Role In Knowledge Representation Research Articles

The Role of Cognitive Schema Theory in the Translation from Arabic into English

This study investigates the role of schema theory in translation and aims to gain knowledge that may progress AI-related domains through the simulation of cognition and AI. Schema theory hypothesizes that translators use mental frameworks (schemata) to organize and interpret information. In the context of translation, schemata play a crucial role in knowledge representation, affecting limitations made during the translation process. A cognitive approach is employed and qualitative methods are used in the analysis. A corpus is collected from the translation of a SL Arabic novel (Frankenstein in Baghdad by A. Saadawi) into TL English, covering domains and language pairs. The translation process is analyzed to identify cognitive patterns employed by the translator as they apply schemata to achieve cross-cultural renditions. Conclusions reveal that schema activation significantly impacts translation choices, influencing how the translator interprets and conveys meaning across languages. Cognitive patterns shed light on how cultural and linguistic factors influence the transfer of information between SL and TL. Additionally, the study uncovers variations in schema utilization across different translation tasks and the adaptability and flexibility of cognitive processes in response to varying contexts and linguistic challenges. By deepening our understanding of schema theory, this research contributes to the design, training, and assessment of AI algorithms. It also provides valuable insights into the cognitive mechanisms underlying successful cross-linguistic communication and offers practical implications for translators engaged in intercultural exchange. Finally, this paper recommends that AI systems can drive advancements in MT and other AI-related fields of prior knowledge to produce accuracy and more closely with native speakers by using schema theory concepts.

Eliminating ontology contradictions based on the Myerson value

Ontologies play a pivotal role in knowledge representation across various artificial intelligence domains, serving as foundational frameworks for organizing data and concepts. However, the construction and evolution of ontologies frequently lead to logical contradictions that undermine their utility and accuracy. Typically, these contradictions are addressed using an Integer Linear Programming (ILP) model, which traditionally treats all formulas with equal importance, thereby neglecting the distinct impacts of individual formulas within minimal conflict sets. To advance this method, we integrate cooperative game theory to compute the Shapley value for each formula, reflecting its marginal contribution towards resolving logical contradictions. We further construct a graph-based representation of the ontology, enabling the extension of Shapley values to Myerson values. Subsequently, we introduce a Myerson-weighted ILP model that employs a lexicographic approach to eliminate logical contradictions in ontologies. The model ensures the minimum number of formula deletions, subsequently applying Myerson values to guide the prioritization of deletions. Our comparative analysis across 18 ontologies confirms that our approach not only preserves more graph edges than traditional ILP models but also quantifies formula contributions and establishes deletion priorities, presenting a novel approach to ILP-based contradiction resolution.

AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach

BackgroundAutomated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI). Semantic ontology can play a crucial role in knowledge representation, data integration, and information retrieval.MethodsThis study proposes a semantic ontology model to annotate the AI predictions, forecasting outcomes, and personal preferences to conceptualize a personalized recommendation generation model with a hybrid approach. This study considers a mixed activity projection method that takes individual activity insights from the univariate time-series prediction and ensemble multi-class classification approaches. We have introduced a way to improve the prediction result with a residual error minimization (REM) technique and make it meaningful in recommendation presentation with a Naïve-based interval prediction approach. We have integrated the activity prediction results in an ontology for semantic interpretation. A SPARQL query protocol and RDF Query Language (SPARQL) have generated personalized recommendations in an understandable format. Moreover, we have evaluated the performance of the time-series prediction and classification models against standard metrics on both imbalanced and balanced public PMData and private MOX2-5 activity datasets. We have used Adaptive Synthetic (ADASYN) to generate synthetic data from the minority classes to avoid bias. The activity datasets were collected from healthy adults (n = 16 for public datasets; n = 15 for private datasets). The standard ensemble algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) serve as input for the classification models. Subsequently, we re-verify the classifiers on the private MOX2-5 dataset. The performance of the ontology has been assessed with reasoning and SPARQL query execution time. Additionally, we have verified our ontology for effective recommendation generation.ResultsWe have tested several standard AI algorithms and selected the best-performing model with optimized configuration for our use case by empirical testing. We have found that the autoregression model with the REM method outperforms the autoregression model without the REM method for both datasets. Gradient Boost (GB) classifier outperforms other classifiers with a mean accuracy score of 98.00%, and 99.00% for imbalanced PMData and MOX2-5 datasets, respectively, and 98.30%, and 99.80% for balanced PMData and MOX2-5 datasets, respectively. Hermit reasoner performs better than other ontology reasoners under defined settings. Our proposed algorithm shows a direction to combine the AI prediction forecasting results in an ontology to generate personalized activity recommendations in eCoaching.ConclusionThe proposed method combining step-prediction, activity-level classification techniques, and personal preference information with semantic rules is an asset for generating personalized recommendations.

A Bi-criteria Optimization Model for Adjusting the Decision Tree Parameters

Decision trees play a very important role in knowledge representation because of its simplicity and self-explanatory nature. We study the optimization of the parameters of the decision trees to find a shorter as well as more accurate decision tree. Since these two criteria are in conflict, we need to find a decision tree with suitable parameters that can be a trade off between two criteria. Hence, we design two algorithms to build a decision tree with a given threshold of the number of vertices based on the bi-criteria optimization technique. Then, we calculate the local and global misclassification rates for these trees. Our goal is to study the effect of changing the threshold for the bi-criteria optimization of the decision trees. We apply our algorithms to 13 decision tables from UCI Machine Learning Repository and recommend the suitable threshold that can give us more accurate decision trees with a reasonable number of vertices.

Designing of higher order information granules through clustering heterogeneous granular data

This study is devoted to the generalization of information granules by forming higher order, namely, order-2 information granules. Information granules are semantically meaningful entities, which play a central role in knowledge representation and system modeling in the framework of Granular Computing. The encountered information granules could exhibit significant heterogeneity because of the diversified formal formalisms. To facilitate an effective generalization of heterogeneous granular data when using clustering algorithms, an efficient scheme has been proposed to form a unified representation of various types of granular data by using Possibility–necessity measures. Once the clustering process has been completed in the possibility–necessity feature space, the higher order information granules come as results of decoding by involving the possibility–necessity metrics and fuzzy relational calculus. The extent to which the higher order information granules are supported by the granular data present at a lower level of hierarchy is quantified in terms of the membership degrees obtained in the clustering process. Experimental studies concerning a series of publicly available datasets coming from UCI and KEEL machine learning repositories are carried out in this study.

Semantic assessment of smart healthcare ontology

PurposeHealth-care ontologies and their terminologies play a vital role in knowledge representation and data integration for health information. In health-care systems, Internet of Technology (IoT) technologies provide data exchange among various entities and ontologies offer a formal description to present the knowledge of health-care domains. These ontologies are advised to assure the quality of their adoption and applicability in the real world.Design/methodology/approachOntology assessment is an integral part of ontology construction and maintenance. It is always performed to identify inconsistencies and modeling errors by the experts during the ontology development. A smart health-care ontology (SHCO) has been designed to deal with health-care information and IoT devices. In this paper, an integrated approach has been proposed to assess the SHCO on different assessment tools such as Themis, Test-Driven Development (TDD)onto, Protégé and OOPs! Several test cases are framed to assess the ontology on these tools, in this research, Themis and TDDonto tools provide the verification for the test cases while Protégé and OOPs! provides validation of modeled knowledge in the ontology.FindingsAs of the best knowledge, no other study has been presented earlier to conduct the integrated assessment on different tools. All test cases are successfully analyzed on these tools and results are drawn and compared with other ontologies.Originality/valueThe developed ontology is analyzed on different verification and validation tools to assure the quality of ontologies.

Experiential knowledge representation and reasoning based on linguistic Petri nets with application to aluminum electrolysis cell condition identification

Fuzzy Petri nets (FPNs) play an important role in knowledge representation and reasoning (KRR), and they have been widely used in many fields. Linguistic terms are usually used to express the experiential knowledge of decision-makers in the above fields. However, cognitive nonconformity, fuzziness and uncertainty of experiential knowledge are widespread in industrial production processes, making it difficult for current FPNs to precisely model the experience or cognition of experts. In an effort to overcome the shortcomings of FPNs, linguistic Petri nets (LPNs) are proposed based on interval type-2 fuzzy sets theory and FPNs in this paper. The extended TOPSIS (ETOPSIS) is proposed to fuse together the cognition of multiple decision-makers. An interval type-2 fuzzy ordered weighted averaging operator is proposed to improve the knowledge reasoning capability of LPNs. Two comparisons are presented to demonstrate the validity of the proposed ETOPSIS and LPNs. In addition, the KRR model for aluminum electrolysis cell condition identification (AECCI) is proposed and AECCI results show the proposed methods are efficient to embrace cognitive nonconformity and manage fuzziness and uncertainty of experiential knowledge.

The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine.

Summary Introduction : Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods : We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results : Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions : Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.

Evaluating epistemic negation in answer set programming

Epistemic negation not along with default negation ¬ plays a key role in knowledge representation and nonmonotonic reasoning. However, the existing epistemic approaches such as those by Gelfond [13,15,14], Truszczynski [33] and Kahl et al. [18] behave not satisfactorily in that they suffer from the problems of unintended world views due to recursion through the epistemic modal operator K or M (KF and MF are shorthands for ¬notF and not¬F, respectively). In this paper we present a new approach to handling epistemic negation which is free of unintended world views and thus offers a solution to the long-standing problem of epistemic specifications which were introduced by Gelfond [13] over two decades ago. We consider general logic programs consisting of rules of the form H←B, where H and B are arbitrary first-order formulas possibly containing epistemic negation, and define a general epistemic answer set semantics for general logic programs by introducing a novel program transformation and a new definition of world views in which we apply epistemic negation to minimize the knowledge in world views. The general epistemic semantics is applicable to extend any existing answer set semantics, such as those defined in [26,27,32,1,8,12,29], with epistemic negation. For illustration, we extend FLP answer set semantics of Faber et al. [8] for general logic programs with epistemic negation, leading to epistemic FLP semantics. We also extend the more restrictive well-justified FLP semantics of Shen et al. [29], which is free of circularity for default negation, to an epistemic well-justified semantics. We consider the computational complexity of epistemic FLP semantics and show that for a propositional program Π with epistemic negation, deciding whether Π has epistemic FLP answer sets is Σ3p-complete and deciding whether a propositional formula F is true in Π under epistemic FLP semantics is Σ4p-complete in general, but has lower complexity for logic programs that match normal epistemic specifications, where the complexity of world view existence and query evaluation drops by one level in the polynomial hierarchy.

Improve the Classifier Accuracy for Continuous Attributes in Biomedical Datasets Using a New Discretization Method

In real-time data mining applications discrete values play vital role in knowledge representation as they are easy to handle and very close to knowledge level representation than continuous attributes. Discretization is a major step in data mining process where continuous attributes are transformed into discrete values. However, most of the classifications algorithms are require discrete values as the input. Even though some data mining algorithms directly contract with continuous attributes, the learning process yields low quality results. In this paper, we introduce a new discretization method based on standard deviation technique called ‘z-score’ for continuous attributes on biomedical datasets. We compare performance of the proposed algorithm with the state-of- the-art discretization techniques. The experiment results show the efficiency in terms of accuracy and also minimize the classifier confusion for decision making process.

Semantic E-Learn Services and Intelligent Systems using Web Ontology

Present vision for the web is the semantic web in which information is given explicit meaning, making it easier for machines to automatically process and integrate information available on the web. It provides the information exactly. Now days, ontology is playing a major role in knowledge representation for the semantic web [1]. Ontology is a conceptualization of domain into a human understandable and machine readable or machine process able format consisting of entities, attributes, relationships and axioms. Ontology web language is designed for use by applications that need to process the content of information [22]. In this context many e-learning systems were proposed in the literature. Semantic Web technology may support more advanced Artificial intelligence problems for knowledge retrieval [20]. This paper aims at presenting an intelligent e-learning system from the literature.

ATOB algorithm: an automatic ontology construction for Thai legal sentences retrieval

Ontology plays an important role in knowledge representation, especially in the domain of information retrieval. However, building ontology remains a challenging problem because it is a time-consuming task for experts. To overcome these drawbacks, we propose a novel approach called the Automatic Thai Legal Ontology Building (ATOB) algorithm for automatic legal ontology building and to improve the court sentences retrieval process. The ATOB can automatically generate seed ontology and expand the ontology using Thai legal terminology, i.e. TLlexicon. The expansion process is terminated automatically by the threshold parameter. Moreover, the ATOB applies the concept of the ant colony algorithm to improve the court sentences retrieval process. We conclude that the effective ontology should be weight-embedded. The empirical results demonstrate that the performance of the ATOB algorithm is better than that of the traditional search method. The performance figures for the ATOB framework measured in terms of precision, recall, F-measure and diversity are 0.90, 0.91, 0.90 and 0.39, respectively.

Generating ontologies with basic level concepts from folksonomies

Abstract This paper deals with the problem of ontology generation. Ontology plays an important role in knowledge representation, and it is an artifact describing a certain reality with specific vocabulary. Recently many researchers have realized that folksonomy is a potential knowledge source for generating ontologies. Although some results have already been reported on generating ontologies from folksonomies, most of them do not consider what a more acceptable and applicable ontology for users should be, nor do they take human thinking into consideration. Cognitive psychologists find that most human knowledge is represented by basic level concepts which is a family of concepts frequently used by people in daily life. Taking cognitive psychology into consideration, we propose a method to generate ontologies with basic level concepts from folksonomies. Using Open Directory Project (ODP) as the benchmark, we demonstrate that the ontology generated by our method is reasonable and consistent with human thinking.

Precisiated natural language (PNL)

This article is a sequel to an article title A New Direction in AI--Toward a Computational Theory of Perceptions, which appeared in the Spring 2001 issue of AI Magazine (volume 22, No. 1, 73-84). The concept of precisiated natural language (PNL) was briefly introduced in that article, and PNL was employed as a basis for computation with perceptions. In what follows, the conceptual structure of PNL is described in greater detail, and PNL's role in knowledge representation, deduction, and concept definition is outlined and illustrated by examples. What should be understood is that PNL is in its initial stages of development and that the exposition that follows is an outline of the basic ideas that underlie PNL rather than a definitive theory.

Intention without representation

A mechanism for planning ahead would appear to be essential to any creature with more than insect level intelligence. In this paper it is shown how planning, using full means‐ends analysis, can be had while avoiding the so called symbol grounding problem. The key role of knowledge representation in intelligence has been acknowledged since at least the enlightenment, but the advent of the computer has made it possible to explore the limits of alternate schemes, and to explore the nature of our everyday understanding of the world around us. In particular, artificial intelligence (AI) and robotics has forced a close examination, by people other than philosophers, of what it means to say for instance that “snow is white.” One interpretation of the “new AI” is that it is questioning the need for representation altogether. Brooks and others have shown how a range of intelligent behaviors can be had without representation, and this paper goes one step further showing how intending to do things can be achieved without symbolic representation. The paper gives a concrete example of a mechanism in terms of robots that play soccer. It describes a belief, desire and intention (BDI) architecture that plans in terms of activities. The result is a situated agent that plans to do things with no more ontological commitment than the reactive systems Brooks described in his seminal paper, “Intelligence without Representation.”

The Role of Knowledge Representation in Geographic Knowledge Discovery: A Case Study

AbstractWith the advent of massive, heterogeneous geographic datasets, data mining and knowledge discovery in databases (KDD) have become important tools in deriving meaningful information from these data. In this paper, we discuss how knowledge representation can be employed to significantly enhance the power of the knowledge discovery process to uncover patterns and relationships. We suggest that geographic data models that support knowledge discovery must represent both observational data and derived knowledge. In addition, knowledge representation in the context of KDD must support the iterative and interactive nature of the knowledge discovery process to enable the analyst to iteratively apply, and revise the parameters of, specific analytical techniques. Our approach to knowledge representation and discovery is demonstrated through a case study that focuses on the identification and analysis of storms and other related climate phenomena embedded within a spatio‐temporal data set of meteorological observations.

Sources of Stress-Resistant Performance in Aeronautical Decision Making: The Role of Knowledge Representation and Trait Anxiety

It is not clear what elements best “protect” performance from the degrading influences of stress, or how they interact. The experiment reported here examined the effects of trait anxiety and expertise on stressed aeronautical decision making (ADM) performance in a flight simulation task. Novice and expert pilots were administered a battery of cognitive tests, personality tests, and a flight simulation task under stressed and nonstressed conditions. Both groups showed a significant decrement in performance under stress in the non-domain-specific tasks. However, this was not reflected in any performance decrement in ADM under stress by experienced pilots. Only novice pilots made poorer decisions under stress. Measures of knowledge representation were very predictive of proficient ADM under stress. In contrast, trait anxiety scores (equal for both groups) were associated with poorer ADM only in the novice group. Highly trait-anxious experts showed no performance decrements in ADM under stress.