A method may include selecting a particular entity from a knowledge graph when a level of connectivity between entities in the knowledge graph that are neighbors to the particular entity is above a certain level and determining whether the particular entity is in a character string.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-15. (canceled) 16. A method, comprising: selecting a particular entity from a knowledge graph when a level of connectivity between entities in the knowledge graph that are neighbors to the particular entity is above a certain level; and determining whether the particular entity is in a character string. 17. The method of claim 16, comprising, when the particular entity is determined to be in the character string, taking an ambiguous entity in the character string to mean a certain entity in the knowledge graph that is a potential meaning of the ambiguous entity and that is connected to the particular entity, in response to determining that the certain entity is more similar to the particular entity than an other entity in the knowledge graph that is a potential meaning of the ambiguous entity and that is connected to the particular entity. 18. The method of claim 17, wherein the certain entity that is more similar to the particular entity than the other entity is connected to the particular entity by a shorter path than is the other entity. 19. The method of claim 16, wherein the level of connectivity of entities in the knowledge graph that are neighbors to the particular entity is determined by determining a clustering coefficient for the particular entity. 20. The method of claim 16, comprising determining a location of the particular entity within the character string in response to determining that the particular entity is in the character string. 21. The method of claim 16, wherein each of the entities in the knowledge graph that are neighbors to the particular entity are connected to the particular entity by a single link. 22. The method of claim 16, further comprising: determining a first path length of a first path within the knowledge graph that connects the particular entity to a first entity in the knowledge graph, wherein the particular entity is determined to be in the character string; determining a second path length of a second path within the knowledge graph that connects the particular entity to a second entity in the knowledge graph; determining a third path length of a third path within the knowledge graph that connects a third entity in the knowledge graph to the first entity, wherein the third entity is determined to be in the character string; determining a fourth path length of a fourth path within the knowledge graph that connects the third entity in the knowledge graph to the second entity; combining the first and the third path lengths to determine a combined path length value for the first entity; combining the second and fourth path lengths to determine a combined path length value for the second entity; and disambiguating an ambiguous entity in the character string by taking the ambiguous entity to mean the first entity, in response to determining that combined path length value for the first entity is less than the combined path length value for the second entity. 23. The method of claim 22, wherein the knowledge graph is an as-received, unmodified knowledge graph. 24. A non-transitory machine-readable storage medium encoded with instructions executable by a processor, the machine-readable storage medium comprising: instructions to rank entities in a knowledge graph according to a ranking measure of the entities in the knowledge graph, wherein a ranking measure of an entity in the knowledge graph is proportional to a level of interconnectivity between entities that are neighbors to that entity; and instructions to compare ranked entities that have a ranking measure above a certain value to text. 25. The non-transitory machine-readable storage medium of claim 24, comprising instructions to determine path lengths of paths in the knowledge graph that respectively connect respective entities in the knowledge graph that are potential meanings of an ambiguous entity in the text to a ranked entity determined to be in the text and to take the ambiguous entity to mean the respective entity that is connected to the ranked entity determined to be in the text by a path having a shortest path length of the determined path lengths. 26. The non-transitory machine-readable storage medium of claim 24, comprising: instructions to determine a first combined path length value for first paths that connect first and second ranked entities determined to be in the text to a first certain entity in the knowledge graph that is a potential meaning of an ambiguous entity in the text; instructions to determine a second combined path length value for second paths that connect the first and second ranked entities to a second certain entity in the knowledge graph that is a potential meaning of the ambiguous entity in the text; and instructions to take the ambiguous entity in the text to mean the first certain entity in response to determining that the first combined path length value is less than the second combined path length value. 27. A computer, comprising: a processor; memory coupled to the processor and encoded with instructions to allow the processor to: determine clustering coefficients for respective ones of a plurality of entities in a knowledge graph; rank the respective ones of the plurality of entities based on their determined clustering coefficients; and determine which of the ranked respective ones of the plurality of entities are in a string of text. 28. The computer of claim 27, wherein the instructions allow the processor to determine weights of respective paths that connect respective entities in the knowledge graph that are potential meanings of an ambiguous entity in the string of text to a particular ranked respective one of the plurality of entities that is in the string of text, wherein the weights are indicative of similarities of the respective entities in the knowledge graph to the particular ranked respective one of the plurality of entities. 29. The computer of claim 28, wherein the particular ranked respective one of the plurality of entities is a first entity, and wherein the instructions allow the processor to take the ambiguous entity to mean the respective entity that is connected to the first entity by a respective path having a weight indicative of a similarity of that respective entity to the first entity that is greater than the similarities of other of the respective entities to the first entity indicated by the weights of the other respective paths. 30. The computer of claim 27, wherein instructions to allow the processor to determine which of the ranked respective ones of the plurality of entities are in the string allow the processor to determine which of the ranked respective ones of the plurality of entities that have clustering coefficients above a certain value are in the string of text.