Sharifullah Khan

Ontology based semantic information retrieval

Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries. The main drawback of the existing semantic-based information retrieval techniques is that none of them considers the context of the concept(s). We propose a semantic information retrieval framework to improve the precision of search results. In this paper, thematic similarity approach is employed for information retrieval in order to capture the context of particular concept(s). We store metadata information of source(s) in the form of RDF triples. We search userpsilas queries in the existing metadata by matching RDF triples instead of keywords. The results of the experiments performed on our framework showed improvements in precision and recall compared to the existing semantic-based information retrieval techniques.

Managing Change History in Web Ontologies

Ontologies changes in the sense of constantly growing in scientific discourse and being revised over time by different people. It refers to the fact that groups of professionals over time (e.g. in a longer term project) develop a common understanding based on joint interpretation of a shared terminology. The terminology will get structured and refined as the Communities of Practice concerned with a field of knowledge develop a deeper understanding of issues, thus moving from a loosely clustered terminology to a semi-formal or even formal ontology.Keeping trail of these changes in semantically rich and formally sound mechanism, on one hand, has pragmatic advantages for providing the undo and redo facility and to recover to a previous state of ontology. On the other hand, it also contributes in understanding the change process and identifying the patterns of changes in the ontologies. This research aims at developing semantic structure for comprehensively storing ontology changes, and the application of the stored changes. For change history management in evolving ontologies, we have modeled an ontology for change history.

R2O transformation system: relation to ontology transformation for scalable data integration

The R2O transformation system transforms database relations to OWL based ontology for a source. Compared with existing techniques, the distinguished feature of this system is to build ontology in the absence of necessary metadata from relations. It minimizes the effort and errors involved in manual ontology building. The evaluation shows the transformation is correct.

Building Local Ontology from Database Relations in Data Integration

Source models help in integrating distributed data sources. They need to be represented in conceptual model in order to provide scalable and flexible data integration of autonomous and heterogeneous data sources. Ontology is explicit specification of conceptualization and can handle semantic heterogeneity of the local sources. The technique in this paper, maps database relations to OWL based ontology for the local sources. It minimizes errors and effort involved in manual ontology building. Its distinguished feature is to create ontology from relations in the absence of necessary metadata. Compared with existing techniques, the technique builds ontology automatically and allows adding additional semantics. The technique is presented with examples in this paper.

Ontology Driven Query Expansion in Data Integration

There is huge explosion in the number of new databases, applications and documents in the recent past. This results into lot of redundancy and duplication, which leads to high inefficiency in query processing. Most of the users, who need the information, are naive, because they do not have knowledge of internal structures of databases, contents of data sources and query languages. So it is very difficult for them to query and analyze the desired data from autonomous, geographically distributed and heterogeneous data sources. Query expansion is used to answer the users¿ queries to improve performance and effectiveness of queries. We propose a solution for expanding the users¿ queries with the support of ontology so that recall is improved and information loss is minimized while answering users¿ queries. We have developed rules for expanding the semantically meaningful and illustrate with appropriate example. The results show that our rules are better in terms of recall.

LeedsCQ: A Scalable Continual Queries System

Continual Queries (CQs) are persistent queries that are issued once and then are run at regular intervals or when source data change until a termination condition is satisfied. Users receive new information automatically as it becomes available. CQs systems need to support a large number of CQs due to the scale of the Internet. This paper describes a novel architecture for a CQs system that scales to a large number of queries. In this system CQs are evaluated locally on the CQ server without accessing base relations after initial evaluation. Only group queries are run to retrieve auxiliary data. We optimize the retrieval of auxiliary data. A performance evaluation shows that the architecture reduces data transmission and I/O costs.

Transforming relational model to source ontology for data integration

Data integration can be made scalable, flexible and interoperable if the source descriptions are represented in conceptual model i.e., ontology. Ontology is a shared and common understanding of a domain and can solve the heterogeneity of distributed data sources. The methodology provided in this paper transforms database relations of a local source to OWL based ontology for source descriptions. It minimizes the effort and errors involved in manual ontology building. Compared with existing techniques the distinguished feature of the proposed technique is to build ontology in the absence of necessary metadata from physical or logical models. Results of the proposed methodology are provided to show the transformation is correct (i.e., total, injective).

Image retrieval based on fuzzy ontology

Rapid increase in digital images demands effective and efficient image retrieval systems. In text based image retrieval, images are annotated with keywords based on human perception. A user query is composed of keywords according to his/her requirements. Query keywords are matched with the keywords associated with images, for retrieval. This process has been extended with ontology to resolve semantic heterogeneities. However, crisp annotation and retrieval processes could not produce the desired results because both processes involve human perception. To overcome this problem, we have proposed a retrieval system that makes use of fuzzy ontology for improving retrieval performance. For modeling the semantic description of an image, it is divided into regions in our dataset and then regions are classified into concepts. The concepts are combined into categories. The concepts, categories and images are linked among themselves with fuzzy values in ontology. The retrieved results are ranked based on the relevancy between the keywords of a query and images. For evaluating the performance of the proposed methodology, we have used both the objective and subjective measures. Experimental results show that the proposed system performs better than the existing systems in terms of retrieval performance.

DSont: DSpace to ontology transformation

Semantic web facilitates the effective sharing and reuse of existing information. Institutional repositories (IRs) are built to organize and manage the intellectual output of an institute. They generally use relational databases for maintaining metadata of digital documents. The focus of this research is to share the information of an existing IR with other information systems for discovering common interests. To process the data in semantic context, a relational database needs to be transformed into an ontology. The existing relation to ontology transformation systems produces odd results if they are applied on an IR database because its schema is meta-schema. The proposed system first creates an intermediate database, having a normalized schema for the data model of an institute preserved in an IR database and then transforms it into an ontology. Finally semantic correspondence is established between entities of source and target ontologies in order to integrate them. The system has been implemented and evaluated for its correct and lossless transformation. The results demonstrate that the transformation is correct and the information is preserved.

Storage schema and ontology-independent SPARQL to HiveQL translation

Growing size of Semantic Web data demands scalable semantic stores. Hadoop-based distributed and parallel processing frameworks such as HBase and Hive are becoming increasingly popular for storing and retrieving voluminous data. Hive, more specifically, supports complex analytical processing but the query interface does not support data exploration using SPARQL, a standard query language for Semantic Web. We propose a semantic preserving SPARQL to HiveQL translation scheme that provides a querying interface for Hive in an attempt to realize a scalable semantic web triplestore. Major contributions of our research include: semantic preserving SPARQL to HiveQL query translation algorithm and storage schema-independent querying mechanism that accommodates different storage schemes without impacting translation time. The results demonstrate efficient working of proposed translation algorithm and that it supports different types of SPARQL queries.