Contextual Understanding in Neural Dialog Systems: the Integration of External Knowledge Graphs for Generating Coherent and Knowledge-rich Conversations

The integration of external knowledge graphs has emerged as a powerful approach to enrich conversational AI systems with coherent and knowledge-rich conversations. This paper provides an overview of the integration process and highlights its benefits. Knowledge graphs serve as structured representations of information, capturing the relationships between entities through nodes and edges. They offer an organized and efficient means of representing factual knowledge. External knowledge graphs, such as DBpedia, Wikidata, Freebase, and Google's Knowledge Graph, are pre-existing repositories that encompass a wide range of information across various domains. These knowledge graphs are compiled by aggregating data from diverse sources, including online encyclopedias, databases, and structured repositories. To integrate an external knowledge graph into a conversational AI system, a connection needs to be established between the system and the knowledge graph. This can be achieved through APIs or by importing a copy of the knowledge graph into the AI system's internal storage. Once integrated, the conversational AI system can query the knowledge graph to retrieve relevant information when a user poses a question or makes a statement. When analyzing user inputs, the conversational AI system identifies entities or concepts that require additional knowledge. It then formulates queries to retrieve relevant information from the integrated knowledge graph. These queries may involve searching for specific entities, retrieving related entities, or accessing properties and attributes associated with the entities. The obtained information is used to generate coherent and knowledge-rich responses. By integrating external knowledge graphs, conversational AI systems can augment their internal knowledge base and provide more accurate and up-to-date responses. The retrieved information allows the system to extract relevant facts, provide detailed explanations, or offer additional context. This integration empowers AI systems to deliver comprehensive and insightful responses that enhance user experience. As external knowledge graphs are regularly updated with new information and improvements, conversational AI systems should ensure their integrated knowledge graphs remain current. This can be achieved through periodic updates, either by synchronizing the system's internal representation with the external knowledge graph or by querying the external knowledge graph in real-time

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table