Browsing by Author "Sugandhika, C"

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    Assessing information quality of wikipedia articles through google's E-A-T model
    (IEEE, 2022) Sugandhika, C; Ahangam, S
    Along with the emergence of Web 2.0, User Generated Content (UGC) is becoming increasingly important for knowledge sharing. Wikipedia being the world’s largest-ever community-based collaborative encyclopedia, is also one of the biggest UGC databases in the world. Wikipedia is dealing with a significant problem of Information Quality (IQ) because of its open-source and collaborative nature. When carrying out attacks such as link spamming, malicious users take advantage of Wikipedia’s popularity on the World Wide Web (WWW). As a result, Wikipedia is generally not recommended for academic-related work. There are, however, some articles that are both rich in information and quality. Existing approaches for assessing Wikipedia’s IQ involve statistical models and machine learning algorithms; however, the existing models do not produce satisfactory results. In this study, a novel theoretical model based on Google’s E-A-T framework is introduced to assess Wikipedia’s IQ. The model comprises three IQ constructs Expertise, Authority and Trustworthiness. Based on the empirical findings and study results, a set of IQ dimensions that influence the above three IQ constructs, as well as 45 IQ attributes to measure the IQ dimensions, were identified. The IQ attributes were automatically and inexpensively extracted from the content and meta-data statistics of Wikipedia articles using a Selenium 3.14 web automation script. A sample of 2000 articles comprising 1000 Featured Articles (FA) and 1000 non-FA articles from six WikiProjects was used for the data analysis. The proposed model was compared with three previously published models in terms of classification and clustering accuracy. It received classification and clustering accuracies of 95% and 93% respectively, which is a drastic improvement over the existing models. Furthermore, an average inter-rater agreement of 84% was observed. Thus, the proposed model’s effectiveness is fairly validated by this extensive experiment. This study contributes to the related knowledge area by introducing a novel framework to assess Wikipedia articles’ IQ.
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    Heuristics-based sql query generation engine
    (Faculty of Information Technology, University of Moratuwa., 2021-12) Sugandhika, C; Ahangama, S; Ganegoda, GU; Mahadewa, KT
    A database is one of the prime media to store data. Most of the time, relational databases are preferred over other databases due to their ability to represent complex relationships between data. Languages like Structured Query Language (SQL) are used to retrieve data stored in relational databases. Information stored in these databases is often accessed by naïve users who do not possess high competencies in technical database querying. Therefore, Natural Language Interfaces to Databases (NLIDB) are being developed to translate natural language into SQL queries and retrieve the corresponding database results. This paper proposes a novel NLIDB called SQL Query Generation Engine which has been developed using a heuristics-based approach. The system was tested with more than 200 natural language queries and has shown an overall accuracy of 93%.
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    A scenario-based er diagram and query generation engine
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2019-12) Hettiarachchi, S; Sugandhika, C; Kathriarachchi, A; Ahangama, A; Weerasuriya, GT; Sudantha, BH
    Designing and developing a database is a crucial task in the System Development Life Cycle (SDLC). To design a database, it is essential to have proper knowledge about drawing Entity-Relationship (ER) Diagrams. Drawing ER diagrams is challenging for novices and people without a technical background. Furthermore, to retrieve data from a database requires expert domain knowledge about a database querying language like Structured Query Language (SQL). To address these issues, a system is proposed to identify and extract the necessary information from a given scenario to automatically generate the ER diagram. Based on that ER diagram, the system creates the database and is capable of generating SQL queries for any given type of natural language queries, in order to simplify accessing the data stored in the database.