OntoCase-Automatic Ontology Enrichment Based on Ontology Design Patterns

In the research project NanOn: Semi-Automatic Ontology Construction—a Contribution to Knowledge Sharing in Nanotechnology an ontology for chemical nanotechnology has been constructed. Parts of existing ontologies like CMO and ChEBI have been incorporated into the final ontology. The main focus of the project was to investigate the applicability of text mining methods for ontology construction and for automatic annotation of scientific texts. For this purpose, prototypical tools were developed, based on open source tools like GATE and OpenNLP. It could be shown that text mining methods which extract significant terms from relevant articles support conceptualisation done manually and ensure a better coverage of the domain. The quality of the annotation depends mostly on the completeness of the ontology with respect to synonymous and specific linguistic expressions.

In this paper we present a new method based on semi-automatic ontology construction that can be used to improve the understandability of legal documents. Legal documents typically extensively define requirements and procedures in a specific legislative area; usually, they are hard to comprehend for citizens without a proper legal knowledge. However, a vast majority of today’s e-Government activities for citizens (G2C) are governed by legal documents. Therefore, by improving the citizen’s comprehension many intricacies that occasionally occur during G2C activities can be avoided. Our method first divides a legal document into several paragraphs. From the paragraphs it semi-automatically constructs ontology of the field by using a tool OntoGen. Ontology concepts are then used to classify each paragraph and the resulting classification is visualized in a simple matrix, where rows represent paragraphs and columns represent top-level ontology concepts. Based on the visualization, paragraphs that need revising are identified; they can be relocated to more suitable context within the document or rewritten using more appropriate wording. We demonstrate the presented method on the document defining the tender for selling flats at favorable prices at the Housing Fund of the Republic of Slovenia, a public fund. We argue that by using the new method we were able to substantially improve the comprehension of the document. In addition, the constructed ontology helped the Fund’s officers improve the structure of their knowledge about the underlying business process.Keywordsontology constructionknowledge managementlegal documents

Information and resources available on the Web are growing increasingly and web users need to have a common understanding of them. The Semantic Web whose most important role is to help machine to understand and analyze the existing data on the Web, has not been used commonly, yet. The foundation of the Semantic Web are ontologies. Ontologies play the main role in the exchange of information and development of the Lexical Web to the Semantic Web. Manual construction of ontologies is time-consuming, expensive, and dependent on the knowledge of domain engineers. Also, Ontologies that have been extracted automatically from corpus on the Web might have incomplete information. The main objective of this study is describing a method to improve and expand the information of the ontologies. Therefore, this study first discusses the automatic construction of prototype ontology in animals‟ domain from Wikipedia and then a method is presented to improve the built ontology. The proposed method of improving ontology expands ontology concepts through Bootstrapping methods using a set of concepts and relations in initial ontology and with the help of the Google search engine. A confidence measure was considered to choose the best option from the returned results by Google. Finally, the experiments showed the information that was obtained using the proposed method is twice more accurate than the information that was obtained at the stage of automatic construction of ontology from Wikipedia.

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.

Ontology Learning is a process of (semi)automatically creating, maintaining, and transferring various forms of information into an ontology with minimum human intervention to guarantee a better knowledge representation and sharing. In recent years, the research on automating financial data modeling has become a hot topic among researchers because of the exponential increase of the number of financial documents and the heterogeneous of financial data [19, 20]. So, we highlight the emergence of new computational tools and methods to deal with the automatic modeling and exploration of large financial corpus. That's why, we propose here a solution named Norms2Onto which is a semi-automatic ontology construction method based on machine learning algorithms to facilitate the reading and ease updating of financial data and to guarantee their understanding. Experiments have been conducted to measure the effectiveness of our solution compared to a manual classification made by an domain expert. The results show the superiority of our approach.

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.

Compare with SOAP-based service that based on interfaces access pattern, RESTful web service is HTTP-based and use resource as the operation object. On the purpose of supporting RESTful in the enterprise information application, a Resource-oriented Information Supported Framework is constructed based on ontology construction and evolution. First, the initial ontology with relational pattern is extracted from the enterprise legacy databases. Users can generate the domain ontology through the evolution of initial ontology led by enterprise elements. And then evolve domain ontology to resource model according to the virtual resource in enterprise. Finally user can describe the resources based on resource model to meet the REST Web Service. In order to support the dynamic, flexible, and scalable enterprise application, the framework also provides a semantic reference for RESTful service invocation. The prototype system has been developed now in order to implement the application, which includes a semantic uniform view in complex data environments of enterprise so as to build an information environment for RESTful services execution. Considering there are an increasing research and application tendency in the field of resource-oriented approach, the Ontology-based Resourceoriented Information Supported Framework provides a semantic-level enterprise information base not only for heterogeneous data integration but also for RESTful services application.

The explosive growth of textual data on the web coupled with the increase on demand for ontologies to promote the semantic web, have made the automatic ontology construction from the text a very promising research area. Ontology learning (OL) from text is a process that aims to (semi-) automatically extract and represent the knowledge from text in machine-readable form. Ontology is considered one of the main cornerstones of representing the knowledge in a more meaningful way on the semantic web. Usage of ontologies has proven to be beneficial and efficient in different applications (e.g. information retrieval, information extraction, and question answering). Nevertheless, manually construction of ontologies is time-consuming as well extremely laborious and costly process. In recent years, many approaches and systems that try to automate the construction of ontologies have been developed. This paper reviews various approaches, systems, and challenges of automatic ontology construction from the text. In addition, it also discusses ways the ontology construction process could be enhanced in the future by presenting techniques from shallow learning to deep learning (DL).

Ontologies and knowledge bases are fundamental cornerstones in semantic search systems based on which efficient web services and sophisticated search mechanisms are designed. High quality domain ontologies are essential for successful employment of semantic Web services. However, acquisition of ontologies is difficult and costly, thus hindering the development of this field. In this work, the knowledge acquisition problem in the semantic Web is cast as the task of ontology construction. We construct an ontology for “polycystic kidney disease” from domain text by a pattern based method which requires minimum human intervention. We also create ontology manually to present the evaluation results of the constructed ontology.KeywordsOntologyontology constructionpatternsconcept extraction

Ontology learning has become a popular research field recently. However, the typical ontology may not be sufficient to represent uncertainty information. Fuzzy ontology is proposed to solve the uncertainty reasoning problems. But the construction of fuzzy ontology is still a tedious and painstaking task. The cognitive model of fuzzy ontology learning is an automatic model of fuzzy ontology construction that simulates the process of human being recognizing the world. Induction is an important step of the model. In this paper, we present a strategy for the fuzzy ontology induction in the cognitive model of ontology learning which includes some generalization principles and the corresponding induction operators. It generates induction hypothesis through a series of operations. As a result, induction hypothesis are generated from the present ontology and it can generalize the existing ontology.

Enhancing relevant concepts extraction for ontology learning using domain time relevance

Ontology learning has become a popular research field recently. But most ontology construction methods are semiautomatic, and the construction process of ontology is still a tedious and painstaking task. The cognitive model of ontology learning is an automatic model of ontology construction that simulates the process of human being recognizing the world. Induction is an important step of the model. This paper presents a strategy for the ontology induction in the cognitive model of ontology learning, which includes some generalization principles and the corresponding induction operators. It generates induction hypothesis through a series of operations. As a result, induction hypothesis are generated from the present ontology and it can generalize the existing ontology.

The operation of Ultra-High Voltage (UHV) power grids generates massive quantities of unstructured textual data, primarily in the form of dispatch logs, maintenance manuals, and accident reports. While these documents contain critical information regarding system states and operational procedures, their unstructured nature hinders automated processing and intelligent decision support. This paper proposes a comprehensive framework for ontology construction and logical consistency reasoning specifically tailored for UHV grid operation texts. By transforming unstructured textual data into a structured knowledge base, we aim to enhance the semantic interoperability of power system data. The methodology involves a domain-specific ontology construction phase that integrates the Common Information Model with linguistic characteristics of operation logs, followed by a semantic reasoning mechanism designed to detect logical inconsistencies in operational commands. We employ deep learning techniques for entity and relation extraction to populate the ontology, subsequently applying description logic rules to validate operational sequences against safety regulations. The experimental results demonstrate that the proposed framework significantly improves the accuracy of identifying unsafe operational directives and conflicting equipment states compared to traditional keyword-based approaches. This research provides a theoretical foundation and practical tools for realizing cognitive intelligence in modern smart grids.

Most approaches to ontology learning combine techniques from different areas (hybrid approaches) to increase the efficiency of the ontology learning process. However, the results from the ontology learning process do not fully satisfy the users at present. An important problem is that there is a lack of quantitative and comparative data about the efficiency of techniques and technique combinations applied to ontology learning. In this paper we present a quantitative comparison of technique combinations for concept extraction and a software system (OntoLancs) to support the evaluation of techniques. By applying OntoLancs, users are able to assist the process of building ontologies by semi- automatically acquiring concepts from large-scale domain document collections and experiment with different combinations of knowledge acquisition techniques to refine and organize domain concepts into a taxonomy. Quantitative and comparative studies about the performance of several techniques and user experiences indicate the applicability and usefulness of our approach.

Domain ontology, encompassing both concepts and entities, along with their relations and properties, is a new medium for the storage and propagation of domain specific knowledge. For all of its associated advantages, a great deal of effort must still be expended during ontology construction, including collecting the domain-related vocabularies, developing the domain concept hierarchy, defining the properties of each concept and relationships between concepts. In recent years, several engineering handbooks have described detailed domain knowledge via organization into categories, sections, and chapters with indices in the appendix. In this paper, an approach is proposed with a view to extracting concepts, entities, properties and relationships from a handbook of a specific domain. The extracted information can then be reorganized and converted into OWL (Web Ontology Language) format to represent the domain ontology. The generation of an earthquake engineering ontology from an Earthquake Engineering Handbook is used to illustrate the proposed