Knowledge Representation Scheme Research Articles

ISoLIM: a similarity-based spatial prediction software for the big data era

ABSTRACT Spatial prediction software is a valuable tool for predicting the spatial distribution of geographic variables and have a wide applicability with numerous non-expert users. However, as the input data to spatial prediction tasks gets more various in sources and larger in volume in the big data era, existing software are faced with challenges when processing such data on personal computers, which is one of the main platforms for running the software. This study uses the design and implementation of a similarity-based spatial prediction desktop software, the intelligent Soil Land Inference Model (iSoLIM), as an example to enable existing software to easily execute spatial prediction tasks with multi-source and large-volume data. The implemented similarity-based method was originally designed for digital soil mapping and has been applied to many other geographic variables. In the presented software, an expandable knowledge representation schema is adopted to intelligently manage multi-source knowledge data and enable knowledge integration; block division-based parallelization is adopted for intelligent computation arrangement on large-volume data. Experiments attest that, compared to existing software, iSoLIM can achieve higher accuracy from multi-source knowledge data by knowledge integration; and shows higher efficiency when processing data in large volume.

Strategies of Content Knowledge Representation and EFL Learners‘ English Writing Proficiency: Mediating Role of Critical Thinking Skills

The exploration of optimizing content knowledge representation and enhancing critical thinking capabilities in English as Foreign Language (EFL) learners’ writing still needs to be improved. Cultivating adept critical thinking skills and effective content knowledge representation is paramount for EFL learners, given its correlation with heightened writing proficiency. This study sought to examine critical thinking skills as a mediator in the correlation between strategies of content knowledge representation and writing proficiency among EFL learners. A total of 360 English as foreign language participants engaged in the California Critical Thinking Skills Test and responded to a questionnaire on strategies of content knowledge representation conducted at Kaifeng Science Technology and Communication College, China. Subsequently, participants undertook a writing task, and the outcomes underwent analysis using SPSS 26.0, incorporating descriptive analysis, correlational analysis, and AMOS 24.0 for mediating analysis. The findings unveiled a correlation between critical thinking skills and EFL learners’ writing proficiency, echoing the association observed between content knowledge representation strategies and writing proficiency. Furthermore, critical thinking skills were identified as a mediator, linking content knowledge representation strategies to writing proficiency among EFL learners. The combination of content knowledge representation and critical thinking skills emerged as a positive role in the writing process. This research underscored the paramount significance of critical thinking skills within a specialized discipline, highlighting their transformative potential for EFL learners’ writing competency and accentuating their functions in the broader writing pedagogy.

Unsupervised Transformer-Based Anomaly Detection in ECG Signals

Anomaly detection is one of the basic issues in data processing that addresses different problems in healthcare sensory data. Technology has made it easier to collect large and highly variant time series data; however, complex predictive analysis models are required to ensure consistency and reliability. With the rise in the size and dimensionality of collected data, deep learning techniques, such as autoencoder (AE), recurrent neural networks (RNN), and long short-term memory (LSTM), have gained more attention and are recognized as state-of-the-art anomaly detection techniques. Recently, developments in transformer-based architecture have been proposed as an improved attention-based knowledge representation scheme. We present an unsupervised transformer-based method to evaluate and detect anomalies in electrocardiogram (ECG) signals. The model architecture comprises two parts: an embedding layer and a standard transformer encoder. We introduce, implement, test, and validate our model in two well-known datasets: ECG5000 and MIT-BIH Arrhythmia. Anomalies are detected based on loss function results between real and predicted ECG time series sequences. We found that the use of a transformer encoder as an alternative model for anomaly detection enables better performance in ECG time series data. The suggested model has a remarkable ability to detect anomalies in ECG signal and outperforms deep learning approaches found in the literature on both datasets. In the ECG5000 dataset, the model can detect anomalies with 99% accuracy, 99% F1-score, 99% AUC score, 98.1% recall, and 100% precision. In the MIT-BIH Arrhythmia dataset, the model achieved an accuracy of 89.5%, F1 score of 92.3%, AUC score of 93%, recall of 98.2%, and precision of 87.1%.

An Approach to Task Representation Based on Object Features and Affordances.

Multi-purpose service robots must execute their tasks reliably in different situations, as well as learn from humans and explain their plans to them. We address these issues by introducing a knowledge representation scheme to facilitate skill generalization and explainability. This scheme allows representing knowledge of the robot’s understanding of a scene and performed task. We also present techniques for extracting this knowledge from raw data. Such knowledge representation and extraction methods have not been explored adequately in previous research. Our approach does not require any prior knowledge or 3D models of the objects involved. Moreover, the representation scheme is easy to understand for humans. The system is modular so that new recognition or reasoning routines can be added without changing the basic architecture. We developed a computer vision system and a task reasoning module that works with our knowledge representation. The efficacy of our approach is demonstrated with two different tasks: hanging items on pegs and stacking one item on another. A formalization of our knowledge representation scheme is presented, showing how the system is capable of learning from a few demonstrations.

Multi-level Image Classification Using Fuzzy Petri Net

For a multi-level image classification, a knowledge representation scheme based on Fuzzy Petri Net with fuzzy inference algorithms is used. A simple graphical Petri net notation and a welldefined semantics displaying the process of reasoning through inference trees are used for visualization of the knowledge base and explanations of derived conclusion. Used knowledge representation formalism has the ability to show a probability of concepts and relations. The procedures of image multi-level classification using fuzzy recognition and inheritance algorithms on a knowledge representation scheme, as well as experimental results of image semantic interpretation, are presented.

Existence, Hypotheses and Categories in Knowledge Representation

Cognitive architectures employ different means for knowledge representation. In this work, we describe how the Cognitive Systems Toolkit (CST), a toolkit for the construction of cognitive architectures addresses the issue of knowledge representation, by introducing the notion of a computational idea, as being an abstract and generic building block for representing multiple different pieces of knowledge. We particularly address how computational ideas can be used to represent both facts that really happened at an environment and just hypothesis that are not to be considered as being a part of existence, explaining how these are instances of general categories. At the end, we provide different examples to illustrate the subtle differences that are possible to be represented using this knowledge representation scheme.

Ontologies as Knowledge Representation Strategy in Biomedicine

Ontologies as Knowledge Representation Strategy in Biomedicine

Онтологическая система «знания-деятельность»

The paper considers an approach to construction of human rational activity ontological description. Such a description can serve as an additional source for entering the information space, expands the class of possible indexes for describing documents and allows to implement contextual search. The purpose and features of the use of ontologies in describing the subject area, both from a theoretical and an applied point of view, are considered. Criteria for identifying types of ontologies are indicated. Based on the principles of knowledge schematism, a knowledge representation ontologies system has been developed, which unites language, forms of knowledge representation and process schemes. It is shown that precisely such kind of ontologies system makes possible the practical use of ontologies in computing environments. For a comprehensive description of production (rational) activity, ontologies of artifacts and processes are presented, developed on the basis of an analysis of state and international standards. The ontology of an artifact is a description of an object - a means and a result of purposeful activity, an ontology of processes is a formalized description of the life cycle of an activity. With the help of the ontology of the artifact, it becomes possible to represent the logical structure of activity, and with the help of the ontology of processes, the temporal structure, which jointly determine the two sides of the description of complex activity. The proposed ontology of processes includes a hierarchically related set of stages of the life cycle of activity, objects and subjects of activity of each stage and plays the role of a "navigation map" for subjects of activity, allowing them to navigate in complex organizational structures.

Dear Leader’s Happy Story Time: A Party Game Based on Automated Story Generation

Players in Dear Leader’s Happy Story Time are placed in the role of contestants in a reality TV show where they are forced to audition for roles in the upcoming film of the host, a deranged billionaire who has inexplicably been elected president.  The stories are produced by a story generator that combines stock plots and characters to produce kitsch story outlines.  The players then collaborate to improvise a camp performance of the outline.  The game design provides a context for experimenting with automatic story generation within a narrative game, as well as an opportunity for experimenting with knowledge representation schemes for expressing the tropes of popular narrative.  The story generator uses a higher-order logic for describing tropes, and an HTN planning algorithm based on Nau et al.’s SHOP.

Ontology based knowledge representation: Case study from agriculture domain

The growth of web has touched everyone’s life from an economist, entrepreneur, and academician to a farmer. The agriculture sector is quite important for any country’s growth. Farmers can also be benefited if they are provided with relevant information they need. The reason that it is not happening on a large scale is due to many reasons. Information is available in different formats, platforms and it is highly unstructured. The availability and real time usage of this information is prohibited mainly by the way it is represented. Recently, Ontology has emerged as one of very expressive knowledge representation scheme which enables gathering information from heterogeneous sources and creates a common data model that is shared and agreed upon in diverse domains. The interoperability and deduction capabilities that Ontology offers are very useful for generating new knowledge. Most of the work pertaining to ontology development in agriculture domain is crop specific, where information about fertilizers is confined to a particular crop only. In this paper, we have designed and developed a generic ontology for fertilizers. Fertilizer itself is a concept which should be modeled independently. Even for crop specific ontology, fertilizer is one of the most important concept to be captured. So, it needs to be represented independently. Finding this research gap, an ontology in fertilizer subdomain is developed, taking into account the various issues that are faced while constructing an ontology and the enormous amount of data that is available but is not easy to structure and present in the form of ontology. We have also validated the Ontology using an available tool and domain experts have also validated the developed Ontology.

Knowledge ontology system

The paper considers a purpose and features of ontologies use in describing of subject area, both from a theoretical and an applied point of view. Criteria for identifying ontologies types are indicated. Based on cognition schematism principles, a knowledge representation ontologies system has been developed, that combines language, forms of knowledge representation and process schemes. It is shown that it is exactly such a system of ontologies makes it possible for the practical use of ontologies in computing environments.

Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network.

The paper focuses on the development of a reliable medical expert system for diagnosis of low back pain (LBP) by proposing an efficient frame-based knowledge representation scheme and a suitable resolution logic with conflicts in outcomes being resolved using Bayesian network. Considering that LBP is classified into many diseases based on different pain generators, the proposed methodology infers non-conflicting LBP diseases sorted according to their chances of occurrence. A satisfactory clinical efficacy (average relative error - 0.09, recall 74.44%, precision 76.67%, accuracy 71.11%, and F1-score 73.88%) of the proposed methodology has been found after validating the design with empirically selected thirty LBP patient cases. Constraining that an inferred disease having chance of occurrence, prior to pathological investigations, below 0.75 (as set by four pain specialists) is not accepted clinically; the design can correctly identify, on average, 74.44% of actual diagnosis; and 76.67% of inferred diagnosis is included in actual diagnosis. With the predicted chance of occurrence being lower than 0.75 by a fraction of 0.09 on average, the proposed design performs well for 73.88% cases detecting 71.11% inferred outcomes as accurate. The design offers homogeneity to the actual outcomes, with the chi-squared static being calculated as 11.08 having 12 as degree of freedom. Graphical abstract.

A Semantic Focused Web Crawler Based on a Knowledge Representation Schema

The Web has become the main source of information in the digital world, expanding to heterogeneous domains and continuously growing. By means of a search engine, users can systematically search over the web for particular information based on a text query, on the basis of a domain-unaware web search tool that maintains real-time information. One type of web search tool is the semantic focused web crawler (SFWC); it exploits the semantics of the Web based on some ontology heuristics to determine which web pages belong to the domain defined by the query. An SFWC is highly dependent on the ontological resource, which is created by domain human experts. This work presents a novel SFWC based on a generic knowledge representation schema to model the crawler’s domain, thus reducing the complexity and cost of constructing a more formal representation as the case when using ontologies. Furthermore, a similarity measure based on the combination of the inverse document frequency (IDF) metric, standard deviation, and the arithmetic mean is proposed for the SFWC. This measure filters web page contents in accordance with the domain of interest during the crawling task. A set of experiments were run over the domains of computer science, politics, and diabetes to validate and evaluate the proposed novel crawler. The quantitative (harvest ratio) and qualitative (Fleiss’ kappa) evaluations demonstrate the suitability of the proposed SFWC to crawl the Web using a knowledge representation schema instead of a domain ontology.

A knowledge-driven layered inverse reinforcement learning approach for recognizing human intents

ABSTRACT There is a rising trend in exploring the capability of inverse reinforcement learning (IRL) in high dimensional demonstrations. Our aim is to recognise human intents from video data within an IRL framework. For this, we present a two-layered maximum likelihood IRL model. The usefulness of knowledge representation (KR) schemes and availability of advisors at different layers is exploited through this model. Two main aspects are addressed: a. the importance of having abstract high-level information to the IRL framework in terms of semantic object affordance and b. deductively exploring the utility of a state at different temporal abstractions. The effectiveness of the proposed model has been evaluated with the help of standard Cornell Activity Dataset (CAD-120).

A Micro-view on Techniques for Knowledge Based Systems

Knowledge representation has been a topic of research and study from the time machine processing of data and information became popular. The attempt to mimic the human cognition using artificial intelligence techniques has been the goal of artificial intelligence systems. The crux of cognition is knowledge. Knowledge is an inference made from data and information that is acquired from a variety of sources. The ability to efficiently acquire information, process it and store it as a knowledge base for further retrieval and use makes the artificially intelligent system a cognitive system. The paper is a brief introduction to knowledge representation techniques and schemes

Rough set based lattice structure for knowledge representation in medical expert systems: Low back pain management case study

The aim of medical knowledge representation is to capture detailed domain knowledge in a clinically efficient manner, and to offer reliable resolution with the acquired knowledge. The knowledge base should allow incremental growth with inclusion of updated knowledge over the time. Accommodating the knowledge acquired from a variety of knowledge sources by different knowledge engineers may lead to a redundant and inconsistent design of the knowledge base, increasing the storage size and the time for knowledge retrieval. In this paper, we have proposed a rough set based lattice framework for representation of knowledge in medical expert systems which overcomes the problem of redundancy and inconsistency in knowledge and offers computational efficiency with respect to both time and space. The proposed knowledge representation offers a flexible scheme for expressing diverse possibilities of inter-relatedness among the symptoms and diseases in a systematic manner within the lattice structures.Through our design, we generate an optimal set of decision rules for use during inference. Reliability of each rule is measured using a new metric called credibility factor, and the certainty and coverage factors of a decision rule have been re-defined. During inference, the medical expert system considers the highly reliable and certain rules first, and the possible and uncertain rules at the later stages, if recommended by physicians. The proposed scheme ensures completeness, consistency, integrity, non-redundancy, and ease of access. These qualities are automatically preserved in the designed knowledge base while being updated with the new knowledge of medical advancements. As a result, the overall maintenance cost of a medical expert system is significantly reduced. The proposed knowledge representation technique has been illustrated using an example from the domain of low back pain.Though the proposed technique has been demonstrated for low back pain, it offers a generalized scheme for knowledge representation and may be adopted by medical expert systems (e.g. pathology, psychology, and many other specialties) having complexity similar to this one; no application-specific knowledge representation technique needs to be designed. The medical expert system developed using this scheme may act as a standardized example for development of different unexplored or semi-explored health care automated systems. This scheme may also be applied for pattern recognition, rule mining, and conflict analysis in complex medical data domains. Our design will have a wide ranging impact towards finding low-cost, reliable and available healthcare solutions for different diseases, especially in primary care units where expert physicians are scarce.

REVIEW OF METHODOLOGIES AND APPLICATIONS OF EXPERT SYSTEMS: OPTIMIZATION PERSPECTIVE

A comprehensive study of research carried out by eminent researchers givesa critical analysis of concepts, methodologies and applications used for their research work. It gives context for the continuation of ongoing research or inspires new research ideas.In general, a literature evaluation aids in the identification of research gaps. This research paper presents a systematic study of components, methodologies and applications of various expert systems introduced in the diverse range of sectors. This paper surveys the development of expert systems through the study of various research articles related to the expert system methodologies, applications, and components that include knowledge acquisition, knowledge representation schemes, inference engines, learning tools etc.

REVIEW OF METHODOLOGIES AND APPLICATIONS OF EXPERT SYSTEMS: OPTIMIZATION PERSPECTIVE

A comprehensive study of research carried out by eminent researchers givesa critical analysis of concepts, methodologies and applications used for their research work. It gives context for the continuation of ongoing research or inspires new research ideas.In general, a literature evaluation aids in the identification of research gaps. This research paper presents a systematic study of components, methodologies and applications of various expert systems introduced in the diverse range of sectors. This paper surveys the development of expert systems through the study of various research articles related to the expert system methodologies, applications, and components that include knowledge acquisition, knowledge representation schemes, inference engines, learning tools etc.

Confidence level evaluation of LOD resources on CKAN instances

Linked Open Data has been an initiative aimed at offering principles for the interconnection of data through machine-readable structures and knowledge representation schemes. At present, there are platforms that allow consuming LOD resources, being CKAN one of the most relevant on a large community made up of governmental organizations, NGOs, among others. However, the resources consumption lacks minimum criteria to determine their validity such as level of trust, quality, linkage and usability of the data; aspects that require a previous systematic analysis on the set of published data. To support this process of analysis and determination of the mentioned criteria, this paper has as purpose to present a method that allows analyzing the dataset current state obtained from the different instances published in CKAN, with the aim of evaluating the levels of trust that can offer from their sources. Finally, it presents results, conclusions and future work from the use of the tool for the dataset consumption belonging to certain instances ascribed to the CKAN platform.

Semantics of Analogies from a Logical Perspective

A number of different approaches to model analogies and analogical reasoning in AI have been proposed, applying different knowledge representation and mapping strategies. Nevertheless, analogies still seem to be hard to grasp from a formal perspective, with no known treatment in the literature of, in particular, their formal semantics, though the empirical treatments involving human subjects are abundant. In this paper we present a framework that allows to analyze the syntax and the semantics of analogies in a universal logic-based setting without committing ourselves to a specific type of logic. We show that the syntactic process of analogy-making by finding a generalization can be given a sensible interpretation on the semantic level based on the theory of institutions. We then apply these ideas by considering a framework of analogy-making that is based on classical first-order logic.