Classification of Under-Resourced Language Documents Using English Ontology

Automatic documents classification is an important task due to the rapid growth of the number of electronic documents, which aims automatically assign the document to a predefined category based on its contents. The use of automatic document classification has been plays an important role in information extraction, summarization, text retrieval, question answering, e-mail spam detection, web page content filtering, automatic message routing , etc.Most existing methods and techniques in the field of document classification are keyword based, but due to lack of semantic consideration of this technique, it incurs low performance. In contrast, documents also be classified by taking their semantics using ontology as a knowledge base for classification; however, it is very challenging of building ontology with under-resourced language. Hence, this approach is only limited to resourced language (i.e. English) support. As a result, under-resourced language written documents are not benefited such ontology based classification approach. This paper describes the design of automatic document classification of under-resourced language written documents. In this work, we propose an approach that performs classification of under-resourced language written documents on top of English ontology. We used a bilingual dictionary with Part of Speech feature for word-by-word text translation to enable the classification of document without any language barrier. The design has a concept-mapping component, which uses lexical and semantic features to map the translated sense along the ontology concepts. Beside this, the design also has a categorization component, which determines a category of a given document based on weight of mapped concept. To evaluate the performance of the proposed approach 20-test documents for Amharic and Tigrinya and 15-test document for Afaan Oromo in each news category used. In order to observe the effect of incorporated features (i.e. lemma based index term selection, pre-processing strategies during concept mapping, lexical and semantics based concept mapping) five experimental techniques conducted. The experimental result indicated that the proposed approach with incorporation of all features and components achieved an average F-measure of 92.37%, 86.07% and 88.12% for Amharic, Afaan Oromo and Tigrinya documents respectively. Keywords: under-resourced language, Multilingual, Documents or text Classification, knowledge base, Ontology based text categorization, multilingual text classification, Ontology. DOI: 10.7176/CEIS/10-6-02 Publication date:July 31st 201

Clinical Decision Support System for Unani Medicine Practitioners

Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.Comment: 59 pages, 11 figures, Computer Science Bachelor's Thesis on use of Artificial Intelligence in Clinical Decision Support System for Unani Medicine

Constructive Ontology Engineering

The proliferation of the Semantic Web depends on ontologies for knowledge sharing, semantic annotation, data fusion, and descriptions of data for machine interpretation. However, ontologies are difficult to create and maintain. In addition, their structure and content may vary depending on the application and domain. Several methods described in literature have been used in creating ontologies from various data sources such as structured data in databases or unstructured text found in text documents or HTML documents. Various data mining techniques, natural language processing methods, syntactical analysis, machine learning methods, and other techniques have been used in building ontologies with automated and semi-automated processes. Due to the vast amount of unstructured text and its continued proliferation, the problem of constructing ontologies from text has attracted considerable attention for research. However, the constructed ontologies may be noisy, with missing and incorrect knowledge. Thus ontology construction continues to be a challenging research problem. The goal of this research is to investigate a new method for guiding a process of extracting and assembling candidate terms into domain specific concepts and relationships. The process is part of an overall semi automated system for creating ontologies from unstructured text sources and is driven by the user’s goals in an incremental process. The system applies natural language processing techniques and uses a series of syntactical analysis tools for extracting grammatical relations from a list of text terms representing the parts of speech of a sentence. The extraction process focuses on evaluating the subject predicate-object sequences of the text for potential concept-relation-concept triples to be built into an ontology. Users can guide the system by selecting seedling concept-relation-concept triples to assist building concepts from the extracted domain specific terms. As a result, the ontology building process develops into an incremental one that allows the user to interact with the system, to guide the development of an ontology, and to tailor the ontology for the user’s application needs. The main contribution of this work is the implementation and evaluation of a new semi- automated methodology for constructing domain specific ontologies from unstructured text corpus

How organisation of architecture documentation affects architectural knowledge retrieval

A common approach to software architecture documentation in industry projects is the use of file-based documents. This approach offers a single-dimensional arrangement of the architectural knowledge. Knowledge retrieval from file-based architecture documentation is efficient if the organisation of knowledge supports the needs of the readers; otherwise it can be difficult. In this paper, we compare the organisation and retrieval of architectural knowledge in a file-based documentation approach and an ontology-based documentation approach. The ontology-based approach offers a multi-dimensional organisation of architectural knowledge by means of a software ontology and semantic wiki, whereas file-based documentation typically uses hierarchical organisation by directory structure and table of content. We conducted case studies in two companies to study the efficiency and effectiveness of retrieving architectural knowledge from the different organisations of knowledge. We found that the use of better knowledge organisation correlates with the efficiency and effectiveness of AK retrieval. Professionals who used the knowledge organisation found this beneficial

Upper Tag Ontology (UTO) For Integrating Social Tagging Data

Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube)