Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Ontology based approach for event detection in twitter datastreams(Institute of Electrical and Electronics Engineers Inc., 2015) Kaushik, R.; Apoorva Chandra, S.; Mallya, D.; Chaitanya, J.N.V.K.; Kamath S․, S.In this paper, we present a system that attempts to interpret relations in social media data based on automatically constructed dataset-specific ontology. Twitter data pertaining to the real world events such as the launch of products and the buzz generated by it, among the users of Twitter for developing a prototype of the system. Twitter data is filtered using certain tag-words which are used to build an ontology, based on extracted entities. Wikipedia data on the entities are collected and processed semantically to retrieve inherent relations and properties. The system uses these results to discover related entities and the relationships between them. We present the results of experiments to show how the system was able to effectively construct the ontology and discover inherent relationships between the entities belonging to two different datasets. © 2015 IEEE.Item Sociopedia: An interactive system for event detection and trend analysis for twitter data(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2016) Kaushik, R.; Apoorva Chandra, S.; Mallya, D.; Chaitanya, J.N.V.K.; Kamath S․, S.The emergence of social media has resulted in the generation of highly versatile and high volume data. Most web search engines return a set of links or web documents as a result of a query, without any interpretation of the results to identify relations in a social sense. In the work presented in this paper, we attempt to create a search engine for social media datastreams, that can interpret inherent relations within tweets, using an ontology built from the tweet dataset itself. The main aim is to analyze evolving social media trends and providing analytics regarding certain real world events, that being new product launches, in our case. Once the tweet dataset is pre-processed to extract relevant entities, Wiki data about these entities is also extracted. It is semantically parsed to retrieve relations between the entities and their properties. Further, we perform various experiments for event detection and trend analysis in terms of representative tweets, key entities and tweet volume, that also provide additional insight into the domain. © Springer India 2016.Item Enhancing web service discovery using meta-heuristic CSO and PCA based clustering(Springer Verlag service@springer.de, 2018) Kotekar, S.; Kamath S․, S.Web service discovery is one of the crucial tasks in service-oriented applications and workflows. For a targeted objective to be achieved, it is still challenging to identify all appropriate services from a repository containing diverse service collections. To identify the most suitable services, it is necessary to capture service-specific terms that comply with its natural language documentation. Clustering available Web services as per their domain, based on functional similarities would enhance a service search engine’s ability to recommend relevant services. In this paper, we propose a novel approach for automatically categorizing the Web services available in a repository into functionally similar groups. Our proposed approach is based on the Meta-heuristic Cat Swarm Optimization (CSO) Algorithm, further optimized by Principle Component Analysis (PCA) dimension reduction technique. Results obtained by experiments show that the proposed approach was useful and enhanced the service discovery process, when compared to traditional approaches. © Springer Nature Singapore Pte Ltd. 2018.Item fastText-Based Siamese Network for Hindi Semantic Textual Similarity(Springer Science and Business Media Deutschland GmbH, 2025) Chandrashekar, A.; Rushad, M.; Nambiar, A.; Rashmi, V.; Koolagudi, S.G.Semantic textual similarity is a measurement of the degree of similarity or equivalence between two sentences semantically. Semantic sentence pairs have the ability to substitute text from each other and retain their meaning. Various rule-based and machine learning models have gained quick prominence in the field, especially in a language like English, where there is an abundance of lexical tools and resources. However, other languages like Hindi have not seen much improvement in state-of-the-art methods and models to evaluate semantic similarity of text data. This paper proposes a fastText-based Siamese neural network architecture to evaluate the semantic equivalency between a Hindi sentence pair. The pair is scored on a scale of 0–5, where 0 indicates least similar and 5 indicates most similar. The corpus contains a combination of two datasets containing manually scored sentence pairs. The performance parameters used to evaluate this approach are model accuracy and model loss over a training period of multiple epochs. The proposed architecture incorporates a fastText-based embedding layer and a bi-directional Long Short Term Memory layer to achieve a similarity score. The proposed architecture can extract semantic and various global features of the text to determine a similarity score. This model achieves an accuracy of 85.5% on a compiled Hindi-Hindi sentence pair dataset, which is a considerable improvement over existing rule and supervise-based systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.