In this research, we present a Natural Language Understanding (NLU) component of an open-domain chatbot designed to extract explicit and implicit personal information from user dialogues, such as name, age, gender, education, occupation, marital status, residence, etc. The NLU component comprises two main units: explicit IE and implicit IE. The explicit IE unit detects the main topic (intent) of the utterance and extracts explicitly stated information to fill the values of predefined slots. It utilizes a custom joint training model leveraging the pretrained language model of XLM-RoBERTa and incorporating the previous utterance as an input feature. Training on a dataset of 6,652 utterances, it yields an intent accuracy of 91.22%, slot-F1 of 88.07% and exact match of 83.40%. The implicit IE unit is built upon an ontology containing common-sense knowledge, alongside an LLM-based inference engine. It infers the remaining slots based on explicit information or complementary data in the utterance. Our NLU component can even detect semantic conflicts in the dialogue and assessing the full pipeline from this aspect shows 92% accuracy which is 21% higher than GPT-4o.