Baidu USA LLC

Richard Chun Ching Wang

Yu Long Cao

Hongliang Fei

Wei Fan

Yi Zhen

Chaochun Liu

Dawen Zhou

Shulong Tan

Jing Zhai

Nan Du

Yusheng Xie

Erheng Zhong

Bocong Liu

Systems and methods are disclosed for question generation to obtain more related medical information based on observed symptoms from a patient. In embodiments, possible diseases associated with the observed symptoms are generated by querying a knowledge graph. In embodiments, candidate symptoms associated with the possible diseases are also identified and are combined with the observed symptoms to obtain combined symptom sets. In embodiments, discriminative scores for the candidate symptom sets are determined and candidate symptoms with top discriminative scores are selected. In embodiments, these selected candidate symptoms may be checked for conflicts with observed symptoms and removed from further consideration if a conflict exists. In embodiments, one or more questions may be generated based on the remaining selected candidate systems to aid in collecting information about the patient. In embodiments, the process may be repeated with the updated observed symptoms.

Question generation systems and methods for automating diagnosis

Systems and methods are disclosed for extending and reinforcing a knowledge graph using wiki-like web pages as a source of information. A web crawler parse a wiki-like source and obtain a topic entity from the source. Relationships between the topic entity and sub-topics within the source are identified and a graph is built with the topic and relationships to sub-topics. A candidate topic in the knowledge graph is identified, and a sub-graph of the knowledge graph is either identified or generated. The knowledge sub-graph contains the candidate topic and relationships to sub-topics. A similarity is computed between the source graph and the knowledge sub-graph. If the two graphs are sufficiently similar, then the source topic graph is merged with the knowledge graph.

Knowledge graph entity reconciler

Systems and methods are disclosed to identify entities that have a similar meaning, and may, in embodiments, be grouped into entity groups for knowledge base construction. In embodiments, the entity relations of similarity or non-similarity for an entity pair are predicted as a binary relationship. In embodiments, the prediction may be based upon similarity score between the entities and the entity features, which features are constructed using an entity feature or representation model. In embodiments, the prediction may be an iterative process involving minimum human checking and existing knowledge update. In embodiments, one or more entity groups are formed using graph search from the predicted entity pairs. In embodiments, a group centroid entity may be selected to represent each group based on one or more factors, such as its generality or popularity.

Systems and methods for homogeneous entity grouping

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Mountain View, CA, United States of America

Richard Chun Ching Wang