Knowledge graph entity reconciler
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.
1. A computer-implemented method for expanding and reinforcing a knowledge graph, the method comprising:
receiving source data comprising a source data topic entity and a plurality of source data edges, wherein each edge of the plurality of source data edges comprises a relationship-entity pair associated with the source data topic entity;
generating a source data graph from the source data topic entity and the plurality of source data edges;
identifying a candidate topic entity in a knowledge graph having a plurality of knowledge graph edges, wherein each edge of the plurality of knowledge graph edges comprises a knowledge graph relationship-entity pair associated with the knowledge graph candidate topic entity;
determining a similarity between the source data graph and a sub-graph of the knowledge graph having the candidate topic entity, the sub-graph having a plurality of knowledge graph edges associated with the candidate topic entity, wherein determining the similarity comprises determining a similarity between the plurality of source data edges and the plurality of knowledge sub-graph edges, and wherein determining the similarity between the plurality of source data graph edges and the plurality of knowledge sub-graph edges comprises determining a ratio of an intersection of the plurality of source data graph edges with the plurality of knowledge sub-graph edges to a union of the plurality of source data edges with the plurality of knowledge sub-graph edges;
merging the source data graph into the knowledge graph, in response to determining that the similarity is greater than a threshold value.
2. The method of claim 1 , further comprising:
generating a sub-graph of the knowledge graph using the knowledge graph topic candidate entity and the plurality of knowledge graph edges.
3. The method of claim 1 , wherein the relationship in each of the plurality of source data edges exists in the knowledge graph.
4. The method of claim 1 , wherein the entity in each of the plurality of source data edges exists in the knowledge graph.
5. The method of claim 1 , wherein determining the similarity further comprises:
determining a similarity between the source data topic entity and the knowledge graph candidate topic entity.
6. The method of claim 5 , wherein determining the similarity further comprises:
determining a similarity between a context of the source data topic entity and a context of the knowledge graph candidate topic entity,
wherein a context comprises a plurality of words and word frequencies for an entity.
7. The method of claim 5 , wherein determining the similarity between the source data topic entity and the knowledge graph candidate topic entity comprises translating a language of the source data topic entity.
8. A non-transitory computer-medium having stored thereon executable instructions that, when executed by at least one hardware processor, perform operations for automating comprising:
receiving source data comprising a source data topic entity and a plurality of source data edges, wherein each edge of the plurality of source data edges comprises a relationship-entity pair associated with the source data topic entity;
generating a source data graph from the source data topic entity and the plurality of source data edges;
identifying a candidate topic entity in a knowledge graph having a plurality of knowledge graph edges, wherein each edge of the plurality of knowledge graph edges comprises a knowledge graph relationship-entity pair associated with the knowledge graph candidate topic entity;
determining a similarity between the source data graph and a sub-graph of the knowledge graph having the candidate topic entity, the sub-graph having a plurality of knowledge graph edges associated with the candidate topic entity, wherein determining the similarity comprises determining a similarity between the plurality of source data edges and the plurality of knowledge sub-graph edges, and wherein determining the similarity between the plurality of source data graph edges and the plurality of knowledge sub-graph edges comprises determining a ratio of an intersection of the plurality of source data graph edges with the plurality of knowledge sub-graph edges to a union of the plurality of source data edges with the plurality of knowledge sub-graph edges;
merging the source data graph into the knowledge graph, in response to determining that the similarity is greater than a threshold value.
9. The medium of claim 8 , further comprising:
generating a sub-graph of the knowledge graph using the knowledge graph topic candidate entity and the plurality of knowledge graph edges.
10. The medium of claim 8 , wherein the relationship in each of the plurality of source data edges exists in the knowledge graph.
11. The medium of claim 8 , wherein the entity in each of the plurality of source data edges exists in the knowledge graph.
12. The medium of claim 8 , wherein determining the similarity further comprises:
determining a similarity between the source data topic entity and the knowledge graph candidate topic entity.
13. The medium of claim 12 , wherein determining the similarity further comprises:
determining a similarity between a context of the source data topic entity and a context of the knowledge graph candidate topic entity,
wherein a context comprises a plurality of words and word frequencies for an entity.
14. The medium of claim 12 , wherein determining the similarity between the source data topic entity and the knowledge graph candidate topic entity comprises translating a language of the source data topic entity.
15. A system comprising at least one hardware processor coupled to a memory, the memory having stored thereon executable instructions that, when executed by the at least one hardware processor, perform operations for automating comprising:
receiving source data comprising a source data topic entity and a plurality of source data edges, wherein each edge of the plurality of source data edges comprises a relationship-entity pair associated with the source data topic entity;
generating a source data graph from the source data topic entity and the plurality of source data edges;
identifying a candidate topic entity in a knowledge graph having a plurality of knowledge graph edges, wherein each edge of the plurality of knowledge graph edges comprises a knowledge graph relationship-entity pair associated with the knowledge graph candidate topic entity;
determining a similarity between the source data graph and a sub-graph of the knowledge graph having the candidate topic entity, the sub-graph having a plurality of knowledge graph edges associated with the candidate topic entity, wherein determining the similarity comprises determining a similarity between the plurality of source data edges and the plurality of knowledge sub-graph edges, and wherein determining the similarity between the plurality of source data graph edges and the plurality of knowledge sub-graph edges comprises determining a ratio of an intersection of the plurality of source data graph edges with the plurality of knowledge sub-graph edges to a union of the plurality of source data edges with the plurality of knowledge sub-graph edges;
merging the source data graph into the knowledge graph, in response to determining that the similarity is greater than a threshold value.
16. The system of claim 15 , further comprising:
generating a sub-graph of the knowledge graph using the knowledge graph topic candidate entity and the plurality of knowledge graph edges.
17. The system of claim 15 , wherein the relationship in each of the plurality of source data edges exists in the knowledge graph.
18. The system of claim 15 , wherein the entity in each of the plurality of source data edges exists in the knowledge graph.
19. The system of claim 15 , wherein determining the similarity further comprises:
determining a similarity between the source data topic entity and the knowledge graph candidate topic entity.
20. The system of claim 19 , wherein determining the similarity further comprises:
determining a similarity between a context of the source data topic entity and a context of the knowledge graph candidate topic entity,
wherein a context comprises a plurality of words and word frequencies for an entity.
21. The system of claim 19 , wherein determining the similarity between the source data topic entity and the knowledge graph candidate topic entity comprises translating a language of the source data topic entity.