Learning neuro-symbolic multi-hop reasoning rules over text

1. A computer-implemented method for generating neuro-symbolic rules, the computer-implemented method comprising:

inputting training texts and pre-defined entities into a neural model, wherein the training texts and the pre-defined entities relate to a domain;

generating an entity graph including nodes and edges, wherein the nodes represent the pre-defined entities and the edges represent passages in the training texts with a co-occurrence of the pre-defined entities connected together by the edges;

determining a relation based on the passages for each of the pre-defined entities connected together by the edges;

calculating a probability relating to the relation for each of the pre-defined entities;

generating a potential reasoning path between a head entity and a target entity;

scoring the potential reasoning path based on a beam search of the potential reasoning path using the-probabilities of the edges;

learning a neuro-symbolic rule by converting the edges along the potential reasoning path into symbolic rules and combining the symbolic rules into the neuro-symbolic rule; and

applying the neuro-symbolic rule to perform multi-hop reasoning, thereby increasing a possibility that an entity answer selected using the multi-hop reasoning is correct.

2. The computer-implemented method of claim 1 , further comprising:

inputting answers into the neural model; and

scoring the potential reasoning path based on the answers.

3. The computer-implemented method of claim 1 , further comprising:

providing answer candidates to the neural model; and

learning neuro-symbolic rules for the answer candidates.

4. The computer-implemented method of claim 1 , further comprising:

inputting pre-defined symbolic rules into the neural model; and

combining the pre-defined symbolic rules with the neuro-symbolic rule.

5. The computer-implemented method of claim 1 , wherein the neural model is a long short-term memory (LSTM) recurrent neural network (RNN).

6. The computer-implemented method of claim 1 , wherein the neural model includes entity-aware encoding.

7. A computer-implemented method for applying neuro-symbolic rules, the computer-implemented method comprising:

inputting texts comprising passages pertaining to a domain into a neural model;

inputting a query relating to the domain, wherein the query includes a head entity and a target relationship into the neural model;

extracting entities from the texts, wherein the entities relate to the domain;

generating an entity graph with nodes connected by edges, wherein the nodes represent the entities extracted from the text and the edges represent passages within the texts with a co-occurrence of the entities connected together by the edges;

determining a textual relation for each of the edges in the entity graph including a probability relating to the textual relation;

extracting potential reasoning paths for candidate answers in the entity graph by applying the neuro-symbolic rules learned by the neural model, wherein applying the neuro-symbolic rules increases a possibility that the entity answer is correct;

scoring the potential reasoning paths based on a structured prediction; and

providing an entity answer based on scoring the potential reasoning paths.

8. The computer-implemented method of claim 7 , further comprising:

weighing the symbolic relation for each of the edges within the potential reasoning paths.

9. The computer-implemented method of claim 7 , further comprising:

converting a reasoning path with a highest score into a neuro-symbolic rule; and

combining the neuro-symbolic rule with the neuro-symbolic rules already learned by the neural model.

10. The computer-implemented method of claim 7 , wherein the entities are pre-defined entities relating to the texts.

11. The computer-implemented method of claim 7 , wherein scoring the potential reasoning paths is performed by a beam search through the entity graph to the candidate answer.

12. The computer-implemented method of claim 7 , further comprising:

optimizing the potential reasoning paths based on the structured prediction, wherein a potential reasoning path that has a neuro-symbolic rule indicating a likely correct answer is assigned a higher score as compared to scores of other potential reasoning paths, and

a potential reasoning path that has a neuro-symbolic rule indicating a likely incorrect answer is assigned a lower score as compared to scores of other potential reasoning paths.

13. The computer-implemented method of claim 7 , wherein the query includes multiple queries inputted into the neural model.

14. A neuro-symbolic rules system for learning neuro-symbolic rules using multi-hop reasoning, the neuro-symbolic rules system comprising:

one or more computer-readable storage media storing program instructions and one or more processors which, in response to executing the program instructions, are configured to:

extract entities from texts for a domain, wherein the entities are nouns relating to the domain;

generate an entity graph including nodes and edges, wherein the nodes correspond to the entities and the edges correspond to passages within the texts with a co-occurrence of the entities connected together by the edges;

determine, using a neural model, reasoning paths between a head entity and an answer entity that result in a target relationship, wherein the neural model scores the reasoning paths based on probabilities calculated by the neural model for each edge along the reasoning path;

learn, by the neural model, a neuro-symbolic rule; and

apply the neuro-symbolic rule to the reasoning paths, for increasing a possibility increases a likelihood of selecting a correct entity.

15. The neuro-symbolic rules system of claim 14 , wherein the neural model scores the reasoning paths based on inputted ground truth answers during a training process.

16. The neuro-symbolic rules system of claim 14 , wherein the neural model is a long short-term memory recurrent neural network.

17. The neuro-symbolic rules system of claim 14 , wherein the neural model scores the reasoning paths based on a beam search and using structured prediction.

18. The neuro-symbolic rules system of claim 14 , wherein the neuro-symbolic rule is learned by converting the edges along the reasoning paths into symbolic rules and combining those rules into the neuro-symbolic rule.

19. The neuro-symbolic rules system of claim 18 , wherein the neuro-symbolic rule is added to other neuro-symbolic rules learned by the neural model.

20. The neuro-symbolic rules system of claim 18 , wherein the neural model is configured to determine weights for the symbolic rules.