Integration of knowledge graph embedding into topic modeling with hierarchical Dirichlet process

A non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by at least one processor, causes steps to be performed comprising:

• given a corpus of documents in which each document comprises words and entities:
• using entity embeddings obtained from a knowledge graph to represent entities in the corpus of documents;
• and
• for each document in the corpus of documents, generating a word frequency representation for words from the document;
• using the entity embeddings and the word frequency representations as an input to a topic model to generate latent topics for the corpus of documents, the topic model comprising:
• a corpus-level Dirichlet process that uses the word frequency representations and the entity embeddings to obtain a shared base measure that is used as a prior for two document-level Dirichlet processes;
• a first document-level Dirichlet process that uses the shared base measure as a prior to generate a latent topic distribution for words;
• a second document-level Dirichlet process that uses the shared base measure as a prior to generate a latent topic distribution for entity embeddings;
• a word generative process that uses the latent topic distribution for words and a word-level Dirichlet process to assign latent topics to words;
• and
• using the latent topic distribution for entity embeddings and a distribution to assign latent topics to entity embeddings;
• and
• assigning one or more topics to one or more documents from the corpus of documents using at least one or more of the latent topics generated by the topic model.

A computing system comprising:

• at least one processor;
• and
• a non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by at least one of the one or more processors, causes steps to be performed comprising:
• given a corpus of documents in which each document comprises words and entities:
• using entity embeddings obtained from a knowledge graph to represent entities in the corpus of documents;
• and
• for each document in the corpus of documents, generating a word frequency representation for words from the document;
• and
• using the entity embeddings and the word frequency representations as an input to a topic model to generate latent topics for the corpus of documents, the topic model comprising:
• a corpus-level Dirichlet process that uses the word frequency representations and the entity embeddings to obtain a shared base measure that is used as a prior for two document-level Dirichlet processes;
• a first document-level Dirichlet process that uses the shared base measure as a prior to generate a latent topic distribution for words;
• a second document-level Dirichlet process that uses the shared base measure as a prior to generate a latent topic distribution for entity embeddings;
• a word generative process that uses the latent topic distribution for words and a word-level Dirichlet process to assign latent topics to words;
• and
• using the latent topic distribution for entity embeddings and a distribution to assign latent topics to entity embeddings;
• and
• assigning one or more topics to one or more documents from the corpus of documents using at least one or more of the latent topics generated by the topic model.