My reaserch interest lies in Graph Querying and Mining, Data Mining, Machine Learning, Algorithms, and Social Networks. My current research work is focused on querying of large-scale, semi-structured and heterogeneous network data. In the domain of semi-structured data, e.g., DBpedia, Online Government Records, Social networks, there exist relations between two entities. However, such relations are not very strict or typed, unlike the relational data. In other words, usually there is no standardized schema for the semi-structured data. Thus, without knowing the exact structure of the data and the semantics of the entity attributes and their relationships, can we still query them and get the relevent results? In our current work, we propose a graph-based query processing system, where both the input data and query are modeled as graphs; and we perform neighborhood-based subgraph matching for answering queries.
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