keras | ||
Predictive Analytics | Predicting capacity needs and utilization, material and energy consumption, predicting component and system wear and failures | RapidMiner, RStudio Server, Tensorflow, Keras, AutoKeras, Google AutoML | |
Prescriptive Analytics | Guidance to recommend operational changes to optimize processes, avoid failures and associated downtime | RapidMiner, RStudio Server, Tensorflow, Keras, AutoKeras, Google AutoML | |
Simulation | Digital Twin Concept | Real-time analysis, simulation of scalable products and product changes, wear and tear projection | MATLAB Simulink, Azure Digital Twins, Ansys Twin Builder |
Semantic Knowledge Representation | Knowledge Graphs | Contextualization of multi-source data, Semantic relational learning | Neo4j, Grakn, Ontotext GraphDB, Eccenca corporate memory, Protégé |
Text Mining | Intelligent Documents | Integration of lessons learned from reporting and failure logs in new design or production cycles | tm (R), nltk (Python), RapidMiner, Text Analytics Toolbox (MATLAB), Apache OpenNLP, Stanford CoreNLP |
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Zenkert, J.; Weber, C.; Dornhöfer, M.; Abu-Rasheed, H.; Fathi, M. Knowledge Integration in Smart Factories. Encyclopedia 2021, 1, 792-811. https://doi.org/10.3390/encyclopedia1030061
Zenkert J, Weber C, Dornhöfer M, Abu-Rasheed H, Fathi M. Knowledge Integration in Smart Factories. Encyclopedia. 2021; 1(3):792-811. https://doi.org/10.3390/encyclopedia1030061
Chicago/Turabian StyleZenkert, Johannes, Christian Weber, Mareike Dornhöfer, Hasan Abu-Rasheed, and Madjid Fathi. 2021. "Knowledge Integration in Smart Factories" Encyclopedia 1, no. 3: 792-811. https://doi.org/10.3390/encyclopedia1030061