ONTOLOGY VALIDATION ALGORITHM ON DATA DRIVEN APPROACH AND VOCABULARY ASPECT

Trying out or updating an existing learning ontology and providing evaluation tools to assess its quality are essential steps before putting an e-learning system online. Ontology evaluation is a crucial task and it is usually the output of an automatic process. This paper proposes an ontology-driven model, called Ontology-Driven model for the Evaluation of Learning Ontologies (ODELO), for the evaluation of ontologies representing learning resources with respect to syntactic, semantic, pragmatic, social and cohesion metrics. Syntax deals with the formal relations between signs (e.g., words, phrases, sentences) and the production of new ones. Semantics is the study of the relationships between the system of signs and their meanings. Pragmatics is the study of natural language understanding. Social metrics reflect the fact that software agents and ontologies coexist and communicate in communities. Ontology cohesion metrics refer to the degree of relatedness of ontology classes. ODELO is a deductive evaluation model that identifies the elements of ontological quality for learning ontologies. In this paper we propose a framework for assessing the quality of learning ontologies, which constitute the basis for intelligent educational Adaptive Hypermedia (AH) systems. We introduce a new pragmatic quality metric for assessing the quality of learning ontologies.

Many ontologies have been developed recently, and evaluation is important for researchers or users when searching ontologies needed in their work. Ontology evaluation also is important phase of ontology development, refinement or evolution process. Automatically-developed ontologies as a result of ontology learning also need from evaluation. So, ontology evaluation is important both for selecting ontologies, meeting some requirements, and as a part of ontology life cycle. In this paper we propose deep analysis of ontology evaluation and discuss how multilingualism affects ontology evaluation. We classify ontology evaluation approaches, methods and metrics according to several dimensions. Our analysis and conclusions will be helpful for development of good ontologies and for finding or selection of needed ontologies.

This study examines various approaches for ontology construction and evaluation in medicinal plant ontology, focusing on the challenges associated with this domain. Thirty review articles on three databases revealed five alternative approaches for ontology generation in herbal medicine: well-structured methodology, customized methodology, semiautomatic approach, socio-technical approach, domain expert techniques, and top-level approach. However, 68% of the research did not include ontology evaluation. Ontology quality was often assessed using competency questions, expert opinion, and automated error detection. The study provides ontology developers with a better understanding of established methodologies for developing and evaluating standard ontologies in this area and identifies gaps in existing research for future studies.

The Semantic Web relies heavily on formal ontologies to structure data for comprehensive and transportable machine understanding. Thus, the proliferation of ontologies factors largely in the Semantic Web's success. The authors present an ontology learning framework that extends typical ontology engineering environments by using semiautomatic ontology construction tools. The framework encompasses ontology import, extraction, pruning, refinement and evaluation.

Ontology is a representation term used to describe and represent a domain of knowledge. Manually ontology development is currently considered complex, requiring a lot of time and effort. This research was proposed to develop methods to build automatic domain ontology bilingual in Indonesian and English by using corpus and ontology design patterns (ODPs) in tuberculosis disease. In this study, the methods used were to combine ontology learning from text and correspond with ontology design patterns to decrease the role of expert knowledge. The methods in this research consist of six stages: (i) Term and relation extraction (ii) Corresponding with Tuberculosis glossary (iii) Corresponding the ontology design patterns (iv) Score computation similarity term and relations with ODPs (v) Ontology Building (vi) Ontology evaluation. The results of ontology construction were 361 terms and 44 relations with 260 terms were added. The calculation accuracy of ontology construction was 71%. Ontology construction had higher complexity and shorter time as well as decreases the role of the expert knowledge which proof that the automatic ontology evaluation is better than manual ontology construction.

This paper presents the basis of our approach for evaluation of application ontologies. Adapting an existing task-based evaluation, this approach explains how crowdsourcing, involving application users, can efficiently help in the improvement of an application ontology all along the ontology lifecycle. A real case experiment on an application ontology designed for the semantic annotation of geobusiness user data illustrates the proposal.

Ontology evaluation poses a number of difficult challenges requiring different evaluation methodologies, particularly for a “dynamic ontology” representing a complex set of concepts and generated by a combination of automatic and semi-automatic methods. We review evaluation methods that focus solely on syntactic (formal) correctness, on the preservation of semantic structure, or on pragmatic utility. We propose two novel methods for dynamic ontology evaluation and describe the use of these methods for evaluating the different taxonomic representations that are generated at different times or with different amounts of expert feedback. The proposed “violation” and “volatility” scores represent an attempt to merge syntactic and semantic considerations. Violation measures the degree of “ontological fit” to a text corpus representative of the domain. Volatility calculates the stability of the methods for ontology generation and extension. Combined, they support estimation of convergence towards a stable representation of the domain. No method of evaluation can avoid making substantive normative assumptions about what constitutes “correct” representation, but rendering those assumptions explicit can help with the decision about which methods are appropriate for selecting amongst a set of available ontologies or for tuning the design of methods used to generate a hierarchically organized representation of a domain.

In this article, we propose a metric for the automatic evaluation of restricted domain ontologies. The metric is defined in terms of the evaluation of different lexico-syntactic, statistical and semantic approaches. A syntactic approach employed is the use of lexical syntactic patterns, other approaches as grouping by formal concept analysis, similarity, latent semantic analysis and dependence graphs are used as well. These approaches focus on reference corpora to find evidence of the validity of concepts and semantic relationships stored in the target ontology. The proposed evaluation approach is able to provide a score obtained through the metric, which is based on the accuracy measure used for each ontology evaluated. The score is associated in some way with the ontology quality. This score is given with a certain degree of reliability, and it is obtained by comparing the results given against the evaluation of human experts and a baseline.

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.

Selecting the most appropriate candidate domain ontology is necessary to ensure that the ontology covers the domain of interest at a reasonable level of detail. Existing approaches have the following drawbacks: (1) Focused on the ontology coverage of concepts in the domain of interest, and ignored the semantic richness associated with each concept. (2) The ontology coverage metrics tend to select either large ontologies with broad scope or ontologies with a small number of concepts which may not capture a particular domain of interest. (3) The approaches are not robust in the coverage and semantic richness metric results when different term extraction and recognition algorithms are used. The limitations mentioned above will result in selecting ontologies which are not related to the domain of interest. Therefore, this paper presents a novel Terminological ONtology Evaluator (TONE). TONE uses a textual corpus to evaluate the ontology coverage and semantic richness. TONE was compared with existing ontology evaluation approaches and it proved that it was able to select the domain ontology which was intentionally developed to cover a specific domain of interest. In addition, TONE proved to be more robust in the coverage and semantic richness metric results compared to existing approaches.

The Gene Ontology (GO) provides a framework to systematically classify and annotate gene function. The annotations associated with GO play a critical role in modern biology and cover many organisms. For the human genome, over 10,000 GO terms are used to annotate gene function in an expansive database of over 200,000 annotations. Due to the importance of the GO annotations in modern biology, significant effort has been put into assessing the quality of the annotations. Providing measures of annotation completeness, accuracy, and precision is critical if researchers are to use the annotations in real-world applications with confidence. Here, we describe a task-based approach that examines the completeness and utility of GO annotations through the lens of gene enrichment analysis. Our approach can be used to model the progression of the GO annotations over time, either for a particular area of interest or for the body of annotations as a whole. Using this framework, we conducted a large-scale analysis of gene expression datasets from the NCBI Gene Expression Omnibus (GEO). In particular, we identified terms of interest for each dataset through semantic annotation of biomedical data, then tracked the behavior of these terms as a function of time. The preliminary results provide significant information about the progress and character of GO annotations over time. This framework is flexible enough to examine all or part of the GO annotations, across multiple species, and with various enrichment methods. We also discuss how this framework can be used to evaluate different annotation methods. For example, by comparing the performance of annotations generated with a particular method to the performance of canonical annotations, it is possible to determine their relative quality.

Applying ontology learning and multi-objective ant colony optimization method for focused crawling to meteorological disasters domain knowledge

Ontologies can lead to important improvements in the definition of a course's knowledge domain, in the generation of an adapted learning path, and in the assessment phase. This paper provides an initial discussion of the role of ontologies in the context of e-learning. Generally, automatic assessment is preferred over manual assessment to avoid bias errors, human errors and also conserves teacher's time. Evaluation through objective tests like multiple choice questions has gained a lot of importance in the e-assessment system. Here we have proposed an efficient ontology generation based on soft computing techniques in e-assessment for multiple choice questions. We have employed fuzzy logic incorporated with optimisation algorithm like modified cuckoo search algorithm. Here a set of rules are first designed for creating the ontology. The rules are generated using fuzzy logic and these rules are optimised in order to generate a better ontology structure.

Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed.Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources.These relationships correspond to slots in a frame-based ontology.This article proposes an initial process for semi-automatic extraction of non-taxonomic relationships of ontologies from textual sources.It uses Natural Language Processing (NLP) techniques to identify good candidates of non-taxonomic relationships and a data mining technique to suggest their possible best level in the ontology hierarchy.Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.

Since the boom of biomedical big data studies, various big data processing technologies have been developed rapidly. As an important form of knowledge representation, ontology has become an important means for the utilization and integration of biomedical big data. The emergence of new technologies for ontology development has resulted in the generation of many biomedical ontologies by many ontology development communities. The Open Biological and Biomedical Ontology Foundry, an academic organization for bio-ontology developers, has provided a set of principles to guide community-based open ontology construction. The Open Biological and Biomedical Ontology Foundry have also built many widely used ontologies, such as Gene Ontology, Human Phenotype Ontology, and Chemical Entities of Biological Interest. Other various ontology repositories have also been created and used to support ontology reuse. Many efficient tools for ontology applications, such as data annotation and terms mapping, have also been developed. High quality ontologies are also being used to develop new methods and tools for biomedical data analysis. The applications of Gene Ontology and Human Phenotype Ontology for data analysis and integration in recent years are reviewed here. To promote the development and applications of biomedical ontologies in China, a research community, OntoChina, was founded recently. OntoChina aims to support the development of reference ontologies, especially bilingual and Chinese translated ontologies. OntoChina also encourages ontology developers to follow the Open Biological and Biomedical Ontology Foundry principles.