Jagdev Bhogal

• PhD Computer Science
• Professor (Associate) at Birmingham City University

This paper examines the use of ontologies for defining query context. The information retrieval system used is based on the probabilistic retrieval model. We extend the use of relevance feedback (RFB) and pseudo-relevance feedback (PF) query expansion techniques using information from a news domain ontology. The aim is to assess the impact of the o...

Intelligent mobile advertising is an important area for business growth. The foundation of this growth is largely dependent on the semantic web that relies on ontologies to provide various taxonomies. The Intelligent Mobile Advertising Service (iMAS) ontology is developed from its roots in marketing, enabling the development of the Mobile Semantic...

This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and d...

The rapid expansion of Artificial Intelligence(AI) has outpaced the development of ethical guidelines and regulations, raising concerns about the potential for bias in AI systems. These biases in AI can manifest in real-world applications leading to unfair or discriminatory outcomes in areas like job hiring, loan approvals or criminal justice predi...

Today, we have access to a vast data amount, especially on the internet. Online news agencies play a vital role in this data generation, but most of their data is unstructured, requiring an enormous effort to extract important information. Thus, automated intelligent event detection mechanisms are invaluable to the community. In this research, we f...

Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way – through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes w...

The rapid growth of social media networks has resulted in the generation of a vast data amount, making it impractical to conduct manual analyses to extract newsworthy events. Thus, automated event detection mechanisms are invaluable to the community. However, a clear majority of the available approaches rely only on data statistics without consider...

Even though social media contain rich information on events and public opinions, it is impractical to manually filter this information due to data’s vast generation and dynamicity. Thus, automated extraction mechanisms are invaluable to the community. We need real data with ground truth labels to build/evaluate such systems. Still, to the best of o...

The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. A...

Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and hi...

During the last few decades, the widespread growth of scholarly networks and digital libraries has resulted in an explosion of publicly available scholarly data in various forms such as authors, papers, citations, conferences, and journals. This has created interest in the domain of big scholarly data analysis that analyses worldwide dissemination...

Event detection in social media refers to automatic identification of important information shared in social media platforms on a certain time. Considering the dynamic nature and high volume of data production in data streams, it is impractical to filter the events manually. Therefore, it is important to have an automated mechanism to detect events...

Early detection of emerging research trends could potentially revolutionise the way research is done. For this reason, trend analysis has become an area of paramount importance in academia and industry. This is due to the significant implications for research funding and public policy. The literature presents several emerging approaches to detectin...

Research in analysis of big scholarly data has increased in the recent past and it aims to understand research dynamics and forecast research trends. The ultimate objective in this research is to design and implement novel and scalable methods for extracting knowledge and computational history. While citations are highly used to identify emerging/r...

Word embeddings are increasingly attracting the attention of researchers dealing with semantic similarity and analogy tasks. However, finding the optimal hyper-parameters remains an important challenge due to the resulting impact on the revealed analogies mainly for domain-specific corpora. While analogies are highly used for hypotheses synthesis,...

Cloud computing is gathering significant momentum in business and academia through the rich benefits it offers. It is apparent from the literature that both businesses and academic institutions would benefit greatly from the adoption of cloud technology, providing the challenges presented are overcome. This paper aims to review prevalent literature...

The smartphone market and its relative technology is expanding rapidly. Due to smartphone's rich user interface and growing numbers, smartphones are becoming ubiquitous workplace devices for many prime priority applications such Business Intelligence (BI). This paper reviews the literature on recent developments in Mobile BI, conducts a comparison...