KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence
- Title
- KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence
- Creator
- Ashvanth R.; Deepak G.; Sheeba Priyadarshini J.; Santhanavijayan A.
- Description
- The emergence of Web 3.0 has left very few tag recommendation structures compliant with its complex structure. There is a critical need for newer novel methods with improved accuracy and reduced complexity for tag recommendation, which complies with the Web 3.0 standard. In this paper, we propose KMetaTagger, a knowledge-centric metadata-driven hybrid tag recommendation framework. We consider the CISI dataset as the input, from which we identify the most informative terms by applying the Term Frequency - Inverse Document Frequency (TF-IDF) model. Topic modeling is done by Latent Semantic Indexing (LSI). A heterogeneous information network is formalized. Apart from this, the Metadata generation quantifies the exponential aggregation of real-world knowledge and is classified using Gated recurrent units(GRU). The Color Harmony algorithm filters out the initial feasible solutions into optimal solutions. This advanced solution set is finalized into the tag space. These tags are recommended along with the document keywords. When the suggested KMetaTagger's performance is compared to that of baseline techniques and models, it is found to be far superior. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Source
- Lecture Notes in Networks and Systems, Vol-646 LNNS, pp. 1-11.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Color Harmony; GRU; LSI; Tag Recommendation; Tagging; TF IDF
- Coverage
- Ashvanth R., Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India; Deepak G., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Bengaluru, India; Sheeba Priyadarshini J., Deparment of Data Science, CHRIST (Deemed to Be University), Bangalore, India; Santhanavijayan A., Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303127439-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Ashvanth R.; Deepak G.; Sheeba Priyadarshini J.; Santhanavijayan A., “KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence,” CHRIST (Deemed To Be University) Institutional Repository, accessed March 1, 2025, https://archives.christuniversity.in/items/show/19914.