Mechanisms are provided in which a first knowledge graph, comprising nodes representing entities and edges between nodes indicative of a relationship between the entities, is received. The mechanisms identify a candidate missing edge connecting a node of the first knowledge graph to another node not present in the first knowledge graph and evaluate the candidate missing edge to determine if the candidate missing edge should be added to the first knowledge graph. The mechanisms expand the first knowledge graph to include the candidate missing edge connecting the node to a newly added node that is newly added to the first knowledge graph, to thereby generate an expanded knowledge graph, in response to the evaluation indicating that the candidate missing edge should be added to the first knowledge graph. The mechanisms then perform an operation on the expanded knowledge graph to generate a knowledge output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions which are executed by the at least one processor to configure the at least one processor to perform the method, comprising: receiving, by the data processing system, a first knowledge graph comprising nodes representing entities and edges between nodes, wherein each edge represents a relationship between the nodes directly connected to each other by the edge; identifying, by the data processing system, at least one candidate missing edge connecting at least one node of the first knowledge graph to at least one other node not present in the first knowledge graph; evaluating, by the data processing system, the at least one candidate missing edge to determine if the at least one candidate missing edge should be added to the first knowledge graph; expanding, by the data processing system, the first knowledge graph to include the at least one candidate missing edge connecting the at least one node to at least one newly added node that is newly added to the first knowledge graph, to thereby generate an expanded knowledge graph, in response to the evaluation indicating that the at least one candidate missing edge should be added to the first knowledge graph; and performing, by the data processing system, an operation on the expanded knowledge graph to generate a knowledge output. 2. The method of claim 1, wherein evaluating the at least one candidate missing edge further comprises: analyzing partial entity or relationship matches associated with first entities or relationships of the first knowledge graph and second entities or relationships in other knowledge graphs having matching types to the first entities or relationships of the first knowledge graph. 3. The method of claim 2, wherein analyzing partial entity or relationship matches comprises: identifying a first pattern of entity types and relationship types between the entities of a subset of the knowledge graph; searching other knowledge graphs for a second pattern of entity types and relationship types that match the first pattern; and identifying at least one other entity or other relationship associated with the matching second pattern that is not part of the first pattern. 4. The method of claim 3, wherein evaluating the at least one candidate missing edge comprises searching a knowledge base for instances of data corresponding to the at least one other entity or other relationship and which is associated with an entity or relationship present in the first pattern. 5. The method of claim 3, further comprising: generating at least one question in natural language based on the identified at least one other entity or other relationship associated with the matching second pattern; and outputting the at least one question to one of a human user via a computing device or an automated cognitive system configured to generate an answer to the at least one question. 6. The method of claim 5, further comprising: receiving an answer to the at least one question from one of the human user via the computing device or the automated cognitive system, wherein expanding the knowledge graph comprises expanding the knowledge graph to include at least one additional node or at least one edge corresponding to the answer to the at least one question. 7. The method of claim 1, further comprising: receiving an input question; and generating a hypothesis for answering the input question based on a predefined tenet data structure, wherein the predefined tenet data structure specifies criteria for proving or disproving the hypothesis, and wherein identifying at least one candidate missing edge comprises: identifying a first pattern, of entities and relationships between entities, corresponding to the tenet data structure; identifying a sub-graph in the knowledge graph matching the first pattern; identifying entity types and relationship types in the sub-graph; and searching other knowledge graphs for a corresponding pattern of entity types and relationship types at least partially matching the sub-graph. 8. The method of claim 6, wherein the at least one candidate missing edge is an edge, in another knowledge graph, that is connected to a corresponding pattern of entity types and relationship types that at least partially matches the sub-graph, and which is not present in the first knowledge graph. 9. The method of claim 6, wherein searching other knowledge graphs for a corresponding pattern comprises utilizing at least one of explicit matching techniques or implicit matching techniques. 10. The method of claim 1, further comprising: generating a graphical output of the extended knowledge graph, wherein the newly added candidate edge is provided in a visually distinct manner from the first knowledge graph in the graphical output. 11. The method of claim 1, wherein the first knowledge graph represents data collected as part of a first criminal investigation, and wherein the other knowledge graphs represent data collected as part of other criminal investigations. 12. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive a first knowledge graph comprising nodes representing entities and edges between nodes, wherein each edge represents a relationship between the nodes directly connected to each other by the edge; identify at least one candidate missing edge connecting at least one node of the first knowledge graph to at least one other node not present in the first knowledge graph; evaluate the at least one candidate missing edge to determine if the at least one candidate missing edge should be added to the first knowledge graph; expand the first knowledge graph to include the at least one candidate missing edge connecting the at least one node to at least one newly added node that is newly added to the first knowledge graph, to thereby generate an expanded knowledge graph, in response to the evaluation indicating that the at least one candidate missing edge should be added to the first knowledge graph; and perform an operation on the expanded knowledge graph to generate a knowledge output. 13. The computer program product of claim 12, wherein the computer readable program further causes the computing device to evaluate the at least one candidate missing edge further at least by: analyzing partial entity or relationship matches associated with first entities or relationships of the first knowledge graph and second entities or relationships in other knowledge graphs having matching types to the first entities or relationships of the first knowledge graph. 14. The computer program product of claim 13, wherein analyzing partial entity or relationship matches comprises: identifying a first pattern of entity types and relationship types between the entities of a subset of the knowledge graph; searching other knowledge graphs for a second pattern of entity types and relationship types that match the first pattern; and identifying at least one other entity or other relationship associated with the matching second pattern that is not part of the first pattern. 15. The computer program product of claim 14, wherein the computer readable program further causes the computing device to evaluate the at least one candidate missing edge at least by searching a knowledge base for instances of data corresponding to the at least one other entity or other relationship and which is associated with an entity or relationship present in the first pattern. 16. The computer program product of claim 14, wherein the computer readable program further causes the computing device to: generate at least one question in natural language based on the identified at least one other entity or other relationship associated with the matching second pattern; and output the at least one question to one of a human user via a computing device or an automated cognitive system configured to generate an answer to the at least one question. 17. The computer program product of claim 16, wherein the computer readable program further causes the computing device to: receive an answer to the at least one question from one of the human user via the computing device or the automated cognitive system, wherein expanding the knowledge graph comprises expanding the knowledge graph to include at least one additional node or at least one edge corresponding to the answer to the at least one question. 18. The computer program product of claim 12, wherein the computer readable program further causes the computing device to: receive an input question; and generate a hypothesis for answering the input question based on a predefined tenet data structure, wherein the predefined tenet data structure specifies criteria for proving or disproving the hypothesis, and wherein identifying at least one candidate missing edge comprises: identifying a first pattern, of entities and relationships between entities, corresponding to the tenet data structure; identifying a sub-graph in the knowledge graph matching the first pattern; identifying entity types and relationship types in the sub-graph; and searching other knowledge graphs for a corresponding pattern of entity types and relationship types at least partially matching the sub-graph. 19. The computer program product of claim 17, wherein the at least one candidate missing edge is an edge, in another knowledge graph, that is connected to a corresponding pattern of entity types and relationship types that at least partially matches the sub-graph, and which is not present in the first knowledge graph. 20. The computer program product of claim 17, wherein the computer readable program further causes the computing device to search other knowledge graphs for a corresponding pattern at least by utilizing at least one of explicit matching techniques or implicit matching techniques. 21. The computer program product of claim 12, wherein the first knowledge graph represents data collected as part of a first criminal investigation, and wherein the other knowledge graphs represent data collected as part of other criminal investigations. 22. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: receive a first knowledge graph comprising nodes representing entities and edges between nodes, wherein each edge represents a relationship between the nodes directly connected to each other by the edge; identify at least one candidate missing edge connecting at least one node of the first knowledge graph to at least one other node not present in the first knowledge graph; evaluate the at least one candidate missing edge to determine if the at least one candidate missing edge should be added to the first knowledge graph; expand the first knowledge graph to include the at least one candidate missing edge connecting the at least one node to at least one newly added node that is newly added to the first knowledge graph, to thereby generate an expanded knowledge graph, in response to the evaluation indicating that the at least one candidate missing edge should be added to the first knowledge graph; and perform an operation on the expanded knowledge graph to generate a knowledge output.