Knowledge Representation Research Articles

A joint knowledge representation learning of sentence vectors weighting and primary neighbor constraints

A joint knowledge representation learning of sentence vectors weighting and primary neighbor constraints

Examining site intervention efficacy and uncertainties with conceptual Bayesian networks: preventing offsite migration of DNAPL and contaminated groundwater.

For contaminated sites, conceptual site models (CSMs) guide the assessment and management of risks, including remediation strategies. Recent research has expanded diagrammatic CSMs with structural causal modeling to develop what are nominally called conceptual Bayesian networks (CBNs) for environmental risk assessment. These CBNs may also be useful for problems of controlling and preventing offsite contaminant migration, especially for sites containing dense nonaqueous phase liquids (DNAPLs). In particular, the CBNs provide greater clarity on the causal relationships between source term, onsite and offsite migration, and remediation effectiveness characterization for contaminated DNAPL sites compared to traditional CSMs. These ideas are demonstrated by the inclusion of modifying variables, causal pathway analysis, and interventions in CBNs. Additionally, several new extensions of the CBN concept are explored including the representation of measurement variables as lines of evidence and alignment with conventional pictorial CSMs for groundwater modeling. Taken as a whole, the CBNs provide a powerful and adaptable knowledge representation tool for remediating subsurface systems contaminated by DNAPL.

When is it acceptable to break the rules? Knowledge representation of moral judgements based on empirical data

Constraining the actions of AI systems is one promising way to ensure that these systems behave in a way that is morally acceptable to humans. But constraints alone come with drawbacks as in many AI systems, they are not flexible. If these constraints are too rigid, they can preclude actions that are actually acceptable in certain, contextual situations. Humans, on the other hand, can often decide when a simple and seemingly inflexible rule should actually be overridden based on the context. In this paper, we empirically investigate the way humans make these contextual moral judgements, with the goal of building AI systems that understand when to follow and when to override constraints. We propose a novel and general preference-based graphical model that captures a modification of standard dual process theories of moral judgment. We then detail the design, implementation, and results of a study of human participants who judge whether it is acceptable to break a well-established rule: no cutting in line. We then develop an instance of our model and compare its performance to that of standard machine learning approaches on the task of predicting the behavior of human participants in the study, showing that our preference-based approach more accurately captures the judgments of human decision-makers. It also provides a flexible method to model the relationship between variables for moral decision-making tasks that can be generalized to other settings.

ISAND: AN INFORMATION SYSTEM FOR SCIENTIFIC ACTIVITY ANALYSIS (IN THE FIELD OF CONTROL THEORY AND ITS APPLICATIONS)

This paper describes the approaches underlying ISAND, an information system for scientific activity analysis in the field of control theory and its applications. ISAND is being developed at the Trapeznikov Institute of Control Sciences, the Russian Academy of Sciences. The ISAND ontology is oriented toward the representation and collection of knowledge in the field of control theory and its applications, namely, scientific knowledge (the ontology of control theory) and knowledge related to the scientific activity of agents (organizations, journals, conferences, and individual researchers) in this field. Based on this ontology, the ISAND architecture is a complex program system to collect, store, and analyze publications and their metadata from external sources. The ISAND algorithm for building the thematic profiles of scientific objects (publications, researchers, organizations, journals, and conferences), as well as ISAND text processing and network analysis capabilities, are presented. Finally, the main possibilities of using ISAND are considered.

A survey of ontology-enabled processes for dependable robot autonomy.

Autonomous robots are already present in a variety of domains performing complex tasks. Their deployment in open-ended environments offers endless possibilities. However, there are still risks due to unresolved issues in dependability and trust. Knowledge representation and reasoning provide tools for handling explicit information, endowing systems with a deeper understanding of the situations they face. This article explores the use of declarative knowledge for autonomous robots to represent and reason about their environment, their designs, and the complex missions they accomplish. This information can be exploited at runtime by the robots themselves to adapt their structure or re-plan their actions to finish their mission goals, even in the presence of unexpected events. The primary focus of this article is to provide an overview of popular and recent research that uses knowledge-based approaches to increase robot autonomy. Specifically, the ontologies surveyed are related to the selection and arrangement of actions, representing concepts such as autonomy, planning, or behavior. Additionally, they may be related to overcoming contingencies with concepts such as fault or adapt. A systematic exploration is carried out to analyze the use of ontologies in autonomous robots, with the objective of facilitating the development of complex missions. Special attention is dedicated to examining how ontologies are leveraged in real time to ensure the successful completion of missions while aligning with user and owner expectations. The motivation of this analysis is to examine the potential of knowledge-driven approaches as a means to improve flexibility, explainability, and efficacy in autonomous robotic systems.

Functional integration and segregation during semantic cognition: Evidence across age groups

Semantic cognition is underpinned by ventral anterior temporal lobe (vATL) which encodes knowledge representations and inferior frontal gyrus (IFG), which controls activation of knowledge based on the needs of the current context. This core semantic network has been validated in substantial empirical findings in the past. However, it remains unclear how these core semantic areas dynamically communicate with each other, and with other neural networks, to achieve successful semantic processing. Here, we investigated this question by testing functional connectivity in the core semantic network during semantic tasks and whether these connections were affected by cognitive ageing. Compared to a non-semantic task, semantic tasks increased the connectivity between left and right IFGs, indicating a bilateral semantic control system. Strengthened connectivity was also found between left IFG and left vATL, and this effect was stronger in the young group. At a whole-brain scale, IFG and vATL increased their coupling with multiple-demand regions during semantic tasks, even though these areas were deactivated relative to non-semantic tasks. This suggests that the domain-general executive network contributes to semantic processing. In contrast, IFG and vATL decreased their interaction with default mode network (DMN) areas during semantic tasks, even though these areas were positively activated by the task. This suggests that DMN areas do not contribute to all semantic tasks: their activation may sometimes reflect automatic retrieval of task-irrelevant memories and associations. Taken together, our study characterizes a dynamic connectivity mechanism supporting semantic cognition within and beyond core semantic regions.

A task‐centric knowledge graph construction method based on multi‐modal representation learning for industrial maintenance automation

AbstractMaintenance manuals are crucial information sources for maintenance and repair. Prior studies explored factual knowledge extraction from textual documents. However, maintenance knowledge in manuals is more task‐centric rather than factual knowledge and often documented in an unstructured Portable Document Format (PDF), posing challenges for knowledge extraction. Addressing this, this research develops effective methods to extract task‐centric maintenance knowledge from unstructured PDF manuals. A new Task‐centric Knowledge Graph (TCKG) schema centralized on maintenance task components (MTCs) is proposed to address the need for structured knowledge representation. A method (Heterogeneous Graph‐based Method, HGM) for knowledge extraction is then proposed, which is enhanced by incorporating visual and spatial information. In the experiments, the proposed HGM exhibits robust performance in the knowledge extraction process, surpassing the baseline Graph‐based Interaction Model with a Tracker (GIT) method in MTCs extraction by 13.3%, and the baseline Translate Embedding (TransE) method in MTCs' relation extraction by 3.8%. A series of ablation studies also prove that including visual and spatial information through the proposed method can improve the relation extraction performance by over 10%. This research supplies valuable insights for future developments in information extraction from maintenance manuals.

A Study on the Design of Knee Exoskeleton Rehabilitation Based on the RFPBS Model.

The gait rehabilitation knee exoskeleton is an advanced rehabilitative assistive device designed to help patients with knee joint dysfunction regain normal gait through training and activity support. This paper introduces a design framework based on the process knowledge representation method to optimize the design and control efficiency of the knee exoskeleton. This framework integrates knowledge of design objects and processes, specifically including requirements, functions, principle work areas, and the representation and multi-dimensional dynamic mapping of the Behavior-Structure (RFPBS) matrix, achieving multi-dimensional dynamic mapping of the knee exoskeleton. This method incorporates biomechanical and physiological knowledge from the rehabilitation process to more effectively simulate and support gait movements during rehabilitation. Research results indicate that the knee rehabilitation exoskeleton design, based on the RFPBS process knowledge representation model, accomplishes multi-dimensional dynamic mapping, providing a scientific basis and effective support for the rehabilitation of patients with knee joint dysfunction.

Knowledge Representation of Supply and Demand Through Data Visualization: Assessing Usability Using Dual Method

Knowledge Representation of Supply and Demand Through Data Visualization: Assessing Usability Using Dual Method

Challenges for Non-Classical Reasoning in Contemporary AI Applications

In knowledge representation and reasoning, a key area in artificial intelligence research, non-classical logics play a prominent double role: firstly, non-classical logic languages allow for a precise and transparent encoding of domain specific knowledge. Secondly, as the logical languages are equipped with custom-tailored rules of logical inference, they make available a principled approach to derive new knowledge from previous information. In practice, the first aspect addresses data storage and retrieval, the second aspect the utilization of available information. This article briefly surveys contemporary challenges of NCL research in AI.

Federated learning for spiking neural networks by hint-layer knowledge distillation

In recent years, research on the federated Spiking Neural Network (SNN) has attracted increasing attention because of its advantages of low-power consumption and privacy security. However, existing federated SNN researches rely primarily on the Federated Average (FedAvg) strategy, transferring full network parameters between the server and clients and incurring substantial communication. To address this issue, we propose a Hint-layer Distillation-based Spiking Federated Learning (HDSFL) framework that reduces the communication cost by transferring knowledge and losslessly compressing the spiking tensor. To compensate for the information loss due to the binary representation of spikes in knowledge distillation, we introduce the hint-layer information instead of just soft-label distribution. To aggregate the knowledge effectively across clients on the server, we process the weighted knowledge aggregation on the spiking knowledge representation based on local performance. The experimental results on four classical and large-scale benchmarks show that our method reduces the communication cost by about 1–2 orders of magnitude compared to conventional methods, while achieving comparable accuracies. Especially on the CIFAR-10 dataset, our method achieves the same accuracy as FedAvg using only 20% of the communications, and consumes only 5.8% communications of FedAvg in a single round.

Knowledge Representation and Knowledge Reasoning in Square{most} and Square{all}

Making much of reduction operations, this paper firstly proves the validity of the generalized syllogism 1,EMO− and then infers the other 14 valid generalized ones from EMO−1. That is to say that these syllogisms are reducible. The reason why these syllogisms are reducible is that the quantifiers in Square{all} can be mutually defined, and that so can the quantifiers in Square{most}. All proofs in this paper are deductive reasoning. All proofs in this paper are deductive reasoning, therefore this work is consistent. It is hoped that this paper not only benefits the study of modern logic, but also further development of inference machines.

The method of linear-logical operators and logical equations in information extraction tasks

Relational and logical methods of knowledge representation play a key role in creating a mathematical basis for information systems. Predicate algebra and predicate operators are among the most effective tools for describing information in detail. These tools make it easy to formulate formalized information, create database queries, and simulate human activity. In the context of the new need for reliable and efficient data selection, a problem arises in deeper analysis. Subject of the study is the theory of quantum linear equations based on the algebra of linear predicate operations, the formal apparatus of linear logic operators and methods for solving logical equations in information extraction tasks. Aim of the study is a developing of a method for using linear logic operators and logical equations to extract information. This approach can significantly optimize the process of extracting the necessary information, even in huge databases. Main tasks: analysis of existing approaches to information extraction; consideration of the theory of linear logic operators; study of methods for reducing logic to an algebraic form; analysis of logical spaces and the algebra of finite predicate actions and the theory of linear logic operators. The research methods involve a systematic analysis of the mathematical structure of the algebra of finite predicates and predicate functions to identify the key elements that affect the query formation process. The method of using linear logic operators and logical equations for information extraction is proposed. The results of the study showed that the method of using linear logic operators and logical equations is a universal and adaptive tool for working with algebraic data structures. It can be applied in a wide range of information extraction tasks and prove its value as one of the possible methods of information processing. Conclusion. The paper investigates formal methods of intelligent systems, in particular, ways of representing knowledge in accordance with the peculiarities of the field of application and the language that allows encoding this knowledge for storage in computer memory. The proposed method can be implemented in the development of language interfaces for automated information access systems, in search engine algorithms, for logical analysis of information in databases and expert systems, as well as in performing tasks related to object recognition and classification.

Exploring the Frontiers of Artificial Intelligence: A Comprehensive Analysis

Artificial Intelligence (AI) has emerged as a transformative force reshaping industries, societies, and human experiences. This in-depth written work delves into the multifaceted landscape of AI, examining its history, key concepts, ethical implications, current applications, and future directions. Drawing upon a diverse array of research, literature, and expert insights, this written work aims at providing a comprehensive understanding of AI’s evolution, challenges, and potentials. In doing this, intelligent agent, problem solving and search; and knowledge representation and reasoning are discussed as the fundamental concepts of AI. Furthermore, some main frontiers of AI like machine learning, ethical considerations in artificial intelligence and the socio-economic impact of AI are highlighted. It is noted in the discourse that the future belongs to AI and as such, humanity should make proper utilization of the phenomenon for the good of mankind. Finally, recommendations are made, among which is collaboration among critical stakeholders of Artificial Intelligence in maximizing its usage.

Interval Methods in Knowledge Representation

Interval Methods in Knowledge Representation

Knowledge reuse for ontology modelling and application of maintenance motion state sequence

With the current digital transformation and the development of complex manufacturing systems, advanced maintenance is proposed to improve the competitiveness of complex products, generating a large amount of heterogeneous maintenance data and information. There is a lack of standardized representations of motion-centred maintenance knowledge which leads to semantic ambiguity and poor intertranslatability. In addition, it causes subjective deviations and human resource investments in related time prediction applications. Therefore, a knowledge reuse method for ontology modelling and the application of maintenance motion state sequences is proposed. First, a framework for reusing maintenance motion state sequence (MMSS) knowledge is established, which is defined as the state sets of time-sequence maintenance motion. Second, maintenance motion state sequence ontology (MMSSO) is constructed to standardize the definition of MMSS, as a supplement to the current maintenance ontologies. Third, an MMSSO application for automatic maintenance time prediction is proposed by incorporating the standardized specifications of MMSSO and improving the MODAPTS method. Finally, using aviation equipment as an example, the rationality and superiority of MMSSO in real applications are verified. MMSSO is a new practice of integrating multi-source information in advanced maintenance. It can also provide predicted time as an iterative reference for industrial practitioners in the digital design stage.

Implementation of differentiated learning with a Culturally Responsive Teaching approach to increase students' interest in learning

Implementing learning aligned with the Culturally Responsive Teaching (CRT) approach is a method enabling a more accurate comprehension and representation of knowledge, tailored to students' individual learning tendencies and styles, while considering the diverse cultures surrounding them. Transitioning from the old paradigm to adopting learning in accordance with the demands of the 21st century and the current curriculum is imperative, despite the uneven distribution of the Independent Curriculum across schools. This study is prompted by students' lack of interest in learning, evident in their limited engagement in the learning process. The objective of this research is to illustrate the impact of employing differentiated learning with a Culturally Responsive Teaching (CRT) approach on enhancing students' learning interest. The research methodology employed was classroom action research conducted over two cycles. The research process was executed collaboratively, employing a lesson study approach and utilizing qualitative descriptive methods, with data collection techniques comprising observation and questionnaires. Research findings demonstrate an increase in students' learning interest through the implementation of differentiated learning with a Culturally Responsive Teaching (CRT) approach. This is evidenced by the average N-Gain value for learning interest in Cycle I, which was 0.4029, classified as medium, and in Cycle II, it reached 0.7014, classified as high. Thus, differentiated learning integrating the Culturally Responsive Teaching (CRT) approach has been proven highly effective in establishing a more inclusive and meaningful learning atmosphere within the classroom

M-XAF: Medical explainable diagnosis system of atrial fibrillation based on medical knowledge and semantic representation fusion

BackgroundElectrocardiogram (ECG) is a critical diagnostic tool used for screening atrial fibrillation (AF). In recent years, several deep learning-based ECG automatic diagnosis methods have been proposed and have yielded satisfactory results. However, the lack of interpretability in these systems limits their clinical usage. MethodsTo address this issue, we propose a medical explainable AF diagnosis system (M-XAF) that not only predicts AF diagnostic results but also generates an analysis report of the ECG medical features that influenced the classification model's decision. Using a novel ECG signal features occlusion analysis method, M-XAF establishes a connection between the medical knowledge space and the representation space from the convolutional neural network (CNN) by extracting understandable semantic sensitive channels (SSCs) via gradient-weighted channel sensitivity. Through the visualization and statistical modeling of SSCs, M-XAF provides multimodal explanations, including ECG feature descriptions, regions of interest (ROI), and class activation mapping (CAM), for AF diagnostic results. ConclusionThe experimental results demonstrate that the M-XAF has achieved satisfactory results in the AF identification task, with accuracy, specificity, PPV, F1, and MCC reaching over 0.9715, 0.9896, 0.9778, 0.9567, and 0.9365 on two public ECG datasets. In addition, M-XAF generates quantitative explainable reports and CAM for each predicted sample from the perspective of classification models. Furthermore, the explainable reports provided by M-XAF can address the high-confidence problem in classification models. Ultimately, the explainable framework in M-XAF can be widely applied in AI-ECG classification scenarios.

Integrated representation of technical systems with BIM and linked data: TUBES system ontology

Technical systems in the Architecture, Engineering, Construction, and Operations (AECO) industry are complex interconnected structures. To cope with this high complexity, large amounts of data must be processed and exchanged throughout the whole life cycle of these systems. Building Information Modeling (BIM) and Semantic Web Technologies (SWT) offer the possibility to structure and link this data as well as to define common terminology. Semantic Web ontologies describing HVAC systems in the AECO industry have been developed, but an ontology for an integrated representation based on general system theory is missing. Therefore, this study proposes the TUBES System Ontology (TSO) as a knowledge representation for an integrated, explicit representation of linked technical systems, their hierarchical subdivision, structural and functional interconnections, and relationships to spatial entities. The contribution is validated on two example models, and the results are critically discussed, pointing out corresponding limitations as well as the need for further research.

Medical-informed machine learning: integrating prior knowledge into medical decision systems

BackgroundClinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML.MethodsThe study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models.ResultsThe findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios.ConclusionsBy illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.