A Comprehensive Framework for Ontology based Classifier using Unstructured Data

The knowledge contained within the natural language data can be used to build expert systems. Classifying unstructured data using ontology and text classification algorithms to extract information is one way of approaching the problem of building intelligent systems. One major problem with text processing is most data generated is unstructured and ambiguous, as, data with a structure helps to identify meaningful patterns and eventually exhibit the latent knowledge. Ambiguity in natural language affects accuracy of categorization. Also, Natural Language Processing techniques when combined with semantic data modeling through ontological knowledge will also solve the problem of domain knowledge representation thereby enabling improved data classification facilities, particularly in large datasets where number of features scale to unmanageable proportions. In this paper, the domain knowledge is presented as a knowledge graph, derived from the semantic data modeling. Further, to achieve better Multi Class classification, Multinomial Naive Bayes algorithm is applied to categorize items in their respective classes. For the experiments, Data about various news groups were used for testing the accuracy of the model. Experimental results have proved that the proposed classifier performs better compared to existing systems.

A Meta-Analysis Comparing Relational and Semantic Models

Data modeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic data modeling has emerged as an alternative to traditional relational modeling. The majority of the research in data modeling suggests that the use of semantic data models leads to better performance; however, the findings are not conclusive and are sometimes inconsistent. The discrepancies that exist in the data modeling literature and the relatively low statistical power in the studies make meta-analysis a viable choice in analyzing and integrating the findings of these studies.

OPEN ONTOLOGY-BASED INTEGRATION PLATFORM FOR MODELING AND SIMULATION IN ENGINEERING

The benefits of the use of modeling and simulation in engineering are acknowledged widely. It has proven its advantages e.g., in virtual prototyping i.e., simulation aided design and testing as well as in training and R&D. It is recognized to be a tool for modern decision making. However, there are still reasons that slow down the wider utilization of modeling and simulation in companies. Modeling and simulation tools are separate and are not an integrated part of the other engineering information management in the company networks. They do not integrate well enough into the used CAD, PLM/PDM and control systems. The co-use of the simulation tools themselves is poor and the whole modeling process is considered often to be too laborious. In this article we introduce an integration solution for modeling and simulation based on the semantic data modeling approach. Semantic data modeling and ontology mapping techniques have been used in database system integration, but the novelty of this work is in utilizing these techniques in the domain of modeling and simulation. The benefits and drawbacks of the chosen approach are discussed. Furthermore, we describe real industrial project cases where this new approach has been applied.

A Meta-Analysis Comparing Relational and Semantic Models

Data modeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic data modeling has emerged as an alternative to traditional relational modeling. The majority of the research in data modeling suggests that the use of semantic data models leads to better performance; however, the findings are not conclusive and are sometimes inconsistent. The discrepancies that exist in the data modeling literature and the relatively low statistical power in the studies make meta-analysis a viable choice in analyzing and integrating the findings of these studies.