3D city model construction adopting OGC standards is the need of the hour for many countries. Research is heading towards effective methods for data collection for the 3D city model development process and disseminating all 3D city objects data of a specific user choice of region, through OGC web services. The current research work aims at building 3DCity LOD1 models using 2D geospatial data, disseminating the 3DCity objects information through OGC web services for a geographic region, building an ADE for modelling critical infrastructure such as electric lines, water pipes, etc., and modelling 2D critical infrastructure data in 3D CityGML model using the ADE. Models can be visualized using any CityGML visualizer like FME, Aristoteles, etc. The CityGML model is validated using OGC CityGML schema validator. Sample region based WFS results are shown along with the WFS performance test using a web services benchmark. CI ADE is validated through writing powerlines geospatial feature into CityGML model.

Information interoperability and semantic information retrieval are the major challenges in the era of globalization. Interoperability enables data sharing, information exchange and coordinate actions among multidisciplinary applications. Ontology is a prevalent semantic technology that provides support to address the information interoperability issues. The objective of the current research is twofold: 1) address information interoperability in multidisciplinary applications and data integration 2) ontology concept based semantic information retrieval on the integrated data. The current work provides a generic framework that facilitates information interoperability by addressing semantic and syntactic heterogeneity in multidisciplinary applications data. The framework integrates and exchange data from multiple domains. The performance and efficiency of the framework are evaluated through a real time and multidisciplinary application to test the effect of information interoperability on semantic information retrieval. A query set that covers multiple domains is prepared to test the performance of ontology concept based semantic information retrieval. The evaluation test uses popular relevancy measures that are precision, recall, and F-measure.

The data from pre, post and during disasters play a very important and vital role while immediate decision making in emergency management. Data availability, analysis, and exchange build up a critical knowledge base that helps in taking the right decision at the right time. Voluminous data is available through various sources in heterogeneous formats. It is a very big challenge for utilization of such massive heterogeneous data in disaster management situations. Disaster data is widely available in various documents and formats. Past disasters, anticipated disasters and during the disasters, a large amount of data is getting exchanged in various documents between different agencies. Effective techniques to extract and gather the data from heterogeneous documents helps in right data extraction and build up a knowledge base to formulate ease of analysis and modeling. Ontology based semantic technology is a powerful mechanism that supports information integration, exchange, share, reuse and build a knowledge base from heterogeneous information sources. The current research work focuses on three aspects of knowledge base system for effective disaster management. The current research work implemented a mechanism to extract relevant information from semi-structured and structured heterogeneous documents using NLP, representing extracted information in homogeneous and machine understandable format using RDF and map the RDF triples to appropriate concepts of the disaster management domain ontologies. This is very novel and effective means of data usage mechanism to help and guide policy makers, disaster mitigation agencies and society at large.

Ontology is a formal, explicit specification of a shared conceptualization. Ontology provides domain vocabulary, domain knowledge, common understanding, shareability, information interoperability, reusability, concept hierarchy, and relationships that support semantic information retrieval. Ontology improves performance of the system by addressing interoperability issues due to semantic and syntactic heterogeneity. Vast numbers of application domain experts are using ontologies in diverse applications. Use of effective and efficient ontology storage system results improved performance in applications and enables semantic information retrieval. Many prominent researchers and software agencies have proposed and developed several ontology storage methods and tools with various features. The choice of a specific storage model/tool always depend on the specific purpose of the application and the nature of features that are available in the storage model/tool to be utilized in the specific applications. The familiarity of various ontology storage models and tools with the respective features helps user to choose an appropriate storage structure aiming at high-performance applications. The current research work is a comprehensively authentic study carryout out on various ontology storage models and tools with their respective features, which are very essential for optimum performance.

Remote sensing satellite images are rich, in providing information. Satellites are producing huge number of images and data at regular intervals. Satellite image classification is a powerful method to extract valuable information from massive satellite images. Satellite image classification is a process of grouping pixels/regions into meaningful categories. Many prominent researchers have proposed several satellite image classification methods. However, a hybrid approach of multilevel and multiple classifications would definitely produce more accurate results. The current research work describes ontology based multiple and multilevel generic framework for satellite image classification. The proposed framework has two phases 1) knowledge base construction 2) semantic interpretation using multiple and multilevel satellite image classification. First phase builds domain knowledge and rules through expert experiences. Ontologies are used to represent domain knowledge. Ontologies are, inevitable aspect of knowledge representation, processing, sharing and reuse and this itself is the essence and significance of ontology. Ontologies represent domain knowledge in machine understandable format. Second phase implements multiple and multiple image segmentation and group regions into meaningful categories with the support of domain knowledge and classification rules. Finally satellite image classification results are stored in RDF format. RDF provides a common structure and represents satellite image classification results in machine understandable format. RDF representation of classification results enables application interoperability, information exchange and semantic information retrieval etc.

Satellites and ocean based observing system consists of various sensors and configurations. These observing systems transmit data in heterogeneous file formats and heterogeneous vocabulary from various data centers. These data centers maintain a centralized data management system that disseminates the observations to various research communities. Currently, different data naming conventions are being used by existing observing systems, thus leading to semantic heterogeneity. In this work, sensor data interoperability and semantics of the data are being addressed through ontologies. The present work provides an effective technical solution to address semantic heterogeneity through semantic technologies. These technologies provide interoperability, capability to build knowledge base, and framework for semantic information retrieval by developing an effective concept vocabulary through domain ontologies. The paper aims at a new methodology to interlink the multidisciplinary and heterogeneous sensor data products. A four phase methodology has been implemented to address satellite data semantic interoperability. The paper concludes with the evaluation of the methodology by linking and interfacing multiple ontologies to arrive at ontology vocabulary for sensor observations. Data from Indian Meteorological satellite INSAT-3D satellite have been used as a typical example to illustrate the concepts. This work on similar lines can also be extended to other sensor observations.

The advancement of technology in the area of satellite remote sensing has been generating voluminous amount of satellite images at regular intervals regularly. The satellite image analysis derive very useful, invaluable and essential inputs for various applications such as disaster management, civil aviation, meteorology, ocean observation systems, earth science studies and defense applications, etc. The satellite image analysis technique provides the necessary inputs for image preprocessing systems as a basic prerequisite and essential ingredient. Over the past several years many satellite image preprocessing tools have been developed with various features. The choice of satellite image preprocessing tools basically depend on the purpose of image analysis, the features of satellite image preprocessing tools and the specific need and application. Due to a large number of applications of satellite images, user has a wide ranging choice to select optimally the right preprocessing tool for the right application. Thus, the present study helps in aiding the proper understanding and selection of the various satellite image preprocessing tools and their features for any specific application. To facilitate this scheme, the current study has identified various satellite image preprocessing tools and prominent features of the desired tool. Briefly, the study presents a clear understanding with specific examples on the various available tools and features for the user specific application for the desired satellite images. The various features of the satellite image preprocessing tools have been presented in detail for ready reference, guidance and usage.

Satellite image classification process involves grouping the image pixel values into meaningful categories. Several satellite image classification methods and techniques are available. Satellite image classification methods can be broadly classified into three categories 1) automatic 2) manual and 3) hybrid. All three methods have their own advantages and disadvantages. Majority of the satellite image classification methods fall under first category. Satellite image classification needs selection of appropriate classification method based on the requirements. The current research work is a study on satellite image classification methods and techniques. The research work also compares various researcher’s comparative results on satellite image classification methods.

The capability of semantic technology leads to adaption of semantic technology to multiple applications of various domains. Due to vast number of applications, the size of RDF triple store is increasing. Effective semantic query execution has become a challenge due to the structure of RDF triple store. Effective indexing and partitioning leads to good sematic query performance against RDF triple store. The current research work has focused on various indexing techniques and proposed a predicate centric partitioning and multiple RDF indexing method for database triple store. A detailed analysis process is been executed to measure and compare the query performance. The current method is evaluated using standard benchmark and real datasets with various indexing techniques. Later the methodology is applied to R&D project management dataset. A set of twenty seven queries has been derived by considering various user requirements that cover most of the SPARQL constructs. The method is implemented and a detailed evaluation has been successfully carried out. The query time is evaluated on R&D project management dataset. The test results indicate that the proposed method provides considerable improvement in overall query performance.

Recent studies in the decision making efforts in the area of public healthcare systems have been tremendously inspired and influenced by the entry of ontology. Ontology driven systems results in the effective implementation of healthcare strategies for the policy makers. The central source of knowledge is the ontology containing all the relevant domain concepts such as locations, diseases, environments and their domain sensitive inter-relationships which is the prime objective, concern and the motivation behind this paper. The paper further focuses on the development of a semantic knowledge-base for public healthcare system. This paper describes the approach and methodologies in bringing out a novel conceptual theme in establishing a firm linkage between three different ontologies related to diseases, places and environments in one integrated platform. This platform correlates the real-time mechanisms prevailing within the semantic knowledgebase and establishing their inter-relationships for the first time in India. This is hoped to formulate a strong foundation for establishing a much awaited basic need for a meaningful healthcare decision making system in the country. Introduction through a wide range of best practices facilitate the adoption of this approach for better appreciation, understanding and long term outcomes in the area. The methods and approach illustrated in the paper relate to health mapping methods, reusability of health applications, and interoperability issues based on mapping of the data attributes with ontology concepts in generating semantic integrated data driving an inference engine for user-interfaced semantic queries.

The three concepts of information science are data, information and knowledge. The structure of one is different from another. The structure of knowledge is more complex than data and information. Knowledge management is complex for traditional information management techniques due its complex structure and difficult to achieve common structure for knowledge captured from heterogeneous sources. Ontology is a upright technology to represent knowledge. Ontology provides homogeneous structure for knowledge acquired from heterogeneous sources. It enables knowledge sharing within and among organizations. Ontology based knowledge management provides a better support for integration of related knowledge sources and searching. The current work proposes a enhanced and clear framework for knowledge management using domain ontology. It addresses major issues of traditional and existing ontology based knowledge management systems.

Semantic Web is an intelligent incarnation and advancement in World Wide Web to store, annotate, index and retrieve information by providing knowledge representation, classification, and categorization, a common understanding among the group of people and structuring the information in machine processable format.To structure the information in machine processable semantic models Semantic Web have introduced the concept of ontology.Ontologies are most widely used in artificial intelligence, semantic web, software engineering, information retrieval, knowledge representation, knowledge sharing, knowledge integration, knowledge reuse, and so on.Music plays a vital role in human life.A vast number of musical instruments are used to produce rhythmic sounds.The rapid growth of technology enables on line music.The instruments used and the terms used to describe the concepts varies from individual groups.To support concept based knowledge representation and semantic retrieval music files music instruments knowledge should be identified and represented.This raises the need for construction of Indian music instruments ontology.This ropes semantic web with more intelligent, capable, relevant, responsive interaction and high performance retrieval system.The Indian music instrument ontology is been constructed and the paper concludes with conclusion and future work.

This paper presents a detailed survey on ontology development tools. Ontology development tools used for building a new ontology from scratch or reusing existing ontologies. Some of the popular ontology construction tools are Ontolingua Server, WebOnto, OilEd, OntoSaurus, Protégé, SWOOP, TopBraid Composer, WebODE, OntoEdit and NeOn toolkit. This survey article briefly describes the ontology development and it presents comparison summary of the ontology tools with respect to their features.

Ontologies are an inevitable aspect of knowledge representation, processing, sharing and reuse and this in itself is the essence and significance of ontology. The number of ontologies available online is growing at a rapid pace. This conspicuous growth of ontologies enables sharing of knowledge in distributed and heterogeneous systems. The tremendous growth of ontologies has implied the popping up of multiple ontologies of the same domain. Applications developed based on these ontologies cannot achieve interoperability. To conquer this problem one of the optimum solution is to merge the ontologies in to a single general complete ontology. This brings up the need for a method which merges multiple ontologies, producing consistent, complete ontology. Another decisive factor worth consideration is the performance of multiple ontology merge process in terms of time. The current research work proposes a new cluster based multiple ontology parallel merge process. The performance of the multiple ontology merge process is fine-tuned by considering the similarity measure and parallel merge process. The proposed method produces consistent, optimum and complete knowledge of the domain as a single ontology with reduced time, which can be used by heterogeneous applications. The proposed method is implemented and a comparison of time complexity of the processing is presented at the end.

Day by day the volume of information availability in the web is growing significantly. There are several data structures for information available in the web such as structured, semi-structured and unstructured. Majority of information in the web is presented in web pages. The information presented in web pages is semi-structured. But the information required for a context are scattered in different web documents. It is difficult to analyze the large volumes of semi-structured information presented in the web pages and to make decisions based on the analysis. The current research work proposed a frame work for a system that extracts information from various sources and prepares reports based on the knowledge built from the analysis. This simplifies  data extraction, data consolidation, data analysis and decision making based on the information presented in the web pages.The proposed frame work integrates web crawling, information extraction and data mining technologies for better information analysis that helps in effective decision making.  It enables people and organizations to extract information from various sourses of web and to make an effective analysis on the extracted data for effective decision making. The proposed frame work is applicable for any application domain. Manufacturing,sales,tourisum,e-learning are various application to menction few.The frame work is implemetnted and tested for the effectiveness of the proposed system and the results are promising.

SPARQL is one of the powerful query language for querying semantic data. It is recognized by the W3C as a query language for RDF. As an efficient query language for RDF, it has defined several query result formats such as CSV, TSV and XML etc. These formats are not attractive, understandable and readable. The results need to be converted in an appropriate format so that user can easily understand. The above formats require additional transformations or tool support to represent the query result in user readable format. The main aim of this paper is to propose a method to build HTML report dynamically for SPARQL query results. This enables SPARQL query result display, in HTML report format easily, in an attractive understandable format without the support of any additional or external tools or transformation.

Ontology plays a vital role in semantic web, knowledge engineering and information retrieval. The paper presents a comprehensive survey on ontology engineering methodologies. Ontology methodology is a set of procedures and guidelines derived from ontology development experiences. Some of the popular ontology methodologies are Sensus, Kactus, Uschold and King, Gruninger and Fox, Ontology development 101, Methontology, On-To-Knowledge, UPON, HCOME, DILIGENT, NeOn. This survey article describes the ontology engineering methodologies and it presents comparison summary of the ontology methodologies with respect to their features.

Ontology plays a vital role in semantic technologies. Ontology language is a formal languages used to build ontologies. Some of the popular ontology languages developed by different researchers are Ontolingua, OCML, LOOM, FLogic, SHOE, XOL, RDF(S), OIL, DAML+OIL, OWL and OWL2. This paper presents a detailed survey on ontology languages and their comparison. This survey article briefly describes the ontology languages and presents a comparison summary of the ontology languages with respect to their constructs and features. The comparison of the ontology languages help user to select a suitable language to implement an ontology in the domain of interest.