Jahiruddin Jahiruddin

Jamia Millia Islamia · Department of Computer Science

Satire is prominent in user-generated content on various online platforms in the form of satirical news, customer reviews, blogs, articles, and short messages that are typically of an informal nature. As satire is also used to disseminate false information on the Internet, its computational detection has become a well-known issue. Existing work foc...

For a given pair of pattern and data graphs, the subgraph isomorphism finding problem locates all instances of the pattern graph into the data graph. For a given subgraph isomorphic image of the pattern graph in a data graph, the set of all ordered pairs of the pattern graph’s vertices and their respective images data graph is called an embedding....

The extensive use of digital devices by individuals generates a significant amount of private data which creates challenges for investigation agencies to protect suspects' privacy. Existing digital forensics models illustrate the steps and actions to be followed during an investigation, but most of them are inadequate to investigate a crime with al...

Linked Open Data (LOD) is an emerging Web technology to store and publish structured data in the form of interlinked knowledgebases like DBpedia, Freebase, Wikidata, and Yago. It uses structured data from multiple domains, and it can be used to conceptualize a concept of interest. Recently, researchers have shown that incorporating contextual featu...

Due to increasing volume and unstructured nature of the scientific literatures in biomedical domain, most of the information embedded within them remain untapped. This paper presents a biomedical text analytics system, DiseaSE (Disease Symptom Extraction), to identify and extract disease symptoms and their associations from biomedical text document...

Microblogging sites contain huge amount of textual data and their classification is an imperative task in many applications like information filtering, user profiling, topical analysis, and content tagging. Traditional machine learning approaches mainly use bag of words or n-gram techniques to generate feature vectors as text representation to trai...

Due to proliferation of competitive online Business-to-Consumer (B2C) models, it is becoming a challenging task for new users to choose best products, based on existing users' reviews residing on different e-commerce websites. On analysis, it is found that the opinions of the existing customers play an important role for new customers in making app...

Due to exponential growth of complex data, graph structure has become increasingly important to model various entities and their interactions, with many interesting applications including, bioinformatics, social network analysis, etc. Depending on the complexity of the data, the underlying graph model can be a simple directed/undirected and/or weig...

Large-scale influx of scientific literatures in the biomedical domain, enriched with various biomedical entities like genes, proteins, drugs, diseases, symptoms, microbes, pathogens etc. embed many useful information that remains untapped due to unstructured nature of texts. Processing these texts using NLP techniques, and extracting embedded entit...

Due to exponential growth of complex data, graph structure has become increasingly important to model various entities and their interactions, with many interesting applications including, bioinformatics, social network analysis, etc. Depending on the complexity of the data, the underlying graph model can be a simple directed/undirected and/or weig...

Since the inception of the Web 2.0, World Wide Web is widely being used as a platform by customers and manufactures to share experiences and opinions regarding products, services, marketing campaigns, social events, etc. As a result, there is enormous growth in user-generated contents (e.g. customer reviews), providing an opportunity for data analy...

In line with text-refining task and knowledge distillation approach of the text-mining process, in this chapter, the authors present the design of a Web content mining system that translates biological text documents into an intermediate representation (conceptual graph) using their syntax trees generated by the parser, which is then analyzed durin...

Due to existence of a huge amount of textual data either on the World Wide Web or in textual databases like PubMed, the development of novel automatic keyphrase extraction methods has emerged as one of the key research problems in recent past. Consequently, a number of machine learning techniques, mostly supervised, have been proposed to extract ke...

A number of techniques such as information extraction, document classification, document clustering and information visualization have been developed to ease extraction and understanding of information embedded within text documents. However, knowledge that is embedded in natural language texts is difficult to extract using simple pattern matching...

There is an exponential growth in user-generated contents in the form of customer reviews on the Web. But, most of the contents are stored in either unstructured or semi-structured format due to which distillation of knowledge from this huge repository is a challenging task. In addition, on analysis we found that most of the users use fuzzy terms i...

In this paper, we present a relation mining and visualization framework to identify important semi-structured information components using semantic and linguistic analysis of text documents. The novelty of the paper lies in identifying key snippet from text to validate the interaction between a pair of entities. The extracted information components...

In this paper, we present an opinion mining system to identify product features and opinions from review documents. The features and opinions are extracted using semantic and linguistic analysis of text documents. The polarity of opinion sentences is established using polarity scores of the opinion words through Senti-WordNet to generate a feature-...

The information age has made the electronic storage of large amounts of data effortless. The proliferation of documents available on the Internet, corporate Intranets, news wires, and elsewhere is overwhelming. Technological T&F Cat # C6847 Chapter: 20 page: 485 date: August 5, 2009 T&F Cat # C6847 Chapter: 20 page: 486 date: August 5, 2009 advance...

Semantic frameworks can be used to improve the accuracy and expressiveness of natural language processing for the purpose of extracting meaning from text documents. Such a framework represents knowledge using semantic networks and can be generated using information mined from text documents. The key issue however is to identify relevant concepts an...

In this paper we describe an information extraction and text mining system which identifies key information components from text documents. The information components are centered on domain entities and their relationships. The components mined from a repository are chained using an n-gram-based algorithm. The information chains provide a comprehen...