arXiv subjects

Rudra Pratap Deb Nath

Publications and source records attributed to Rudra Pratap Deb Nath.

4 recordsLinked to original sources

arXiv2026

BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION

Cyberbullying is a growing concern in online communities, yet existing Bengali corpora remain limited in contextual information, annotation transparency, scale, accessibility, and support for multidimensional analysis, thereby hindering robust detection and comprehensive study of harasser behaviors and networks. To address these gaps, we introduce the Bengali Contextual and Diverse Cyberbullying (\textit{BenCoDiC}) corpus, a publicly available contextual cyberbullying resource for Bengali political news discourse comprising 50,250 textual instances collected from 100 YouTube videos and their associated discussions. The corpus preserves caption--comment--reply relationships and is organized through a nine-dimensional star schema covering users, videos, textual content, context, time, engagement, political parties, target identities, and language categories. Five AI chatbots were initially evaluated for annotation but did not meet the predefined reliability criterion. Consequently, a two-tier human annotation process with expert verification was adopted, achieving Fleiss' $κ=0.86$ and Krippendorff's $α=0.86$. Statistical analyses characterize the corpus, while transformer-based experiments show that incorporating conversational context yields modest but consistent improvements over isolated-text classification. \textit{BenCoDiC} supports context-aware cyberbullying detection and perpetrator-oriented behavioral and network analysis. The corpus is available at https://github.com/bencodic/BenCoDiC-Corpus.

cs.CL↗

arXiv2024

Bangladesh Agricultural Knowledge Graph: Enabling Semantic Integration and Data-driven Analysis--Full Version

In Bangladesh, agriculture is a crucial driver for addressing Sustainable Development Goal 1 (No Poverty) and 2 (Zero Hunger), playing a fundamental role in the economy and people's livelihoods. To enhance the sustainability and resilience of the agriculture industry through data-driven insights, the Bangladesh Bureau of Statistics and other organizations consistently collect and publish agricultural data on the Web. Nevertheless, the current datasets encounter various challenges: 1) they are presented in an unsustainable, static, read-only, and aggregated format, 2) they do not conform to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, and 3) they do not facilitate interactive analysis and integration with other data sources. In this paper, we present a thorough solution, delineating a systematic procedure for developing BDAKG: a knowledge graph that semantically and analytically integrates agriculture data in Bangladesh. BDAKG incorporates multidimensional semantics, is linked with external knowledge graphs, is compatible with OLAP, and adheres to the FAIR principles. Our experimental evaluation centers on evaluating the integration process and assessing the quality of the resultant knowledge graph in terms of completeness, timeliness, FAIRness, OLAP compatibility and data-driven analysis. Our federated data analysis recommend a strategic approach focused on decreasing CO$_2$ emissions, fostering economic growth, and promoting sustainable forestry.

cs.CY↗

arXiv2020

High-Level ETL for Semantic Data Warehouses -- Full Version

The popularity of the Semantic Web (SW) encourages organizations to organize and publish semantic data using the RDF model. This growth poses new requirements to Business Intelligence (BI) technologies to enable On-Line Analytical Processing (OLAP)-like analysis over semantic data. The incorporation of semantic data into a Data Warehouse (DW) is not supported by the traditional Extract-Transform-Load (ETL) tools because they do not consider semantic issues in the integration process. In this paper, we propose a layer-based integration process and a set of high-level RDF-based ETL constructs required to define, map, extract, process, transform, integrate, update, and load (multidimensional) semantic data. Different to other ETL tools, we automate the ETL data flows by creating metadata at the schema level. Therefore, it relieves ETL developers from the burden of manual mapping at the ETL operation level. We create a prototype, named Semantic ETL Construct (SETLCONSTRUCT), based on the innovative ETL constructs proposed here. To evaluate SETLCONSTRUCT, we create a multidimensional semantic DW by integrating a Danish Business dataset and an EU Subsidy dataset using it and compare it with the previous programmable framework SETLPROG in terms of productivity, development time and performance. The evaluation shows that 1) SETLCONSTRUCT uses 92% fewer Number of Typed Characters (NOTC) than SETLPROG, and SETLAUTO (the extension of SETLCONSTRUCT for generating ETL execution flow automatically) further reduces the Number of Used Concepts (NOUC) by another 25%; 2) using SETLCONSTRUCT, the development time is almost cut in half compared to SETLPROG, and is cut by another 27% using SETLAUTO; 3) SETLCONSTRUCT is scalable and has similar performance compared to SETLPROG.

cs.DB↗

arXiv2015

An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique

The proliferation of heterogeneous data sources of semantic knowledge base intensifies the need of an automatic instance matching technique. However, the efficiency of instance matching is often influenced by the weight of a property associated to instances. Automatic weight generation is a non-trivial, however an important task in instance matching technique. Therefore, identifying an appropriate metric for generating weight for a property automatically is nevertheless a formidable task. In this paper, we investigate an approach of generating weights automatically by considering hypotheses: (1) the weight of a property is directly proportional to the ratio of the number of its distinct values to the number of instances contain the property, and (2) the weight is also proportional to the ratio of the number of distinct values of a property to the number of instances in a training dataset. The basic intuition behind the use of our approach is the classical theory of information content that infrequent words are more informative than frequent ones. Our mathematical model derives a metric for generating property weights automatically, which is applied in instance matching system to produce re-conciliated instances efficiently. Our experiments and evaluations show the effectiveness of our proposed metric of automatic weight generation for properties in an instance matching technique.

cs.AI↗