Semantic Knowledge Base Research Articles

Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor

AbstractArtificial intelligence (AI) among other digital technologies promise to deliver the next level of process efficiency of manufacturing systems. Although these solutions such as machine learning (ML) based condition monitoring and quality inspection are becoming popular, these work under very limited conditions. Solutions do not scale-up in the real environment, where there is a mix of manufacturing equipment, where the quality and quantity of data available changes from machine to machine, or where the process changes, changing the distribution of data (i.e. concept drift). This is particularly challenging in highly reconfigurable and flexible environments. Having to develop machine learning models from scratch every single time is not cost-effective, time-consuming, requires expert knowledge that is typically not available in the manufacturing environment as well as can be challenging when data is not available in high volumes. Model robustness, reusability, adaptability and life cycle management are the keys to scale-up this technology in the manufacturing industry. In this work, a conceptual framework to enable simple and robust ML model development for the shop floor is introduced. Referred here as Frugal Industrial AI, the approach takes advantage of existing models and their context to build more robust ones in a data-efficient manner. Using a semantic knowledge base of how to construct these models for different manufacturing applications and semi-automating the development or reuse of solutions through semantic similarity, it is demonstrated how models can be developed in a more streamlined way. In addition, it is demonstrated how capturing process context information is important for the effective reuse of existing models through continual learning. This is key to building more robust ML solutions that can deal with real changing manufacturing environments, avoiding retraining from scratch as well as enabling the non-expert to use AI effectively on the shop floor.

Object–attribute–relation model-based semantic coding for image transmission

The development of digital, networked and intelligent communication technology has led to significant growth in data and services. Especially in the communication scenario of monitoring data, a variety of downstream tasks bring significant technical challenges for high-performance data transmission, information mining, and data distribution. The problem to be solved is the coding and low bit-rate transmission of large amounts of surveillance data. Concepts and methods that utilize semantic communication are needed. This paper proposes a communication architecture based on the object–attribute–relation (OAR) semantic knowledge base. The sender uses the model parameters and prior knowledge in the knowledge base to drive the OAR extraction model to encode the image. After semantic coding and channel coding, the signal is transmitted to the receiver through the physical channel. At the receiver, the signal is decoded into a semantic vector. The OAR global vector is generated by structured processing, which is used for downstream tasks such as image retrieval or image reconstruction. Experimental comparison and analysis illustrate the effectiveness and superiority of each part of the communication framework. This approach provides a new solution for improving data utilization and processing efficiency.

Research on Entity Relationships in the Knowledge Graph of Disease Monitoring in Grotto Temples

Abstract. The grotto temple, carved into cliffs and widely distributed, is a significant cultural heritage in China. However, it faces severe damage and collapse threats due to natural disaster risks in its environment. Nearly seventy percent of grotto temples are located in regions prone to earthquakes and water hazards, leading to varying degrees of damage to cultural artifacts. Therefore, preventive measures are necessary to reduce the impact of natural disasters on grotto temples. A knowledge graph, a structured semantic knowledge base describing concepts and their relationships in the physical world, plays a crucial role in knowledge organization and content representation. Entity relationships are the core of knowledge, serving as both foundational data and a key task in constructing knowledge graphs and processing unstructured text. In the field of grotto temple disease monitoring, while data continues to grow, research on the correlation between textual data remains underexplored. This paper adopts the BiLSTM-CRF method to extract entity relationships, matching them with the grotto temple monitoring knowledge graph. Finally, the Neo4j software is utilized to program and display the knowledge graph, aiming to enhance the efficiency of natural disaster risk management and cultural heritage protection for grotto temples.

Development of the mobile application “Fascinating Onomastics” based on semantic base of knowledge

This research, funded under the BR11765535 “Development of Scientific and Linguistic Foundations and IT Resources to Expand the Functions and Improve the Culture of the Kazakh Language” project, is aimed at the need to develop the scientific and linguistic foundations of the Kazakh language with a special focus on anthroponymy. Through the use of descriptive and historical methods, the research creates a semantic knowledge base for Kazakh proper names. This extensive database forms the basis of the innovative mobile application "Fascinating Onomastics", developed using the Flutter 3.0 framework. The application serves as an innovative resource allowing users to study Kazakh anthroponymy with features such as name search, favorites and the ability to add new names. The study revealed about 100 properties that affect the structure of proper names, contributing to a deeper understanding of Kazakh onomastics. The technical and economic effectiveness of the application is highlighted by its potential to increase cultural awareness, promote cultural tourism and be used as an economically viable product. In addition, this study has a high scientific and technical status, offering an unprecedented contribution to the study of Kazakh onomastics and cultural heritage. The application "Fascinating Onomastics" with its rich semantic features and interface in the Kazakh language is becoming an important milestone in achieving scientific, cultural and educational goals.

A Review- Machine Learning Techniques for Text Summarization

Aspect ranking framework is significant to identify the important aspects from numerous consumer reviews posted in various domains like hotel, movie and product etc. They could be broadly classified into supervised and unsupervised approaches broadly. Supervised methods rely on semantic knowledge bases. These are found to be effective for ranking compared to conventional approaches. These methods available in the literature are discussed in detail. Next, this review focuses on the extractive summarization systems, in which the summary is generated by picking a sub-set of sentences from the related text. Extractive summarization systems that utilize machine learning, optimization and map reduce framework are explained elaborately. This is due to the efficiency of these techniques reported in the comprehensive works available for text summarization. A literature covering text similarity discovery methods employing text, semantic information and graph based systems are presented in detail at the end of the chapter. Among these graph based methods play a vital role in current field of the research

Finite State Automata on Multi-Word Units for Efficient Text-Mining

Text mining is crucial for analyzing unstructured and semi-structured textual documents. This paper introduces a fast and precise text mining method based on a finite automaton to extract knowledge domains. Unlike simple words, multi-word units (such as credit card) are emphasized for their efficiency in identifying specific semantic areas due to their predominantly monosemic nature, their limited number and their distinctiveness. The method focuses on identifying multi-word units within terminological ontologies, where each multi-word unit is associated with a sub-domain of ontology knowledge. The algorithm, designed to handle the challenges posed by very long multi-word units composed of a variable number of simple words, integrates user-selected ontologies into a single finite automaton during a fast pre-processing step. At runtime, the automaton reads input text character by character, efficiently locating multi-word units even if they overlap. This approach is efficient for both short and long documents, requiring no prior training. Ontologies can be updated without additional computational costs. An early system prototype, tested on 100 short and medium-length documents, recognized the knowledge domains for the vast majority of texts (over 90%) analyzed. The authors suggest that this method could be a valuable semantic-based knowledge domain extraction technique in unstructured documents.

Research on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases

The health care domain is a knowledge-intensive domain. The quality of clinical diagnosis relies mainly on the medical knowledge and experience held by doctors. However, the ability of a single doctor is very limited, so the quality of clinical diagnosis is currently not high. In this paper, an aided diagnosis method based on domain semantic knowledge bases is proposed. Firstly, a domain semantic knowledge base is established by extracting and refining the knowledge of the medicine subject matter domain from the Freebase RDF dumps. Then, based on the semantic knowledge base, the algorithms for calculating the weights of the symptoms in the knowledge base, the relative weights of the diseases related to the input symptom set from a patient, and the related symptom set related to the input symptom set from the patient are proposed. Finally, the clinical medical record data of several common diseases are selected to make an evaluation on the proposed method. For each medical record, the symptom information is extracted from the chief complaint as the patient’s input symptom set. Based on the input symptom set, the method of this paper is used to obtain the list of related diseases and the ranking of disease relative weights. From the disease relevance rankings, the Top 1 (first diagnosis) and Top-3 (first 3 diagnoses) are compared with the doctor’s diagnoses in the medical records. Among them, ovarian cyst has the highest Top-1 and Top-3 hit rates of 67.3% and 89.1%, respectively. Followed by acute upper respiratory tract infection, Top-1 and Top-3 hit rates are 56.6% and 85.2%, respectively. The average Top-1 and Top-3 hit rates are 47.9% and 79.7%, respectively. Compared with the relevant methods, the method of this paper is better. The evaluation results show that based on the domain semantic knowledge base and the aided diagnosis method of diseases constructed in this paper, it is possible to provide aided diagnosis services of a large number of common diseases for general practitioners (especially inexperienced doctors) at the grass-roots level as well as self-diagnosis services of diseases for patients.

Using semantic story maps to describe a territory beyond its map

The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool’s effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.

Semantic-Driven Efficient Service Network Towards Smart Healthcare System in Intelligent Fabric

The advantages of intelligent fabrics enable health service systems more efficient and safe to perceive and transmit, where massive sensitive data related to health and behavior are collected through the multi-source flexible sensor network. However, due to its complex network structure, health service systems face the challenge of low-power data transmission and processing in intelligent fabric space. Recently, Deep learning-powered semantic communication (Deep-SC) has emerged as a promising communication paradigm. To tackle the above problem, we introduce the concept of semantic cognition into the cyber-physical system of health service and present the Deep-SC based network service framework for the healthcare system, where physical layer blocks are merged into the traditional communication system to jointly optimize the transceiver in communication. Since semantic encoding and decoding require additional knowledge, consider the introduction of the semantic knowledge base in the system to formulate the joint optimization problem of service offloading and bandwidth allocation in the proposed network service framework, with the goals of reliable semantic communication and efficient network transmission. Furthermore, an online algorithm that optimally adapts service offloading and subcarrier allocation decisions to the time-varying channel conditions is required. Finally, we use a reinforcement learning-based decision algorithm to solve combinatorial optimization problems, which further reduces the computing complexity. Experimental results show that the proposed framework achieves significant performance improvements in system energy consumption and reliable transmission, compared with traditional communication strategies.

Exploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction using a Hybrid Semantic Knowledge Base - Machine Learning Approach

Relation extraction from unstructured Arabic text is especially challenging due to the Arabic language complex morphology and the variation in word semantics and lexical categories. The research documented in this paper presents a hybrid Semantic Knowledge base - Machine Learning (SKML) approach for extracting complex Arabic relations from unstructured Arabic documents; the proposed approach exploits the principles of Functional Discourse Grammar (FDG) to emphasise the semantic and pragmatic properties of the language and facilitate the identification of relation elements. At the initial phase, the novel FDG-SKML relation extraction approach deploys a lexical-based mechanism that utilises a purposely built domain-specific Semantic Knowledge to encode the semantic association between the identified relations’ elements. The evaluation of the initial stage evidenced improved accuracy for extracting most complex Arabic relations. The initial relation extraction mechanism was further extended by integrating its output into a Machine Learning classifier that facilitated extracting especially complex relations with significant disparity in the relation elements’ presence, order, and correlation. Using Economics as the problem domain, experimental evaluation evidenced the high accuracy of our FDG-SKML approach in complex Arabic relation extraction task and demonstrated its further improvement upon integration with machine learning classifiers.

The Indoor Positioning Method Time Difference of Arrival with Conic Curves Utilizing a Novel Networking RFID System

At present, the demand for accurate indoor positioning at a low cost is increasing. Based on the architecture of networking passive radio frequency identification (RFID) systems, research into passive location algorithms is important for finding a location solution with ultra-low cost, easy implementation, and no required maintenance. In this paper, TDACC (time difference of arrival with conic curves) based on signal propagation time is proposed, which breaks down the positioning problem into solving the intersection of an ellipse and a hyperbola. The results indicate that this method has a positioning error of 0 m in the absence of signal interference. When the time delay fluctuates to 1 ns and 2 ns, the average errors of TDACC are 0.19 m and 0.33 m, respectively. Different from other time-based localization methods, the proposed method only requires two distribution nodes without time synchronization, which reduces the system cost. These results will help to promote the deeper semantic communication level fusion of passive RFID. By improving the coordinate positioning in the semantic prior knowledge base, this method will lead to more efficient and accurate industry applications.

Federated Zero-Shot Industrial Fault Diagnosis With Cloud-Shared Semantic Knowledge Base

Recently, a considerable literature has grown up around the few-sample fault diagnosis task, in which few samples of fault data are available for model training. The lack of fault samples usually causes significant degradation of diagnosis performance, which is referred as few-sample problem. Many federated learning-based fault diagnosis methods are proposed as a popular strategy to solve this problem. However, the vast majority of these algorithms assume that few-sample categories of the local client are well-sampled in some clients. In this sense, they are unable to cope with faults that have rare or even no sample in all clients, namely global unseen faults. To diagnosis global unseen fault categories, a federated zero-shot fault diagnosis framework is proposed in this article. First, we propose a general approach for building a semantic knowledge base, which provides auxiliary discriminant descriptions of different faults. Second, a bidirectional alignment network is designed using two coupled variational autoencoders, enabling the fusion of data and attributes and allowing attribute descriptions to assist in fault diagnosis tasks. Third, a cloud-edge collaboration model aggregation strategy is developed, which utilizes generative replay mechanism and integrates the knowledge of each client, thus enhancing the generalization and generation ability of the global model. Experiments conducted on the thermal power plant group illustrate the feasibility and effectiveness of the proposed framework to categorize local and global unseen classes.

Generating risk response measures for subway construction by fusion of knowledge and deep learning

This study aims to propose a method to generate response measures for subway construction risks. The HowNet semantic theoretical system was selected and incorporated the knowledge of subway construction risk management to develop the semantic knowledge base. Then, a risk response generation algorithm was designed based on Knowledge-enabled Bidirectional Encoder Representation from Transformers (K-BERT) to fuse knowledge and deep learning (DL). Meanwhile, the BERT in the original K-BERT model was replaced by the Masked Language Model (MLM) as correction BERT (MacBERT) pre-training model suitable for Chinese natural language processing (NLP) tasks. The data-driven and knowledge-driven approaches were combined for better performance of DL. The pre-training model based on MacBERT was selected to undertake the comparative experiment of fusing knowledge and DL. The results showed that the proposed algorithm with the K-BERT fusion of knowledge and DL had better performance than the MacBERT-based model without the knowledge base.

EXTRACTION, TRANSFORMATION AND LOADING PROCESS IN THE CLOUD COMPUTING SCENARIO

-Business Intelligence strategies often need real-time BI and analytics. For this purpose, BI systems focus on data management as a whole and good data and information quality in particular to achieve data consistency and referential integrity across distributed systems. Generating key performance indicators (KPI) of business intelligence might require incorporating the semantic approach of data integration to achieve the consistent and sustainable semantic decisions. So this paper deals with how to improve data and information quality by adopting Extraction-Transformation-Loading (ETL) and Extraction-Loading-Transformation processes towards generating structured data and semantic data with quality towards improving inference capabilities and generating semantic knowledge base for the business domain ontology. However this research did not consider dealing with dynamic or real-time data quality and their role towards producing better business intelligence

A semantic data-driven knowledge base construction method to assist designers in design inspiration based on traditional motifs

Directing at the problem of inaccurate expression of tacit knowledge and low heuristic of retrieval results in the design inspiration drawing process of designers based on traditional motifs, a semantic data representation method of traditional motifs knowledge is put forward, and an effective knowledge base construction and knowledge retrieval method for traditional motifs are described. First, the connotation of traditional motifs is deeply explored based on the theory of symbol semantics, realizing the interpretation and transfer of the semantic features in traditional motifs. Second, shape grammar is adopted for fractal traditional motifs, the hierarchical structure of traditional motifs is built, and the parametric coding of semantic features of traditional motifs is completed, providing the semantic data representation method of traditional motifs knowledge. Third, based on the clustering algorithm, the construction method of the traditional motifs knowledge base is explained, and a traditional motifs knowledge retrieval method taking into account the user's perceptual preference is proposed. Finally, setting the traditional Chinese motifs as an example, with a tea set as the designed product, the construction process of a small traditional motif knowledge base is shown in detail, proving the feasibility of this paper’s theory. Compared with the traditional theory, the semantic data representation method of traditional pattern knowledge is greatly advantageous in semantic differences elimination and computational efficiency.

Investigating the potential of the semantic web for education: Exploring Wikidata as a learning platform.

Wikidata is a free, multilingual, open knowledge-base that stores structured, linked data. It has grown rapidly and as of December 2022 contains over 100 million items and millions of statements, making it the largest semantic knowledge-base in existence. Changing the interaction between people and knowledge, Wikidata offers various learning opportunities, leading to new applications in sciences, technology and cultures. These learning opportunities stem in part from the ability to query this data and ask questions that were difficult to answer in the past. They also stem from the ability to visualize query results, for example on a timeline or a map, which, in turn, helps users make sense of the data and draw additional insights from it. Research on the semantic web as learning platform and on Wikidata in the context of education is almost non-existent, and we are just beginning to understand how to utilize it for educational purposes. This research investigates the Semantic Web as a learning platform, focusing on Wikidata as a prime example. To that end, a methodology of multiple case studies was adopted, demonstrating Wikidata uses by early adopters. Seven semi-structured, in-depth interviews were conducted, out of which 10 distinct projects were extracted. A thematic analysis approach was deployed, revealing eight main uses, as well as benefits and challenges to engaging with the platform. The results shed light on Wikidata's potential as a lifelong learning process, enabling opportunities for improved Data Literacy and a worldwide social impact.

Fuzzy Networks for Modeling Shared Semantic Knowledge

Abstract Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description logic as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C(a) for some concept C” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.”

From unstructured texts to semantic story maps

ABSTRACT Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole. Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching the map with text, pictures, videos, and other multimedia information. This paper presents a semi-automatic workflow to produce story maps from textual documents containing territory data. An expert first assembles one territory-contextual document containing text and images. Then, automatic processes use natural language processing and Wikidata services to (i) extract key concepts (entities) and geospatial coordinates associated with the territory, (ii) assemble a logically-ordered sequence of enriched story-map events, and (iii) openly publish online story maps and an interoperable Linked Open Data semantic knowledge base for event exploration and inter-story correlation analyses. Our workflow uses an Open Science-oriented methodology to publish all processes and data. Through our workflow, we produced story maps for the value chains and territories of 23 rural European areas of 16 countries. Through numerical evaluation, we demonstrated that territory experts considered the story maps effective in describing their territories, and appropriate for communicating with citizens and stakeholders.

A Review- Machine Learning Techniques for Text Summarization

Aspect ranking framework is significant to identify the important aspects from numerous consumer reviews posted in various domains like hotel, movie and product etc. They could be broadly classified into supervised and unsupervised approaches broadly. Supervised methods rely on semantic knowledge bases. These are found to be effective for ranking compared to conventional approaches. These methods available in the literature are discussed in detail. Next, this review focuses on the extractive summarization systems, in which the summary is generated by picking a sub-set of sentences from the related text. Extractive summarization systems that utilize machine learning, optimization and map reduce framework are explained elaborately. This is due to the efficiency of these techniques reported in the comprehensive works available for text summarization. A literature covering text similarity discovery methods employing text, semantic information and graph based systems are presented in detail at the end of the chapter. Among these graph based methods play a vital role in current field of the research.

Semantic-enhanced neural collaborative filtering models in recommender systems

Recommendation systems or recommender systems (RSs) are very popular in entertainment websites. With the combination of neural networks and collaborative filtering, Neural Collaborative Filtering (NCF) recommendation methods have shown their outperformance in making item suggestions. However, the lack of semantic relationships between objects makes the NCF unable to capture the complex user–item interactions. Moreover, traditional NCF is unable to capture the dynamic user preference over time. To address these issues, in this paper, we propose novel semantic-enhanced NCF models which are applied to movie rating prediction and movie recommendation. Therefore, MovieLens and IMDB datasets are taken into account as case studies. The proposed models are the integration of ontology-like modeling and deep learning for recommendation tasks into two parts:(1) building the semantic knowledge base for movies and (2) building the user behavior analytic model that has semantic knowledge inference on the knowledge base combined with the sequential preference learned from user sessions, input into the NCF module for making predictions or recommendations. Several experiments have been conducted to show their better recommendation performance than the traditional NCF model.