{'fraction_non_alphanumeric': 0.012373453318335208, 'fraction_numerical': 0.0, 'mean_word_length': 5.4963503649635035, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
DB PEDIA BASED FACTOID QUESTION ANSWERING SYSTEM

Question Answering (QA) is an area of natural language processing research aimed at providing human users with a convenient and natural interface for accessing information. Nowadays, the need to develop accurate systems gains more importance due to available structured knowledge-bases and the continuous demand to access information rapidly and efficiently.In This paper we propose a new architecture to develop a factoid question answering system based on the DBpedia ontology and the DBpedia extraction framework. We use the SVM learning machine algorithm to train the systems question classifier to achieve a high accuracy ratio. The design and implementation steps are covered with sufficient details. In addition, tests and experiment results of the developed system are presentedwith a short discussion about the systems efficiency.