Browsing by Author "Sakthi Murugan, R."

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    NPRank: Nexus based Predicate Ranking of Linked Data
    (Institute of Electrical and Electronics Engineers Inc., 2019) Sakthi Murugan, R.; Ananthanarayana, V.S.
    In the typical use case of browsing Linked Data in DBpedia, the user would find an average of 180 facts attached to each entity. These facts are ordered alphabetically based on predicates, but a logical ordering of these facts is a better option. In this article, we present a Nexus based predicate ranking of Linked Data facts named NPRank. The key idea of NPRank is, the importance of a predicate is directly proportional to its familiarity among its group called Nexus. NPRank is a language and endpoint independent model allowing seamless integration and querying of data from multiple endpoints. Nexus score generated to rank predicates also assists in fragmentation of large data and bring in more hidden data from the SPARQL endpoints. Our experiments, conducted with the ranking of the Linked Data facts, corresponding to most visited pages of Wikipedia; from 275 active SPARQL endpoints, achieves better performance than the state-of-the-art methods. © 2019 IEEE.
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    WordCode using WordTrie
    (King Saud bin Abdulaziz University, 2022) Sakthi Murugan, R.; Ananthanarayana, V.S.
    Computers work with text data by assigning a code for each character, called encoding. Character-encoding techniques emerged in the late 1960s, and a similar type of technique is still used to encode text data. Computers can only understand alphabets, not words. In this article, we develop an approach that enables computers to understand words. We introduce a word-based encoding of text data named WordCode. WordCode encodes the most frequent set of characters (i.e., words) found in Internet directories with a dynamic code combination. Although some dictionary-encoding techniques have been proposed, we still tend to use character encoding, such as Unicode, to encode text data. Dictionary-encoding techniques have not been adopted due to the massive size of the code page and the complexity in accessing the code page. In this article, we introduce a customised trie named WordTrie to store words for faster encoding and decoding. We generate the code combination in such a way that the size of the WordCode for a word is always smaller than the total size of the character coding. Our experimental results from encoding text files from the Gutenberg corpus, Canterbury corpus, large corpus, Calgary corpus and Silesia corpus using WordCode show an up to 19.9% reduction in file size with respect to character-based encoding. This smaller file size means that less storage space is needed and results in faster processing and communication of text data. © 2019 The Authors