Adaptive Organization of Digital Documents using Knowledge Graphs
Ramakrishna Bairi
10.4225/03/5a9e377107082
https://bridges.monash.edu/articles/thesis/Adaptive_Organization_of_Digital_Documents_using_Knowledge_Graphs/5951068
This thesis studies the problem of automatically evolving a hierarchy of categories to organize the documents in a collection, considering user preferences (e.g., categories biased to a particular field). It makes use of a massive knowledge graph to guide the machine learning models to evolve the category structure and organizes the documents accordingly. The categorization also adapts to the growing document collection. It also presents a novel technique for categorizing “short texts” having very few words. This work has applications in machine learning tasks such as automatic creation of “Wikipedia Disambiguation” like pages, automatic generation of Table of Contents, drill-down search, etc.
2018-05-24 17:22:30
Categorization
Hierarchical Organization
Summarization
Associative Markov Networks
Submodular Optimization
Short Texts
Automatic Organization
Gibbs Sampling
Pattern Recognition and Data Mining
Information Retrieval and Web Search
Information Systems
Information Systems Organisation