A Methodology for Engineering Domain Ontology using Entity Relationship Model
01 Jan 2019-International Journal of Advanced Computer Science and Applications (The Science and Information (SAI) Organization Limited)-Vol. 10, Iss: 8
TL;DR: This research work presents a lightweight approach to build domain ontology using Entity Relationship (ER) model, and investigates the domain of information technology curriculum for the successful interpretation of concepts, attributes, relationships of concepts and constraints among the concepts of the ontology.
Abstract: Ontology engineering is an important aspect of semantic web vision to attain the meaningful representation of data. Although various techniques exist for the creation of ontology, most of the methods involve the number of complex phases, scenario-dependent ontology development, and poor validation of ontology. This research work presents a lightweight approach to build domain ontology using Entity Relationship (ER) model. Firstly, a detailed analysis of intended domain is performed to develop the ER model. In the next phase, ER to ontology (EROnt) conversion rules are outlined, and finally the system prototype is developed to construct the ontology. The proposed approach investigates the domain of information technology curriculum for the successful interpretation of concepts, attributes, relationships of concepts and constraints among the concepts of the ontology. The experts’ evaluation of accurate identification of ontology vocabulary shows that the method performed well on curriculum data with 95.75% average precision and 90.75% average recall.
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01 Jan 2017
TL;DR: Computer simulations offer a way to test the effectiveness of organisational changes suggested and implemented routinely as an answer to increasing patient demands and financial restrictions in healthcare.
Abstract: In healthcare, organisational changes are suggested and implemented routinely as an answer to increasing patient demands and financial restrictions. Computer simulations offer a way to test these n ...
2 citations
Posted Content•
TL;DR: In this paper, a conceptual model is created, in the form of an Entity-relationship (ER) model, and transformed to a relational database schema (RDS) to create the database.
Abstract: In database development, a conceptual model is created, in the form of an Entity-relationship(ER) model, and transformed to a relational database schema (RDS) to create the database. However, some important information represented on the ER model may not be transformed and represented on the RDS. This situation causes a loss of information during the transformation process. With a view to preserving information, in our previous study, we standardized the transformation process as a one-to-one and onto mapping from the ER model to the RDS. For this purpose, we modified the ER model and the transformation algorithm resolving some deficiencies existed in them. Since the mapping was established using a few real-world cases as a basis and for verification purposes, a formal-proof is necessary to validate the work. Thus, the ongoing research aiming to create a proof will show how a given ER model can be partitioned into a unique set of segments and use it to represent the ER model itself. How the findings can be used to complete the proof in the future will also be explained. Significance of the research on automating database development, teaching conceptual modeling, and using formal methods will also be discussed.
1 citations
TL;DR: In this paper , an enhanced ecological and confined domain ontology construction (EC-DOC) scheme is proposed for structured knowledge management. But it does not provide details of the activities and methods involved in the construction of an ontology, which may cause difficulty in implementing the ontology.
Abstract: Knowledge management in a structured system is a complicated task that requires common, standardized methods that are acceptable to all actors in a system. Ontology, in this regard, is a primary element and plays a central role in knowledge management, interoperability between various departments, and better decision making. The ontology construction for structured systems comprises logical and structural complications. Researchers have already proposed a variety of domain ontology construction schemes. However, these schemes do not involve some important phases of ontology construction that make ontologies more collaborative. Furthermore, these schemes do not provide details of the activities and methods involved in the construction of an ontology, which may cause difficulty in implementing the ontology. The major objectives of this research were to provide a comparison between some existing ontology construction schemes and to propose an enhanced ecological and confined domain ontology construction (EC-DOC) scheme for structured knowledge management. The proposed scheme introduces five important phases to construct an ontology, with a major focus on the conceptualizing and clustering of domain concepts. In the conceptualization phase, a glossary of domain-related concepts and their properties is maintained, and a Fuzzy C-Mean soft clustering mechanism is used to form the clusters of these concepts. In addition, the localization of concepts is instantly performed after the conceptualization phase, and a translation file of localized concepts is created. The EC-DOC scheme can provide accurate concepts regarding the terms for a specific domain, and these concepts can be made available in a preferred local language.
1 citations
01 Jul 2021
TL;DR: In this article, a semantic query expansion (QE) strategy is proposed to expand the query with additional terms, which are semantically similar to the original query, but this strategy does not consider individual user interest in the generation of expansion terms.
Abstract: Retrieving user-relevant content from a large volume of data available on the Web via an input query is a difficult task. A user query may not be able to specify user information needs due to the ambiguous and limited number of query terms. The semantic query expansion (QE) strategy offers a solution to this problem by expanding the query with additional terms, which are semantically similar to the original query. However, this strategy does not consider individual user interest in the generation of expansion terms. In this article, semantic QE is improved by combining the notion of ontology knowledge and user interest. The proposed semantic QE technique involves a computing domain of the input query via ontology, generates expansion terms from the user browsing history, and finally selects expansion terms that represent user preferences on the basis of the semantic similarity between expansion terms and query and user feedback. The experimental evaluation indicates that expanded queries produced by the proposed technique retrieve more personalized contents over Web search than initial user queries. The obtained results achieve 86.4% average precision, which proves a positive impact of incorporating user preferences in semantic QE.
References
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01 Jan 2003
TL;DR: This chapter analyses the limitations of RDF Schema and derive requirements for a richer Web Ontology Language, and describes the three-layered architecture of the OWL language.
Abstract: In order to extend the limited expressiveness of RDF Schema, a more expressive Web Ontology Language (OWL) has been defined by the World Wide Web Consortium (W3C). In this chapter we analyse the limitations of RDF Schema and derive requirements for a richer Web Ontology Language. We then describe the three-layered architecture of the OWL language, and we describe all of the language constructs of OWL in some detail. The chapter concludes with two extensive examples of OWL ontologies.
1,251 citations
"A Methodology for Engineering Domai..." refers methods in this paper
...The semantic interpretation of ICT ER-schema associates each component of ER to ontology vocabulary (such as entity to a concept, attribute, and relationship to datatype property or object property, and cardinality to restriction) using OWL-Lite language [24]....
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01 Jul 2005
TL;DR: This volume presents current research in ontology learning, addressing three perspectives, including methodologies that have been proposed to automatically extract information from texts and to give a structured organization to such knowledge, including approaches based on machine learning techniques.
Abstract: This volume brings together ontology learning, knowledge acquisition and other related topics It presents current research in ontology learning, addressing three perspectives The first perspective looks at methodologies that have been proposed to automatically extract information from texts and to give a structured organization to such knowledge, including approaches based on machine learning techniques Then there are evaluation methods for ontology learning, aiming at defining procedures and metrics for a quantitative evaluation of the ontology learning task; and finally application scenarios that make ontology learning a challenging area in the context of real applications such as bio-informatics According to the three perspectives mentioned above, the book is divided into three sections, each including a selection of papers addressing respectively the methods, the applications and the evaluation of ontology learning approaches
488 citations
Additional excerpts
...In [18], authors have presented a novel technique for ontology construction....
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TL;DR: The fact that people haven't yet created as many useful ontologies as the ontology research community would like might indicate either unresolved technical limitations or the existence of sound rationales for why individuals refrain from building them - or both.
Abstract: For years, ontologies have been known in computer science as consensual models of domains of discourse, usually implemented as formal definitions of the relevant conceptual entities. Researchers have written much about the potential benefits of using them, and most of us regard ontologies as central building blocks of the semantic Web and other semantic systems. Unfortunately, the number and quality of actual, "non-toy" ontologies available on the Web today is remarkably low. This implies that the semantic Web community has yet to build practically useful ontologies for a lot of relevant domains in order to make the semantic Web a reality. Theoretically minded advocates often assume that the lack of ontologies is because the "stupid business people haven't realized ontologies' enormous benefits." As a liberal market economist, the author assumes that humans can generally figure out what's best for their well-being, at least in the long run, and that they act accordingly. In other words, the fact that people haven't yet created as many useful ontologies as the ontology research community would like might indicate either unresolved technical limitations or the existence of sound rationales for why individuals refrain from building them - or both. Indeed, several social and technical difficulties exist that put a brake on developing and eventually constrain the space of possible ontologies
205 citations
14 Dec 2015
TL;DR: This work synthesize and highlight the most relevant work regarding ontology methodologies, engineering, best practices and tools that could be applied to Internet of Things (IoT).
Abstract: We discuss in this paper, semantic web methodologies, best practices and recommendations beyond the IERC Cluster Semantic Interoperability Best Practices and Recommendations (IERC AC4). The semantic web community designed best practices and methodologies which are unknown from the IoT community. In this paper, we synthesize and highlight the most relevant work regarding ontology methodologies, engineering, best practices and tools that could be applied to Internet of Things (IoT). To the best of our knowledge, this is the first work aiming at bridging such methodologies to the IoT community and go beyond the IERC AC4 cluster. This research is being applied to three uses cases: (1) the M3 framework assisting IoT developers in designing interoperable ontology-based IoT applications, (2) the FIESTA-IoT EU project encouraging semantic interoperability within IoT, and (3) a collaborative publication of legacy ontologies.
99 citations
"A Methodology for Engineering Domai..." refers background in this paper
...[13] argued that this feature makes the ontology dependent on particular application or task; thus, making its reusability low....
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TL;DR: This document is written for readers who want a first impression of the capabilities of OWL by informally describing the features of each of the sublanguages of OWl by providing an introduction to OWL.
Abstract: The OWL Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. OWL has three increasingly-expressive sublanguages: OWL Lite, OWL DL, and OWL Full. This document is written for readers who want a first impression of the capabilities of OWL. It provides an introduction to OWL by informally describing the features of each of the sublanguages of OWL. Some knowledge of RDF Schema is useful for understanding this document, but not essential. After this document, interested readers may turn to the OWL Guide for more detailed descriptions and extensive examples on the features of OWL. The normative formal definition of OWL can be found in the OWL Semantics and Abstract Syntax.
36 citations
"A Methodology for Engineering Domai..." refers background in this paper
...Ontology is a source of explicit specification of domain concepts, properties, constraints and security [1]....
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