Ontology revision on the semantic web: integration of belief revision theory

The vision of the Semantic Web is to enable content of web resources to be interpreted and processed by software agents. Ontology provides a means to share and reuse data associated with web resources in a manner that can be autonomously performed by software agents. In the context of knowledge representation, ontology represents the abstract world of web resources in the Semantic Web. The Semantic Web will comprise of small, simple ontologies constructed by individual users. It is unlikely that ontology will be built from scratch each time. On the other hand, it is more likely that ontology will be adopted and modified from existing ontology. Why is ontology revision important? Very often, ontology exists in a particular period of timeline is designed based on the purpose of a specific domain of interest at that instance of time. Over time, ontology needs to be revised due to changes in domain, content, requirements, or structural representation. In this regard, ontology is the beliefs that the agents need to reference to in order to perform task in an autonomous way. As ontology evolves, beliefs in agents also evolve and knowledge gained by agents must be reflected in the ontology. This research investigates issues of ontology revision from the theoretical foundation of the belief revision theory. The AGM model of the coherence theory in belief revision is of particular relevant in this research. The AGM model uses three operations of expansion, contraction and revision in conjunction with the concept of epistemic entrenchment to revise the belief set. This research develops an ontology revision framework to manage the ontology revision process. The research will also illustrate a vision in which the practicability of this approach can be applied in e-commerce

A Framework for Case-based Reasoning Integration on Knowledge Management Systems

To support the sharing and reusing of well-defined knowledge among knowledge management systems, it is useful to use standardised formalisation. It is also common effort to difficulty of knowledge acquisition known as knowledge acquisition bottleneck. In this paper investigates the feasibility of using techniques in case-based reasoning of artificial intelligence for the knowledge acquisition phase in knowledge management systems. The need of an ontological approach of the semantic web for well-defined set of domain knowledge is proposed in order to avoid knowledge acquisition bottleneck. Our viewpoint of this approach is that the ontology-driven mechanism allows us to provide standardised structured vocabularies and conceptualisation of knowledge domain. Over the standardised platform, we see an alternative to share and reuse homogenous information and knowledge in the knowledge management systems

Intelligent Knowledge Acquisition with Case-Based Reasoning Techniques

Knowledge management systems are an emerging area gaining interest in organisations. This paper discusses the application of case based reasoning techniques and intelligent agents in the knowledge acquisition phase of knowledge management systems so that an intelligent knowledge acquisition process is possible

Developing ontology revision framework: A case study on the use of the coherence theory for semantic shopping mall

Why is ontology revision important? Very often, ontology exists in a particular period of timeline is often designed based on the purpose of a domain of interest at that instance of time. However over time, ontology needs to be revised due to changes in content, environment, requirements, or even structural representation. As a result, revision and updating of necessary components in the pre-defined ontology is unavoidable. When this happens, it is important to ensure that revision is conducted in a consistent manner so that it does not result in unforseen redundancies and inconsistencies. Any revision performed must be accompanied by a rational change to be dealt with from the consistency perspective. This paper presents an ontology revision approach to achieve this aim based on the coherence theory model of belief revision theory. An application scenario of semantic shopping mall is used to demonstrate the approach

Harvesting Wind Energy from Aerodynamic Design for Building Integrated Wind Turbines

Alternative energy, nowadays, becomes more necessary than fossil fuels which might be destructing and polluting the earth’s environment. Wind can be one of the most cheap, secure, environment friendly and reliable energy supplies. Building Integrated Wind Turbine (BIWT) is becoming increasingly common as a green building icon and new method of assessing optimal building energy. However, to employ BIWT, it is important to design the building shape and swept area carefully to increase wind velocity. Some of numerous design forms of BIWT will be explained in this paper using CFD (Computational Fluid Dynamics) analysis to find the most effective BIWT design in urban area. This paper will focus on the maximum wind velocity which passes the swept area to get maximum wind power. The result shows that, building energy can be optimized through aerodynamic building design to get the maximum wind power for building energy consumption

Antidiabetic Effect of Fresh Nopal ( Opuntia ficus-indica

The objective of the present study was to evaluate α-glucosidase inhibitory and antidiabetic effects of Nopal water extract (NPWE) and Nopal dry power (NADP) in low-dose streptozotocin- (STZ-) induced diabetic rats fed a high-fat diet (HFD). The type 2 diabetic rat model was induced by HFD and low-dose STZ. The rats were divided into four groups as follows: (1) nondiabetic rats fed a regular diet (RD-Control); (2) low-dose STZ-induced diabetic rats fed HFD (HF-STZ-Control); (3) low-dose STZ-induced diabetic rats fed HFD and supplemented with NPWE (100 mg/kg body weight, HF-STZ-NPWE); and (4) low-dose STZ-induced diabetic rats fed HFD and supplemented with comparison medication (rosiglitazone, 10 mg/kg, body weight, HF-STZ-Rosiglitazone). In results, NPWE and NADP had IC50 values of 67.33 and 86.68 μg/mL, both of which exhibit inhibitory activities but lower than that of acarbose (38.05 μg/mL) while NPWE group significantly decreased blood glucose levels compared to control and NPDP group on glucose tolerance in the high-fat diet fed rats model (P<0.05). Also, the blood glucose levels of HR-STZ-NPWE group were significantly lower (P<0.05) than HR-STZ-Control group on low-dose STZ-induced diabetic rats fed HFD. Based on these findings, we suggested that NPWE could be considered for the prevention and/or treatment of blood glucose and a potential use as a dietary supplement

An Ontology-Based Collaborative Interorganizational Knowledge Management Network

Web contents can be represented in a structural form by a finite list of vocabularies and their relationships using ontologies. The concept of ontology and its related mediation methods is capable of enhancing the collaboration among Knowledge Management (KM) approaches that only focus on managing organizational knowledge. Those KM approaches are developed in accordance with organizational KM strategies and business requirements without the concern of system interoperation. In this research, an ontology-based collaborative inter-organizational KM network is proposed to provide a platform for organizations to access and retrieve inter-organizational knowledge in a similar domain

Story Visualization by Online Text Augmentation with Context Memory

Story visualization (SV) is a challenging text-to-image generation task for the difficulty of not only rendering visual details from the text descriptions but also encoding a long-term context across multiple sentences. While prior efforts mostly focus on generating a semantically relevant image for each sentence, encoding a context spread across the given paragraph to generate contextually convincing images (e.g., with a correct character or with a proper background of the scene) remains a challenge. To this end, we propose a novel memory architecture for the Bi-directional Transformers with an online text augmentation that generates multiple pseudo-descriptions as supplementary supervision during training, for better generalization to the language variation at inference. In extensive experiments on the two popular SV benchmarks, i.e., the Pororo-SV and Flintstones-SV, the proposed method significantly outperforms the state of the arts in various evaluation metrics including FID, character F1, frame accuracy, BLEU-2/3, and R-precision with similar or less computational complexity.Comment: ICCV 202