Arvind Chandrasekaran, Colorado Technical University, USA

The knowledge-Based Question Answering (KBQA) system is an essential part of the customer service system aiming to answer natural language questions by recovering the structured data stored under the knowledge base. KBQA answers the natural language questions by recovering the structured data stored under the knowledge base. KBQA receives the user's query and first needs to recognize the topic for the query entities like the location, name, organization, etc., The process is Named Entity Recognition (NER) using the Bidirectional Long Short-Term Memory Conditional Random Field model, and the SoftLexicon method is introduced as the Chinese NER tasks. A fuzzy matching module is proposed to analyze the application scenario characteristics using multiple methods. The module efficiently modifies the error recognition results, improving entity recognition performance. The fuzzy matching and the NER model are combined into the NER system. The power grid-related original data is collected to improvise the system following the power grid data characteristics.

Knowledge-Based Question Answering; SoftLexicon method; Knowledge graph learning representation; System Of Question answering; Knowledge; Spatial Temporal;