Method and system for inferring answers from knowledge graphs
AssigneeAccenture Global Solutions Limited
InventorsShubhashis Sengupta, Annervaz K. M., Gupta Aayushee, Sandip Sinha, Shakti Naik
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Abstract
Automated response generation systems and methods are disclosed. The systems can include a deep learning model specially configured to apply inferencing techniques to redesign natural language querying systems for use over knowledge graphs. The disclosed systems and methods provide a model for inferencing referred to as a Hierarchical Recurrent Path Encoder (HRPE). An entity extraction and linking module as well as a data conversion and generation module process the content of a given query. The output is processed by the proposed model to generate inferred answers.
Claims20
A computer implemented method of using a Hierarchical Recurrent Path Encoder (HRPE) model to control a virtual agent by generating responses to queries received by the virtual agent, the method comprising:
- receiving a natural language query;
- generating hypothesis based on the received query;
- receiving, at an attention component of the HRPE model, the hypothesis;
- encoding, by the HRPE model, a first path in a knowledge graph to generate a first encoding, the first path connecting to a first possible answer to the query;
- collectively encoding, by the HRPE model, a first plurality of paths in the knowledge graph to generate a second encoding, the first plurality of paths including the first path;
- generating, by the HRPE model, attention weights for each of the first encoding and the second encoding to produce weighted encodings;
- selecting, by the attention component and based on the hypothesis, an encoding from the weighted encodings based on which encoding is associated with a greater attention weight, the selected encoding corresponding to a first vector;
- determining, via a feed forward network layer of the HRPE model, whether the first vector follows the hypothesis or not;
- classifying the first vector based on output of the feed forward network layer, the classification indicating whether the selected encoding entails or contradicts the hypothesis;
- and
- generating, via an inference-based response generation module associated with the HRPE model, a natural language response to the query based on the classification.
A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to use a Hierarchical Recurrent Path Encoder (HRPE) model to control a virtual agent by generating responses to queries received by the virtual agent by performing the following operations:
- receive a natural language query;
- generate hypothesis based on the received query;
- receive, at an attention component of the HRPE model, the hypothesis;
- encode, by the HRPE model, a first path in a knowledge graph to generate a first encoding, the first path connecting to a first possible answer to the query;
- collectively encode, by the HRPE model, a first plurality of paths in the knowledge graph to generate a second encoding, the first plurality of paths including the first path;
- generate, by the HRPE model, attention weights for each of the first encoding and the second encoding to produce weighted encodings;
- select, by the attention component and based on the hypothesis, an encoding from the weighted encodings based on which encoding is associated with a greater attention weight, the selected encoding corresponding to a first vector;
- determine, via a feed forward network layer of the HRPE model, whether the first vector follows the hypothesis or not;
- classify the first vector based on output of the feed forward network layer, the classification indicating whether the selected encoding entails or contradicts the hypothesis;
- and
- generate, via an inference-based response generation module associated with the HRPE model, a natural language response to the query based on the classification.
A system for using a Hierarchical Recurrent Path Encoder (HRPE) model to control a virtual agent by generating responses to queries received by the virtual agent, the system comprising:
- one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to:
- receive a natural language query;
- generate hypothesis based on the received query;
- receive, at an attention component of the HRPE model, the hypothesis;
- encode, by the HRPE model, a first path in a knowledge graph to generate a first encoding, the first path connecting to a first possible answer to the query;
- collectively encode, by the HRPE model, a first plurality of paths in the knowledge graph to generate a second encoding, the first plurality of paths including the first path;
- generate, by the HRPE model, attention weights for each of the first encoding and the second encoding to produce weighted encodings;
- select, by the attention component and based on the hypothesis, an encoding from the weighted encodings based on which encoding is associated with a greater attention weight, the selected encoding corresponding to a first vector;
- determine, via a feed forward network layer of the HRPE model, whether the first vector follows the hypothesis or not;
- classify the first vector based on output of the feed forward network layer, the classification indicating whether the selected encoding entails or contradicts the hypothesis;
- and
- generate, via an inference-based response generation module associated with the HRPE model, a natural language response to the query based on the classification.
Dependencies are inferred from the claim text.