Knowledge Representation Framework Research Articles

PrimeNet: A Framework for Commonsense Knowledge Representation and Reasoning Based on Conceptual Primitives

PrimeNet: A Framework for Commonsense Knowledge Representation and Reasoning Based on Conceptual Primitives

A Condition Knowledge Representation and Feedback Learning Framework for Dynamic Optimization of Integrated Energy Systems.

An optimal energy scheduling strategy for integrated energy systems (IESs) can effectively improve the energy utilization efficiency and reduce carbon emissions. Due to the large-scale state space of IES caused by uncertain factors, it would be beneficial for the model training process to formulate a reasonable state-space representation. Thus, a condition knowledge representation and feedback learning framework based on contrastive reinforcement learning is designed in this study. Considering that different state conditions would bring inconsistent daily economic costs, a dynamic optimization model based on deterministic deep policy gradient is established, so that the condition samples can be partitioned according to the preoptimized daily costs. In order to represent the overall conditions on a daily basis and constrain the uncertain states in the IES environment, the state-space representation is constructed by a contrastive network considering the time dependence of variables. A Monte-Carlo policy gradient-based learning architecture is further proposed to optimize the condition partition and improve the policy learning performance. To verify the effectiveness of the proposed method, typical load operation scenarios of an IES are used in our simulations. The human experience strategies and state-of-the-art approaches are selected for comparisons. The results validate the advantages of the proposed approach in terms of cost effectiveness and ability to adapt in uncertain environments.

A Framework for a Persistent Representation of Customer Knowledge to Enhance Product Development

A Framework for a Persistent Representation of Customer Knowledge to Enhance Product Development

Cognitive Structure Analysis: A Technique For Assessing What Students Know, Not Just How They Perform

Assessment has been a key part of education, playing the role of determining how much students have learned. Traditionally, assessments have focused on whether students give the correct answer to problems, implying that the number of correctly-answered test items is a valid measure of how much students know. Unfortunately, the focus on correct answers has also resulted in neglecting the potential ability of assessments to provide diagnostic feedback to educators as to what concepts students have mastered and where the gaps in their knowledge are,thus potentially informing the day-to-day teaching process. The present paper describes an assessment technique called Cognitive Structure Analysis that is derived from John Leddo’s integrated knowledge structure framework (Leddo et al., 1990) that combines several prominent knowledge representation frameworks in cognitive psychology. Using a Google Form, students from Pakistan were queried on four types of knowledge considered the basis of mastery of Algebra 1 concepts: factual, procedural, strategic, and rationale. From students’ responses to these queries, measures of each type of knowledge and a combined knowledge score were created. Students were also given problems to solve. Correlations between each knowledge component score and problem-solving performance were high and the correlation between overall CSA-assessed knowledge and problem-solving performance was .86. Results suggest that CSA can be both easily implemented and highly diagnostic of student learning needs. Future research can investigate CSA’s robustness across other subjects and whether incorporating CSA as part of day-to-day classroom instruction can lead to higher student achievement.

A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports.

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians' writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge.

Knowledge Graph-Based Prediction of Potentially Inappropriate Medication

To improve the accuracy of potentially inappropriate medication (PIM) prediction, a PIM prediction model that combines knowledge graph and machine learning was proposed. Firstly, based on Beers criteria 2019 and using the knowledge graph as the basic structure, a PIM knowledge representation framework with logical expression capabilities was constructed, and a PIM inference process was implemented from patient information nodes to PIM nodes. Secondly, a machine learning prediction model for each PIM label was established based on the classifier chain algorithm, to learn the potential feature associations from the data. Finally, based on a threshold of sample size, a portion of reasoning results from the knowledge graph was used as output labels on the classifier chain to enhance the reliability of the prediction results of low-frequency PIMs. 11 741 prescriptions from 9 medical institutions in Chengdu were used to evaluate the effectiveness of the model. Experimental results show that the accuracy of the model for PIM quantity prediction is 98.10%, the F1 is 93.66%, the Hamming loss for PIM multi-label prediction is 0.06%, and the macroF1 is 66.09%, which has higher prediction accuracy than the existing models. The method proposed has better prediction performance for potentially inappropriate medication and significantly improves the recognition of low-frequency PIM labels.

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

To effectively manage the heterogeneous and discrete knowledge of the bridge maintenance domain, this study adopts knowledge graph technology to build a knowledge base of bridge maintenance, called the bridge maintenance knowledge graph (BMKG). The BMKG uses an ontology as the knowledge organization and representation framework and a graph database as the knowledge storage tool. To facilitate the construction of the BMKG, a hybrid method combining a top-down approach and a bottom-up approach is proposed. Firstly, a bridge maintenance domain ontology (BMDO) is coded with Protégé and represented in Web Ontology Language. Secondly, rule reasoning and ontology reasoning are implemented on the BMDO in Protégé in order to automatically complete missing relations or attribute values. Thirdly, ontology reasoning is adopted to perform consistency check on the BMDO. Lastly, the BMDO model is stored in the Neo4j graph database through data format conversion, thus completing the construction of the BMKG. The BMKG is applied in a typical scenario of bridge maintenance to demonstrate its application value. Results show that the proposed hybrid method can create a knowledge graph that can realize the transformation from discrete data into interconnected knowledge. Knowledge graph offers a novel idea to create a knowledge base in the bridge maintenance domain.

Theoretical Framework of Knowledge Representation for Information Sharing

Since information from enterprises should be shared and exchanged in order to be understood and recognized, whereby unambiguous and transparent data is considered a vital requirement, information sharing has become crucial to the correct use of information assets. Sharing information may be seen as the most significant component of a company. Sharing or integrating information is used to bring together seemingly unrelated bodies of knowledge in an effort to enhance creativity. Development and training programs, reports, Information Technology (IT) platforms, official papers, and collaborative teams are all instances of information integration. It is possible to boost product and service quality, customer service responsiveness, innovation, and environmental sustainability via pervasive information integration. In this article, we take a look back at the revolutionary idea underpinning internal communication networks for Knowledge Management (KM), and Knowledge Representation (KR).

COGNITIVE STRUCTURE ANALYSIS: ASSESSING ELEMENTARY SCHOOL STUDENTS IN MATH TO DETERMINE THE TYPES OF KNOWLEDGE THEY HAVE

Assessment has been a key part of education, playing the role of determining how much students have learned. Traditionally, assessments have focused on whether students give the correct answer to problems, implying that the number of correctly answered test items is a valid measure of how much students know. Unfortunately, the focus on correct answers has also resulted in neglecting the potential ability of assessments to provide diagnostic feedback to educators as to what concepts students have mastered and where the gaps in their knowledge are, thus potentially informing the day-to-day teaching process. The present paper describes an assessment technique called Cognitive Structure Analysis (CSA) that is derived from John Leddo’s integrated knowledge structure framework (Leddo et al., 1990) that combines several prominent knowledge representation frameworks in cognitive psychology. Previous studies with middle and high school students revealed strong correlations between students’ factual, procedural, strategic and rationale subject matter knowledge and their problem solving performance. Given that the Leddo et al. (1990) research showed that all four types of knowledge were present in highly experienced people but that novices tended to be characterized by factual and procedural knowledge, the question arises as to whether elementary school students would show significant correlations between only factual and procedural knowledge and problem solving performance on school subject matter due to their lack of academic experience. The present study investigated this question using fifth graders studying math. 20 fifth graders were taught beginning and advanced concepts in solving distance = rate * time problems. Afterwards, they were assessed on their concept knowledge using CSA and given practical problems to solve. Results showed significant correlations between factual and procedural knowledge as assessed by CSA and problem solving performance. Correlations between strategic and rationale knowledge were not statistically significant. These results suggest that elementary school students not only have less subject matter knowledge than their older counterparts, but that the type of knowledge they have is also qualitatively different. This finding may have implications for how to teach students in order to nurture advanced thinking skills.

A novel smart product-service system configuration method for mass personalization based on knowledge graph

Nowadays, manufacturing enterprises are struggling to satisfy personalized and dynamic user requirements. Smart Product-service System (Smart PSS), as a promising and sustainable business model, can respond to personalized and dynamic demands through effective configuration/reconfiguration processes. Mass personalization (MP), which emphasizes diversified values to improve user experience by considering both functional and affective elements, can significantly contribute to Smart PSS. However, Smart PSS field still lacks a unified knowledge representation framework and an effective configuration method in the MP context. Aiming to fulfill the gap, this research proposes a novel Smart PSS configuration method oriented to MP to meet personalized and dynamic requirements of users, especially satisfy affective and functional requirements simultaneously throughout the reconfiguration life-cycle. Due to the extreme complexity of heterogeneous data from Smart PSS and MP, Knowledge Graph (KG) is introduced as a powerful tool for managing intricate system knowledge. The common schemas of three KGs are constructed as the basis of configuration, with design knowledge, related field knowledge, and user information integrated comprehensively. KG-based question answering is combined with similarity calculation as a hybrid method in the configuration process to provide satisfactory and complete solutions. Moreover, the configuration process is placed in the reconfiguration life-cycle of Smart PSS to deal with dynamic changes through the latest knowledge in updating KGs. Furthermore, an illustrative case study of the intelligent management system for family medication is demonstrated. Through the comparison with other approaches, the proposed method is proved to be valuable for the implementation of Smart PSS configuration for MP.

Aspect of the Knowledge Representation Framework (KR Framework) for Offering the Basis for a Number of Research Activities in the Caisor

The representation of knowledge refers to the technical challenge of capturing human understanding and thinking in a conceptual framework that enables computer networks to handle information. This research study is aimed to critically analyse the impact of the knowledge representation framework on the efficiency level of CAISOR activity. The research study has provided a sustainable viewpoint about the way KRF presents visualization data that is easy to understand for the human as well as the CAISOR agenda. A wide variety of different initiatives are founded on the CAISOR agenda through the Knowledge Representation Framework. The KR model covers many features of structured information representation ranging from simple facts to sophisticated formulations and programs. In addition, the research study has followed a secondary qualitative data collection method to collect the secondary sources of data regarding the research topic. Keyword : CAISOR, framework, AI system, Knowledge representation framework.

The Core of Smart Cities: Knowledge Representation and Descriptive Framework Construction in Knowledge-Based Visual Question Answering

Visual question answering (VQA), which is an important presentation form of AI-complete task and visual Turing tests, coupled with its potential application value, attracted widespread attention from both researchers in computer vision and natural language processing. However, there are no relevant research regarding the expression and participation methods of knowledge in VQA. Considering the importance of knowledge for answering questions correctly, this paper analyzes and researches the stratification, expression and participation process of knowledge in VQA and proposes a knowledge description framework (KDF) to guide the research of knowledge-based VQA (Kb-VQA). The KDF consists of a basic theory, implementation methods and specific applications. This paper focuses on describing mathematical models at basic theoretical levels, as well as the knowledge hierarchy theories and key implementation behaviors established on this basis. In our experiment, using the statistics of VQA’s accuracy in the relevant literature, we propose a good corroboration of the research results from knowledge stratification, participation methods and expression forms in this paper.

An ontology-driven framework for knowledge representation of digital extortion attacks

With the COVID-19 pandemic and the growing influence of the Internet in critical sectors of industry and society, cyberattacks have not only not declined, but have risen sharply. In the meantime, ransomware is at the forefront of the most devastating threats that have launched the lucrative illegal business. Due to the proliferation and variety of ransomware forays, there is a need for a new theory of categories. The intricacy and multiplicity of components involved in digital extortions entails the construction of a knowledge representation system that is able to organize large volumes of information from heterogeneous sources in a formal structured format and infer new knowledge from it. This paper suggests and develops a dedicated ontology of digital blackmails, called Rantology, with a particular focus on ransomware assaults. The logic coded in this ontology allows to assess the maliciousness of programs based on various factors, including called API functions and their behaviors. The proposed framework can be used to facilitate interoperability between cybersecurity experts and knowledge-based systems, and identify sensitive points for surveillance. The evaluation results based on several criteria confirm the adequacy of the suggested ontology in terms of clarity, modularity, consistency, coverage and inheritance richness.

Comparison of biomedical relationship extraction methods and models for knowledge graph creation

Biomedical research is growing at such an exponential pace that scientists, researchers, and practitioners are no more able to cope with the amount of published literature in the domain. The knowledge presented in the literature needs to be systematized in such a way that claims and hypotheses can be easily found, accessed, and validated. Knowledge graphs can provide such a framework for semantic knowledge representation from literature. However, in order to build a knowledge graph, it is necessary to extract knowledge as relationships between biomedical entities and normalize both entities and relationship types. In this paper, we present and compare a few rule-based and machine learning-based (Naive Bayes, Random Forests as examples of traditional machine learning methods and DistilBERT, PubMedBERT, T5, and SciFive-based models as examples of modern deep learning transformers) methods for scalable relationship extraction from biomedical literature, and for the integration into the knowledge graphs. We examine how resilient are these various methods to unbalanced and fairly small datasets. Our experiments show that transformer-based models handle well both small (due to pre-training on a large dataset) and unbalanced datasets. The best performing model was the PubMedBERT-based model fine-tuned on balanced data, with a reported F1-score of 0.92. The distilBERT-based model followed with an F1-score of 0.89, performing faster and with lower resource requirements. BERT-based models performed better than T5-based generative models.

Geoscience Knowledge Graph (GeoKG): Development, construction and challenges

AbstractBig earth data is a cross‐domain of geoscience and information science, which provides a novel perspective for solving geoscience problems. Most contemporary research is driven by data but neglect the potential value of knowledge. As a new scientific language in Geoscience, GeoKG is essential for understanding, representing, and mining geoscience knowledge, and can contribute to the integration of big earth data, geoscience knowledge, and geoscience models. However, research on GeoKG lack spatiotemporal perspectives in knowledge cognition, representation, acquisition and management. To this end, this article first outlines a cognitive mechanism from the human–machine double perspective and categorizes the characteristics and content of geoscience knowledge. To express evolution and complex natural rules, a knowledge representation framework is proposed through ‘state‐process’ and ‘condition‐result’ models. Aiming at multimodal data, a workflow is put forward to acquire knowledge from a small sample, a knowledge graph, a map, and a schematic diagram. Furthermore, we discuss the organization of GeoKG by improving existing data models, developing spatiotemporal correlation indexing and advancing knowledge graph completion. The concrete construction process of GeoKG is analyzed thoroughly in this study, which can support the synthetic analysis of big earth data, promote the development of knowledge engineering and provide a foundation for improving intelligent geoscience.

A hierarchical assembly knowledge representation framework and microdevice assembly ontology

Microdevice assembly knowledge is dispersed in different product development phases, such as assembly design, assembly simulation and assembly process, and a lot of essential knowledge is implicit and heterogeneous. It is difficult for researchers and computer-aided systems to share and reuse different assembly knowledge quickly and accurately, leading to inefficient and inaccurate assembly process planning. To integrate and structurally represent the assembly design knowledge, assembly simulation knowledge and assembly process knowledge of microdevice, this paper proposes a hierarchical assembly knowledge representation framework and develops a microdevice assembly ontology. There are four layers in the framework, including the organizational structure, the structural relationship, the assembly accuracy, and the process characteristics. The assembly design knowledge that is integrated involves the basic properties of the assembly object as well as the spatial, mating, and assembly relationship, etc. Assembly simulation knowledge refers to the permissible range of assembly force and contact force. Knowledge of assembly processes comprises assembly sequence and operating method of the part. The microdevice assembly ontology is developed based on METHONTOLOGY, and implemented with Protégé. The corresponding SWRL rules have been established to inference the implicit knowledge in assembly design. An ignition target assembly knowledge model based on the microdevice assembly ontology is constructed. In the assembly task of the ignition target, engineers can quickly and accurately access the required assembly knowledge from the ignition target assembly knowledge model, thus verifying the integrity and validity of the microdevice assembly ontology.

Linguistic patterns as a framework for an expert knowledge representation in agent movement simulation

The article shows how the concept of linguistic patterns (LP) combined with expert knowledge can be used to model the dynamics of social groups. Linguistic expressions similar to natural language are the basis for generating virtual forces (VF) that govern the movements of the individual agent. Using logical sentences and providing the methodology to determine the degree of their truth led to the development of rules of agent behavior. Such an approach allows for constructing flexible simulation models. This paper describes this new idea in detail and illustrates its capabilities and properties by simple examples. Furthermore, a series of simulation experiments involving problems of known solutions with stable final configurations is performed and qualitatively analyzed. For additional validation, a suboptimal scattered plot generation method proposed by Drezner is used. A set of simulations for classic problems showed the convergence of agent dynamic behavior in the proposed framework with the solutions provided by the Drezner approach.

Dual coding of knowledge in the human brain.

How does the human brain code knowledge about the world? While disciplines such as artificial intelligence represent world knowledge based on human language, neurocognitive models of knowledge have been dominated by sensory embodiment, in which knowledge is derived from sensory/motor experience and supported by high-level sensory/motor and association cortices. The neural correlates of an alternative disembodied symbolic system had previously been difficult to establish. A recent line of studies exploring knowledge about visual properties, such as color, in visually deprived individuals converge to provide positive, compelling evidence for non-sensory, language-derived, knowledge representation in dorsal anterior temporal lobe and extended language network, in addition to the sensory-derived representations, leading to a sketch of a dual-coding knowledge neural framework.

MecCog: a knowledge representation framework for genetic disease mechanism.

Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams and network graphs. Integration and structuring of such mechanistic information greatly enhances its utility. MecCog is a graphical framework for building integrated representations (mechanism schemas) of mechanisms by which a genetic variant causes a disease phenotype. A MecCog mechanism schema displays the propagation of system perturbations across stages of biological organization, using graphical notations to symbolize perturbed entities and activities, hyperlinked evidence tagging, a mechanism ontology and depiction of knowledge gaps, ambiguities and uncertainties. The web platform enables a user to construct, store, publish, browse, query and comment on schemas. MecCog facilitates the identification of potential biomarkers, therapeutic intervention sites and critical future experiments. The MecCog framework is freely available at http://www.meccog.org. Supplementary data are available at Bioinformatics online.

Representation and Presentation of Culinary Tradition as Cultural Heritage

This paper presents a knowledge representation framework and provides tools to allow the representation and presentation of the tangible and intangible dimensions of culinary tradition as cultural heritage including the socio-historic context of its evolution. The representation framework adheres to and extends the knowledge representation standards for the Cultural Heritage (CH) domain while providing a widely accessible web-based authoring environment to facilitate the representation activities. In strong collaboration with social sciences and humanities, this work allows the exploitation of ethnographic research outcomes by providing a systematic approach for the representation of culinary tradition in the form of recipes, both in an abstract form for their preservation and in a semantic representation of their execution captured on-site during ethnographic research.