Semantic Rules Research Articles

The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development

The development of knowledge graphs about water resources as a tool for studying the sustainable development of a region is currently an urgent task, because the growing deterioration of the state of water bodies affects the ecology, economy, and health of the population of the region. This study presents a new ontological approach to water resource monitoring in Kazakhstan, providing data integration from heterogeneous sources, semantic analysis, decision support, and querying and searching and presenting new knowledge in the field of water monitoring. The contribution of this work is the integration of table extraction and understanding, semantic web rule language, semantic sensor network, time ontology methods, and the inclusion of a module of socioeconomic indicators that reveal the impact of water quality on the quality of life of the population. Using machine learning methods, the study derived six ontological rules to establish new knowledge about water resource monitoring. The results of the queries demonstrate the effectiveness of the proposed method, demonstrating its potential to improve water monitoring practices, promote sustainable resource management, and support decision-making processes in Kazakhstan, and can also be integrated into the ontology of water resources at the scale of Central Asia.

Norms and necessity: replies to critics

ABSTRACT The critics in this volume raise several important challenges to the modal normativist position developed in Norms and Necessity, including whether the relation I claim holds between semantic rules and necessity claims generates spurious claims of metaphysical necessity, whether the view is circular (implicitly relying on a more 'robust' form of modal realism), and whether it conflicts with truth-conditional semantics. They also raise probing questions about how it compares to other views of modality, including a Lewisian view and an essentialist view. In these replies, I respond to these challenges in ways that precisify the relevant understanding of ‘semantic rules' and the forms they can take, that make clearer the direction of explanation in modal normativism in ways that show the view doesn't rely on a more ‘robust’ form of modal realism, and that give reason for thinking that there is actually no conflict between modal normativism and truth-conditional semantics. I also aim to give a fuller assessment of how it compares to other approaches to modality, including an essentialist approach.

Semantic enrichment of building functions through geospatial data integration and ontological inference

The comprehensive definition of buildings in urban spatial databases (SDBs) and geographic information systems (GISs) is crucial for city management, considering their essential role in cities. Modern urban geospatial applications such as smart cities need a rich-information infrastructure, relying upon urban SDBs and GISs, powered by multiple data sources. In this respect, many geospatial techniques are available for acquiring geometric data of buildings, but their semantic definitions require extra effort. Today, volunteered geographic information (VGI) platforms are assumed to be alternative geospatial data sources and their integration with official datasets provides a new opportunity for the enrichment of urban geospatial datasets. In this context, geospatial semantic web technologies can contribute to the semantic enrichment process. This study presents a methodology for enriching various building datasets using GIS and geospatial semantic web technologies, enabling an enhanced definition of building functions. The proposed method makes use of an OpenStreetMap (OSM) dataset to obtain missing semantic information about building features. The enrichment process was implemented by transferring OSM point of interest (POI) values to associated OSM building features through the application of semantic web rule language (SWRL) rules. Furthermore, the same process was also applied to two additional official datasets at different levels of detail (TOPO1K and TOPO25K), which were subsequently incorporated into the geospatial ontology. The resulting geospatial ontology offers new opportunities for functional definition and logical rule-making for buildings, with the potential to uncover new classes and insights. In addition, it allows for the definition of terms and explanations in multiple languages, covering most of the OSM values used worldwide for selected tags to define building functions.

A Knowledge-Guided Fusion Visualisation Method of Digital Twin Scenes for Mountain Highways

Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high complexity of mountainous road scene modeling and low visualisation efficiency. It is challenging to construct the digital twin scenarios efficiently for mountain highways. To solve this problem, this article proposes a knowledge-guided fusion expression method for digital twin scenes of mountain highways. First, we explore the expression features and interrelationships of mountain highway scenes to establish the knowledge graph of mountain highway scenes. Second, by utilizing scene knowledge to construct spatial semantic constraint rules, we achieve efficient fusion modeling of basic geographic scenes and dynamic and static ancillary facilities, thereby reducing the complexity of scene modeling. Finally, a multi-level visualisation publishing scheme is established to improve the efficiency of scene visualisation. On this basis, a prototype system is developed, and case experimental analysis is conducted to validate the research. The results of the experiment indicate that the suggested method can accomplish the fusion modelling of mountain highway scenes through knowledge guidance and semantic constraints. Moreover, the construction time for the model fusion is less than 5.7 ms; meanwhile, the dynamic drawing efficiency of the scene is maintained above 60 FPS. Thus, the construction of twinned scenes can be achieved quickly and efficiently, the effect of replicating reality with virtuality is accomplished, and the informatisation management capacity of mountain highways is enhanced.

Semantic rule checking of cross-domain building data in information containers for linked document delivery using the shapes constraint language

Information exchange is a crucial process throughout the lifecycle of built assets. Building Information Modeling (BIM) and standardized information delivery enable exchanging, storing, and linking cross-domain information bundled in containers such as the standardized Information Container for Linked Document Delivery (ICDD). While assessing the quality of the interlinking and metadata is essential to allow all parties to interpret an information container correctly, most research efforts have focused on code compliance of domain-specific models. In order to address this gap, this paper proposes an approach for validating ICDD information containers using the Shapes Constraint Language (SHACL), the W3C recommendation for Resource Description Framework (RDF) data validation. For optimized querying and validation, a case study applies SHACL inference rules to the linking structure of ICDD to infer predicates between linked entities in four ICDD containers in varying dataset sizes and different linking methods. Afterward, the validation of these containers is analyzed using three types of validation rules with increasing complexity for another SHACL implementation. Results show the feasibility of cross-domain building information validation in ICDD with SHACL and how performance enhancements can be obtained on an inferred dataset compared to the original dataset.

Binary Semantic Pattern Rules for Chinese-English Machine Translation Based on Machine Learning Algorithms

With the increase of internationalization and the exponential growth of intercultural communication, the importance of interlingual translation has become increasingly prominent. Machine translation has been a booming area of research as technology has advanced. Due to the complexity of language ability and limited understanding of language laws, there are challenges for machine translation. This paper focused on how to construct and apply binary semantic pattern rules through machine learning to improve the translation effect in Chinese-English machine translation. The research results of this paper would contribute to the further development and improvement of Chinese-English machine translation technology. In order to produce high-quality translation results, research in machine translation has recognized the need to analyze and understand the semantics of natural language. To address the important issue of lexical and syntactic ambiguity, representations of binary semantic pattern rules have been developed to formally describe these rules. Based on this, this paper designed and implemented a corpus-based binary semantic rule extraction and optimization algorithm, which used machine learning algorithms to automatically detect the semantic rules of two or more than two phrases in the Chinese corpus, and then automatically optimized and converted them according to the statistical results, and realized the design of Chinese-English machine translation system. The article evaluated the quality of machine translation to test the effectiveness of machine translation binary semantic pattern rules based on machine learning algorithms. The study found that compared with the rule set A, the rule sets B and C obtained automatically by the rule mining algorithm had significantly improved accuracy, both reaching more than 90%. This showed that the binary semantic pattern rule mining algorithm and optimization algorithm proposed in this paper were reasonable.

The right superior temporal gyrus plays a role in semantic-rule learning: Evidence supporting a reinforcement learning model

In real-life communication, individuals use language that carries evident rewarding and punishing elements, such as praise and criticism. A common trend is to seek more praise while avoiding criticism. Furthermore, semantics is crucial for conveying information, but such semantic access to native and foreign languages is subtly distinct. To investigate how rule learning occurs in different languages and to highlight the importance of semantics in this process, we investigated both verbal and non-verbal rule learning in first (L1) and second (L2) languages using a reinforcement learning framework, including a semantic rule and a color rule. Our computational modeling on behavioral and brain imaging data revealed that individuals may be more motivated to learn and adhere to rules in an L1 compared to L2, with greater striatum activation during the outcome phase in the L1. Additionally, results on the learning rates and inverse temperature in the two rule learning tasks showed that individuals tend to be conservative and are reluctant to change their judgments regarding rule learning of semantic information. Moreover, the greater the prediction errors, the greater activation of the right superior temporal gyrus in the semantic-rule learning condition, demonstrating that such learning has differential neural correlates than symbolic rule learning. Overall, the findings provide insight into the neural mechanisms underlying rule learning in different languages, and indicate that rule learning involving verbal semantics is not a general symbolic learning that resembles a conditioned stimulus-response, but rather has its own specific characteristics.

INK: Knowledge graph representation for efficient and performant rule mining

Semantic rule mining can be used for both deriving task-agnostic or task-specific information within a Knowledge Graph (KG). Underlying logical inferences to summarise the KG or fully interpretable binary classifiers predicting future events are common results of such a rule mining process. The current methods to perform task-agnostic or task-specific semantic rule mining operate, however, a completely different KG representation, making them less suitable to perform both tasks or incorporate each other’s optimizations. This also results in the need to master multiple techniques for both exploring and mining rules within KGs, as well losing time and resources when converting one KG format into another. In this paper, we use INK, a KG representation based on neighbourhood nodes of interest to mine rules for improved decision support. By selecting one or two sets of nodes of interest, the rule miner created on top of the INK representation will either mine task-agnostic or task-specific rules. In both subfields, the INK miner is competitive to the currently state-of-the-art semantic rule miners on 14 different benchmark datasets within multiple domains.

Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

Human Activity Recognition (HAR) has become a subject of concern and plays an important role in daily life. HAR uses sensor devices to collect user behavior data, obtain human activity information and identify them. Markov Logic Networks (MLN) are widely used in HAR as an effective combination of knowledge and data. MLN can solve the problems of complexity and uncertainty, and has good knowledge expression ability. However, MLN structure learning is relatively weak and requires a lot of computing and storage resources. Essentially, the MLN structure is derived from sensor data in the current scene. Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules, MLN structure can be obtained. To this end, we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning. Such a rulebase can reduce the time required for MLN structure learning. We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time. In addition, we evaluate the parameters of the rulebase building scheme to check its stability.

The Iconic-Symbolic Spectrum

It is common to distinguish two great families of representation. Symbolic representations include logical and mathematical symbols, words, and complex linguistic expressions. Iconic representations include dials, diagrams, maps, pictures, 3-dimensional models, and depictive gestures. This essay describes and motivates a new way of distinguishing iconic from symbolic representation. It locates the difference not in the signs themselves, nor in the contents they express, but in the semantic rules by which signs are associated with contents. The two kinds of rule have divergent forms, occupying opposite poles on a spectrum of naturalness. Symbolic rules are composed entirely of primitive juxtapositions of sign types with contents, while iconic rules determine contents entirely by uniform natural relations with sign types. This distinction is marked explicitly in the formal semantics of familiar sign systems, both for atomic first-order representations, like words, pixel colors, and dials, and for complex second-order representations, like sentences, diagrams, and pictures.

Ontology-Based Food Menu Recommender System for Patients with Coronary Heart Disease

Coronary heart disease is one of the leading causes of death. Knowledge of dietary patterns and proper food selection is an effort to address the risk and support coronary heart disease's healing process. Therefore, this study developed a food menu recommender system as a reference for patients with coronary heart disease. The recommender system is crucial in creating a proper dietary pattern for managing personalized meal plans. The system calculates the required nutritional needs of users. Ontology is used to represent knowledge about nutrition data and food intake. The ontology base with Semantic Web Rule Language (SWRL) enables the system to identify the most suitable foods for patients with coronary heart disease. We use SWRL rules to generate recommendation conclusions based on the existing ontology. Using this language enhances the descriptive logic capabilities, as the rules can overcome the limitations of the ontology language. Therefore, the system is built to find food menu options that match the required nutrition for patients. The nutritionist knowledge will be used to measure the system's performance compared to the recommendations made by nutritionists. From the user data sample, 150 recommended food menu data were obtained. The validation performance results obtained a precision 0.893, recall 1, and F_Score 94.3%.

Ontology-Based Food Recommender System for Nutrition in School-Age

Nutrition plays an important role in the body and child development. Therefore, it is very important for parent to understand the nutritional needs of children to grow healthy and smart. If nutritional intake is not met, malnutrition can occur in children it interferes whit their growth and development process. The food recommendation system in this study is based on knowledge modeling. The focus of the research is to develop a recommendation system using ontology with Semantic Web Rule Language (SWRL) and form a knowledge base according to the guidelines proposed by Recommended Nutrient Intakes (RNI). Additionally, an Artificial Intelligence (AI) telegram chatbot named NutritionChildreBot was developed for this purpose. The recommended food menu is following the nutritional needs of children aged 7-9 years. The acquired knowledge base will be managed to provide information to users. The results of this research evaluation are in the form of recommendations for selecting foods that meet children’s nutritional needs based on information obtained from reliable sources.Based on this value, the calculation of precision, memory, and F_Score obtained is 97,9% of the accuracy of the results recommended by the system

Ontology-based Nutrition Recommender System for Stunting Patients

Stunting is a growth disorder that occurs in early childhood. This condition occurs because the child has a chronic nutritional problem which triggers the child to have a height below normal. The indicator used as a standard for whether a child is stunted or not is height for age. If a child has a z-score value less than -2 standard deviations, then the child is said to suffer from stunting. Poor nutritional intake is one of the factors causing children to suffer from stunting. Most Indonesian people think that the genetics of both parents causes children to be shorter than their age, but genetics is a minimal factor that causes stunting. In 2020, Indonesia ranks second in the prevalence of stunting in Southeast Asia, according to the Asian Development Bank (ADB) report. Based on the results of the Indonesian Nutritional Status Survey (SSGI) in 2021, the stunting prevalence rate in Indonesia 2021 is 24.4%, but in 2022, the stunting prevalence rate will drop to 21.6%. One way to treat stunting in children is by providing daily nutritional intake according to the child's condition. In this study, we used the Telegram chatbot with an ontology and the rules Semantic Web Rule Language as a knowledge base. The accuracy performance of our system is 93.3% which shows that our system can provide nutritional recommendations for stunting patients.

Remote intervention assistance system for a person in difficulty based on probabilistic ontologies

The proposed theme of this paper is to develop a system for remote intervention assistance with a person in difficulty, which is becoming increasingly important as the world’s population ages. The goal is to provide effective long-term care to reduce hospitalizations of the elderly and alleviate the burden of care for hospitals and governments. The system requires careful consideration of costs, resources, and efficiency due to the complexity and heterogeneity of the data involved, making human analysis nearly impossible. To enable machine analysis and decision-making, the proposed system is based on an ontological approach that combines medical, social, temporary, and geographical factors, along with Semantic Web Rule Language (SWRL). The system is further enhanced by the integration of probabilistic reasoning through Probabilistic Web Ontology Language (PR-OWL), which allows for the use of ontological resources in a probabilistic environment. The paper also proposes two additional approaches in the form of Jena Application Programming Interfaces (API), including ontological enrichment based on Bayesian Network (BN) knowledge bases and structural/parametric learning based on ontological knowledge bases, as well as a probabilistic clause integrated into the ontological format. As results, the proposed approaches were tested in a simulation, and feedback was collected from users. The feedback was compared, and an increase in precision was observed from one approach to another with varying execution times. These results demonstrate the effectiveness of the proposed system for remote intervention assistance and provide a foundation for future research and development in this area.

A Knowledge Graph-Based Approach for Assembly Sequence Recommendations for Wind Turbines

There are various forms of assembly data sources for wind turbines, which contributes to the lack of a unified and standardized expression. Moreover, the reusability of historical assembly data is low, which leads to the poor reasoning ability of a new product assembly sequence. In this paper, we propose a knowledge graph-based approach for assembly sequence recommendations for wind turbines. First, for the multimodal data (text in process manual, image of tooling, and three-dimensional (3D) model) of assembly, a multi-process assembly information representation model is established to express assembly elements in a unified way. In addition, knowledge extraction methods for different modal data are designed to construct a multimodal knowledge graph for wind turbine assembly. Further, the retrieval of similar assembly process items based on the bidirectional encoder representation from transformers joint graph-matching network (BERT-GMN) is proposed to predict the assembly sequence subgraphs. Also, a Semantic Web Rule Language (SWRL)-based assembly process items inference method is proposed to automatically generate subassembly sequences by combining component assembly relationships. Then, a multi-objective sequence optimization algorithm for the final assembly is designed to output the optimal assembly sequences. Finally, taking the VEU-15 wind turbine as the object, the effectiveness of the assembly process information modeling and part multi-source information representation is verified. Sequence recommendation results are better quality compared to traditional assembly sequence planning algorithms. It provides a feasible solution for wind turbine assembly to be optimized from multiple objectives simultaneously.

A Maximum Semantic Reservation Mapping Method Based on Ontology-to-graph Database

<p>Ontology is a core concept model in a knowledge graph which describes knowledge in the form of a graph. With the increase in knowledge graphs, the semantic relationships between concepts become more and more complex, which increases the difficulty of reserving its semantic integrity when storing it in a database. In this paper, we propose an ontology-to-graph database mapping method, which can reserve maximum semantic integrity and reduce redundant information simultaneously with high storage efficiency and query efficiency. In detail, the mapping method uses an anonymous class storage strategy to handle indefinite long nested structures, a multivariate functional relation storage strategy for multivariate semantic analysis, and an SWRL (Semantic Web Rule Language) storage strategy for disassembling inference structures. We develop an ontology-to-graph database prototype Neo4J4Onto to implement the mapping method. Experimental results show that our method achieves the maximum semantic integrity with the lowest complexity compared to the 6 baseline methods. Besides, compared to graphDB, Neo4J4Onto has better storage and query efficiency, and the concept models retrieved by Neo4J4Onto are more complete.</p> <p> </p>

Fuzzy ontology-based approach for liver fibrosis diagnosis

The domain of the digestive system is prone to severe chronic disease in the form of liver cirrhosis, which is currently a leading cause of mortality. This article presents a new intelligent system for predicting the severity of liver fibrosis in patients with chronic viral hepatitis C. The proposed system is based on the inference capabilities of fuzzy ontology and operates on semantic rule-based techniques. A fuzzy decision tree technique was employed to generate the ontology rule base using a dataset of real fibrosis cases from the Mansoura University Hospital, Egypt. These rules were then encoded into a set of fuzzy semantic rules using the fuzzy description logic format. To evaluate the system’s effectiveness, the proposed ontology was then tested on 47 chronic HCV cases, with an attempt made to see if this correctly diagnosed the patients’ conditions. The performance of the proposed system was compared with that of the now-standard Mamdani fuzzy inference system; while the latter achieved an accuracy of 95.7/%, the proposed fuzzy ontology-based system demonstrated higher performance, with 97.8% accuracy. Furthermore, the proposed system also supports semantic interoperability between clinical decision support systems and electronic health record ecosystems. The positive impacts of this system on the correct prediction of liver fibrosis severity thus suggest that it has the potential to assist medical professionals in diagnosing and treating this dangerous disease.

Ontological Method for the Modeling and Management of Building Component Construction Process Information

Knowledge of the construction process plays a decisive role in guiding construction and thus affects the quality of buildings. However, the integration and relevance of information in the traditional process file are poor, and it is easy to ignore the process changes caused by the differences when applying information directly. Therefore, this paper introduces an ontological method for the information modeling and management of the construction process. The proposed method uses machine readable language to integrate process knowledge in a structured way, thereby improving the relevance of information and promoting the reuse, sharing and retrieval of knowledge. At the same time, the semantic web rule language (SWRL) rules are used to model the relevant laws and regulations with reference to the construction product quality acceptance regulations and to correlate the construction process information so as to ensure the compliance of building products and the accuracy of process information. Finally, the feasibility of the method is verified via specific use cases on an ontology implementation platform.

EASC: An exception-aware semantic compression framework for real-world knowledge graphs

Knowledge graphs (KGs) have achieved great success in many real applications, and great efforts have been dedicated to constructing larger knowledge graphs. An obvious trend in KG construction is that the KGs become ever-increasingly bigger. However, we argue that constructing a KG by directly inserting more triples may harm the performance of the KG, and one possible solution is KG compression. In this paper, we propose an exception-aware semantic lossless compression framework EASC to compress a KG. Since many triples can be inferred from other triples with semantic rules, we remove the triples that can be inferred and store the rules and exception cases. Specifically, we formalize the lossless compression problem as a weighted set cover problem, which is NP-hard, and propose a semantic lossless compression algorithm to get an approximation result. We conduct extensive experiments on seven real-world large-scale KGs. The results show that EASC achieves state-of-the-art performance in semantic compression methods. Furthermore, by combining EASC as an independent module with syntactic compression methods, we achieve state-of-the-art performance in lossless compression methods.

An ontology-guided approach to process formation and coordination of demand-driven collaborations

Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration between firms to scale up production. This paper proposes an approach to formalise product and process requirements via a collaboration ontology and applies semantic reasoning techniques for process formation. Our approach contributes to production research by providing flexibility in coordinating firms engaged in demand-driven collaboration. The proposed approach has four core dimensions: (1) The Collaboration ontology builds on a set of product assembly requirements, process steps, their input/output resources and semantic rules; (2) the ontology reasoner derives resource dependencies between the steps; (3) the java tool interprets resource dependencies as possible transitions in Business Process Management Notation (BPMN); (4) a workflow engine executes the generated product assembly process. The approach and the ontology were validated in an industrial aerospace tendering scenario demonstrating its practical relevance for firms seeking demand-driven collaborations to react to production changes. Finally, we position and explain our contributions to the body of knowledge in collaborative production engineering.