Semantic Parsing for Knowledge Graph Question Answering

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Shanza Noor


Knowledge graphs are full of information, but understanding them all requires knowing about natural language questions. Semantic understanding plays a big role. It changes questions from users into forms that knowledge graphs can understand. But going over this bridge shows obvious breaks. One problem comes from the big words and hard connections in knowledge graphs. Ask about a "mountain range" between France and Spain. Nowadays, most programs are having problems with many things and connections, so they could get this question wrong, leading to useless answers. Another issue shows up in the area of talking with others. In real life, people often ask questions that are complex and build on previous answers to shape the meaning of future queries. A parser that doesn't understand the context might get confused if someone asks, "What is the capital of the country we talked about earlier?" After chatting about Paris without saying which country it belongs to. The bridge lacks provisions for generalization and interpretability. In this study we have proposed parsers encounter difficulties with novel questions, and their reasoning remains opaque. Envision inquiring about the inventor of the printing press and their motivations. A parser incapable of drawing parallels from comparable historical figures or elucidating its rationale for identifying Gutenberg would furnish users with incomplete answers, eroding trust in the process. Furthermore, our strategy endeavors to overcome these challenges, with the goal of constructing a resilient and efficient semantic parser—a bridge devoid of weaknesses and fissures. This endeavor aims to enable users to pose questions in a natural and conversational manner, thereby unleashing the complete potential of knowledge graphs and furnishing them with the precise answers they seek.

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Shanza Noor. (2024). Semantic Parsing for Knowledge Graph Question Answering. INTERNATIONAL JOURNAL OF HUMAN AND SOCIETY, 4(1), 33-45. Retrieved from