Contextual Knowledge Representation Research Articles

MetaGNN-Based Medical Records Unstructured Specialized Vocabulary Few-Shot Representation Learning

With the continuous breakthroughs in artificial intelligence technology, it has become easier to extract general-purpose knowledge using machine learning, but it is a challenging task to extract and learn small samples of knowledge in medical expertise. On the one hand, it is difficult to represent medical expertise entities, and on the other hand, the training samples of such expertise are small, and deep learning methods often require a large number of samples to complete the learning task. To this end, we proposes a graph network learning method for specialized vocabulary representation. Specifically, a contextual knowledge representation model based on graph meta-learning is proposed, which combines text, phrase, vocabulary, and other information to solve the problem of sparse data of medical electronic medical record entities that cannot be extracted and learned. In this method, a text-independent lexical representation learning method, a context-aware graph neural network, and a combined LSTM language model are used to model information from different perspectives as a way to learn semantic representations of professional discourse entities. The experimental results show that the accuracy of the method outperforms other similar methods and proves its effectiveness.

Study on intelligent construction of English-Chinese bilingual context model based on CBR

The article briefly reviews the context-based view of the case-based reasoning mechanism and second language acquisition theories, and analyzes the feasibility of the application of CBR in bilingual context knowledge representation. Based on this, it proposes bilingualism. The computational model, algorithm and general implementation process of the system for contextual knowledge representation CBR system is a preliminary exploration of the application of machine learning and artificial intelligence theory in the study of second language acquisition theory. It is intended to provide a second language acquisition study to the new research methods and perspectives.

Utilization of Contextual Knowledge Elements in 3D CAD Model Visualization from the Perspectives of Practicing Engineers

Today’s industry force demands for knowledgeable and skillful engineering graduates in using modern Computer Aided Design (CAD) tools. However, current practices show that there is a lack of contextual knowledge among Mechanical Engineering undergraduates and fresh graduate engineers in utilizing 3D CAD modeling software to develop a good product design. Hence, a study to address this issue is conducted by focusing on the representation of contextual knowledge in 3D CAD modeling. This article focused on presenting essential elements of contextual knowledge utilized among practicing engineers in their daily design works, in the aspect of Model Visualization. This study was conducted using a transcendental phenomenology approach. Four practicing engineers from engineering department of one shipbuilding company in Peninsular Malaysia were purposively selected to be studied. From the analysis, there are three most frequent emerging themes in the application of contextual knowledge in visualizing and presenting model using 3D CAD modeling: Realization, Design Intention and Normalization. These three elements play an important role in helping engineers to contextualize their design work during the stage of visualizing and presenting created model for new product development process.

An Ontology-Based Contextual Knowledge Representation for Semantic Image Segmentation

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Enhancing context knowledge repositories with justifiable exceptions

Dealing with context dependent knowledge is a well-known area of study that roots in John McCarthy's seminal work. More recently, the Contextualized Knowledge Repository (CKR) framework has been conceived as a logic-based approach in which knowledge bases have a two layered structure, modeled by a global context and a set of local contexts. The global context not only contains the meta-knowledge defining the properties of local contexts, but also holds the global (context independent) object knowledge that is shared by all of the local contexts. In many practical cases, however, it is desirable to leave the possibility to “override” the global object knowledge at the local level: in other words, it is interesting to recognize the pieces of knowledge that can admit exceptional instances in the local contexts that do not need to satisfy the general axiom. To address this need, we present in this paper an extension of CKR in which defeasible axioms can be included in the global context. The latter are verified in the local contexts only for the instances for which no exception to overriding exists, where exceptions require a justification in terms of facts that are provable from the knowledge base. We formally define this semantics and study some semantic and computational properties, where we characterize the complexity of the major reasoning tasks, among them satisfiability testing, instance checking, and conjunctive query answering. Furthermore, we present a translation of extended CKRs with knowledge bases in the Description Logic SROIQ-RL under the novel semantics to datalog programs under the stable model (answer set) semantics. We also present an implementation prototype and examine its scalability with respect to the size of the input CKR and the amount (level) of defeasibility in experiments. Finally, we compare our representation approach with some major formalisms for expressing defeasible knowledge in Description Logics and contextual knowledge representation. Our work adds to the body of results on using deductive database technology such as SQL and datalog in these areas, and provides an expressive formalism (in terms of intrinsic complexity) for exception handling by overriding.

Semantic and Contextual Knowledge Representation for Lexical Disambiguation: Case of Arabic-French Query Translation

We present in this paper, an automatic query translation system in cross-language information retrieval (Arabic-French). For the lexical disambiguation, our system combines between two resources: a bilingual dictionary and a parallel corpus. To select the best translation, our method is based on a correspondence measure between two semantic networks. The first one represents the senses of ambiguous terms of the query. The second one is a semantic network contextually enriched, representing the collection of sentences responding to the query. This collection forms the knowledge base of our disambiguation method and it is obtained by alignment with the relevant sentences in Arabic. The evaluation of the proposed system shows the advantage of the contextual enrichment on the quality of the translation. We obtained a high precision, relatively proportional to the precision provided by the used alignment. Finally, our translation demonstrates its potential by comparing its Bleu score with that of Google translate.

An intelligent approach towards automatic shape modelling and object extraction from satellite images using cellular automata-based algorithms

Automatic feature extraction has witnessed the use of many intelligent methodologies over the past decade. However, inadequate modelling of feature shape and contextual knowledge has limited the detection accuracy. In this article, we present a framework for accurate feature shape modelling and contextual knowledge representation using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), coreset, and Cellular Automata (CA). CNN was found to be effective in modelling different complex features, and the complexity of the approach was considerably reduced using corset optimization. Spectral and spatial information was dynamically combined using adaptive kernels when representing contextual knowledge. The methodologies were compared with contemporary methods using different statistical measures. Application of the algorithms to satellite images revealed considerable success. The methodology was also effective in providing intelligent interpolation and interpretation of random features.

A conceptual framework for component context specification and representation in a metaCASE environment

A lack of design information can be a significant barrier for systems developers when developing and reusing a component. This paper tackles this problem by presenting and exemplifying the conceptual framework of component context and its hypertext representation in a metaCASE environment. It discusses the linking of contextual knowledge to components in systems analysis and design. The contextual knowledge includes the conceptual dependencies of component definition, reuse, and implementation, as well as the reasoning and rationale behind design and reuse processes. We also illustrate the hypertext approach to contextual knowledge representation that enables designers to express, record, explore, recognize, and negotiate their shared context within a metaCASE environment.

Context-based design of robotic systems

The need for improving the robustness, as well as the ability to adapt to different operational conditions, is a key requirement for a wider deployment of robots in many application domains. In this paper, we present an approach to the design of robotic systems, that is based on the explicit representation of knowledge about context. The goal of the approach is to improve the system’s performance, by dynamically tailoring the functionalities of the robot to the specific features of the situation at hand. While the idea of using contextual knowledge is not new, the proposed approach generalizes previous work, and its advantages are discussed through a case study including several experiments. In particular, we identify many attempts to use contextual knowledge in several basic functionalities of a mobile robot such as: behavior, navigation, exploration, localization, mapping and perception. We then show how re-designing our mobile platform with a common representation of contextual knowledge, leads to interesting improvements in many of the above mentioned components, thus achieving greater flexibility and robustness in the face of different situations. Moreover, a clear separation of contextual knowledge leads to a design methodology, which supports the design of small specialized system components instead of complex self-contained subsystems.

ConKMeL: a contextual knowledge management framework to support multimedia e-Learning

As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework.

Strategies for cursive script recognition using hidden Markov models

We describe several approaches for the application of hidden Markov models to the recognition of handwritten words. In all approaches the words are described by strings of symbols. The descriptions differ only with respect to the size of the vocabulary to be recognized. We can define two distinct cases: in the first, the vocabulary is small and constant; in the second, the vocabulary is limited, but dynamic in the sense that it is a varying subset of an open one. We also describe an application of hidden Markov models to the representation of contextual knowledge and propose some strategies for rejecting unreliable word interpretations, in particular, when the word corresponding to the image does not necessarily belong to the lexicon.