Designing Personalized, Dynamic, and Multimodal Knowledge Graphs for Chatbots

Chatbots need more personalized knowledge graphs beyond open-domain sparse large graphs; dense, small graphs model robot self‑awareness and fine‑grained user profiles, as illustrated in Figure 1.

In addition to static graphs, chatbots require dynamic knowledge graphs that capture temporal information such as daily routines and real‑time user states (Figure 2), enabling the system to record events as N‑ary data across time and space.

Beyond objective facts, chatbots must incorporate subjective emotional knowledge, updating user mood states and robot affect to produce empathetic, context‑aware responses (Figure 3).

To fulfill user requests, chatbots must connect to external services or open APIs, extending traditional binary relational graphs into dynamic service graphs that model multi‑entity relationships, causality, and sequence dependencies (Figure 4).

Since humans interact via multimodal media, chatbot knowledge graphs should also include image, audio, and video information, leveraging resources like ImageNet and Visual Genome to support tasks such as Visual QA and personalized multimedia recommendations (Figure 5).

Overall, a comprehensive chatbot knowledge graph integrates heterogeneous, multi‑source data—static world knowledge, user profiles, robot attributes, social relations, emotions, interests, and daily activities—forming a spatio‑temporal mirror of interaction contexts (Figure 6).

After establishing the graph, NLP techniques such as tokenization, entity recognition, and disambiguation link user utterances to graph entities, enabling intent understanding and appropriate response generation.

Promotional Section: The article concludes with a giveaway for the new book "Natural Language Processing Practice: Principles and Applications of Chatbot Technology" by Wang Haofen, Shao Hao, etc. Readers who comment and like the post can win a copy; the book details and purchase link are provided.

Book details: title, authors, publication date (March 2019), and a brief description highlighting its coverage of chatbot classification, key technologies, case studies, and challenges, especially focusing on knowledge graphs and deep learning in NLP.

Purchase link: https://item.jd.com/12539314.html