Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of detecting an association between semantic entities, comprising: identifying semantic entities and associated semantic collections present in one or more knowledge bases, wherein the semantic entities include one or more of single words or multi-word phrases, and the semantic entities of a semantic collection share an entity type; determining a time period for analysis; dividing the time period into one or more time slices; generating, for each time slice, a set of word embeddings for the identified semantic entities based on one or more corpora; determining, for each time slice, a first semantic association strength between a first semantic entity input and a second semantic entity input; determining, for each time slice, a second semantic association strength between the first semantic entity input and a plurality of semantic entities in a semantic collection that is associated with the second semantic entity; and providing an output based on the first and second semantic association strengths for the one or more time slices. 2. The method of claim 1, wherein the one or more corpora comprise structured data and unstructured data. 3. The method of claim 1, wherein identifying semantic entities includes one or more of: (1) automatic methods of identifying one or more single words or multi-word phrases as semantic entities belonging to semantic collections and (2) selecting one or more single words or multi-word phrases forcibly from the one or more knowledge bases. 4. The method of claim 3, wherein the one or more single words or multi-word phrases are selected forcibly from information compiled from a structured database. 5. The method of claim 1, wherein identifying semantic entities is performed on all text in the one or more knowledge bases for the time period. 6. The method of claim 1, wherein the word embeddings are generated using one or more of Word2vec, AdaGram, fastText, and Doc2vec. 7. The method of claim 1, wherein the word embeddings are generated for each time slice independently of word embeddings generated for other time slices. 8. The method of claim 1, wherein the word embeddings for a time slice are generated by leveraging word embeddings from a previous time slice. 9. The method of claim 1, wherein the plurality of semantic entities associated with the semantic collection that is associated with the second semantic entity does not include the second semantic entity. 10. The method of claim 1, wherein the second semantic association strength is a mean, median, or a percentile of a set of semantic association strengths between the first semantic entity input and the plurality of semantic entities associated with a semantic collection that is associated with the second semantic entity. 11. The method of claim 1, further comprising: detecting an increase in the first semantic association strength of a first time slice relative to the first semantic association strength of a second, subsequent time slice; and determining whether the increase in the first semantic association strength is statistically significant relative to the corresponding second semantic association. 12. The method of claim 11, wherein the statistical significance of the increase is determined based on a p-value as a measure of statistical significance of the first semantic association strength relative to the corresponding second semantic association. 13. The method of claim 1, further comprising: selecting the first entity input and the second entity input based on a level of co-occurrence between the first entity and the second entity in the one or more knowledge bases. 14. The method of claim 13, wherein the level of co-occurrence between the first entity and the second entity is zero. 15. The method of claim 1, further comprising: receiving the first entity input and the second entity input from a user. 16. The method of claim 1, further comprising: determining, for each time slice, a count of documents present in the one or more corpora containing the first entity and the second entity; and determining a time difference between (1) a first date associated with an increase in the first semantic association strength for a first time slice relative to the first semantic association strength for a second, subsequent time slice and (2) a second date associated with an increase in a count of documents containing the first entity and the second entity for a third time slice relative to a count of documents containing the first entity and the second entity for a fourth time slice. 17. The method of claim 16, further comprising: detecting the increase in the count of documents containing the first entity and the second entity based on a slope of a curve in a fixed axis, wherein the curve is based on the time period on an x-axis of the curve and the count of documents on a y-axis of the curve. 18. The method of claim 16, further comprising: detecting the second increase in the count of documents containing the first entity and the second entity based on a document count threshold. 19. The method of claim 1, wherein each of the first entity and the second entity is one or more of the following entity types: bio-molecules, bio-entities, diseases, adverse events, phenotypes, companies, institutions, universities, hospitals, people, drugs, medical instruments, or medical procedures. 20. The method of claim 1, wherein the output enables a user device to display a graph line that is created by plotting each of the first semantic association strengths for each of the time slices over the time period. 21. The method of claim 1, wherein the output enables a user device to display a graph line that is created by plotting each of mean second semantic association strengths for each of the time slices over the time period. 22. The method of claim 1, wherein the output enables a user device to display a graph line that is created by plotting a count of documents present in the one or more corpora containing the first entity and the second entity for each of the time slices over the time period. 23. A system for detecting an association between semantic entities, comprising: a memory that stores a module; and a processor configured to run the module stored in the memory that is configured to cause the processor to: identify semantic entities and associated semantic collections present in one or more knowledge bases, wherein the semantic entities include one or more of single words or multi-word phrases, and the semantic entities of a semantic collection share an entity type; determine a time period for analysis; divide the time period into one or more time slices; generate, for each time slice, a set of word embeddings for the identified semantic entities based on one or more corpora; determine, for each time slice, a first semantic association strength between a first semantic entity input and a second semantic entity input; determine, for each time slice, a second semantic association strength between the first semantic entity input and a plurality of semantic entities in a semantic collection that is associated with the second semantic entity; and provide an output based on the first and second semantic association strengths for the one or more time slices. 24. The system of claim 23, wherein identifying semantic entities includes one or more of: (1) automatic methods of identifying one or more single words or multi-word phrases as semantic entities belonging to semantic collections and (2) selecting one or more single words or multi-word phrases forcibly from the one or more knowledge bases. 25. The system of claim 23, wherein the second semantic association strength is a mean, a median, or a percentile of a set of semantic association strengths between the first semantic entity input and the plurality of semantic entities associated with a semantic collection that is associated with the second semantic entity. 26. The system of claim 23, wherein the module stored in the memory is further configured to cause the processor to: detect an increase in the first semantic association strength of a first time slice relative to the first semantic association strength of a second, subsequent time slice; and determine whether the increase in the first semantic association strength is statistically significant relative to the corresponding second semantic association. 27. The system of claim 23, wherein the statistical significance of the increase is determined based on a p-value as a measure of statistical significance of the first semantic association strength relative to the corresponding second semantic association. 28. The system of claim 23, wherein the module stored in the memory is further configured to cause the processor to: select the first entity input and the second entity input based on a level of co-occurrence between the first entity and the second entity in the one or more knowledge bases. 29. The system of claim 23, wherein the module stored in the memory is further configured to cause the processor to: determine, for each time slice, a count of documents present in the one or more corpora containing the first entity and the second entity; and determine a time difference between (1) a first date associated with an increase in the first semantic association strength for a first time slice relative to the first semantic association strength for a second, subsequent time slice and (2) a second date associated with an increase in a count of documents containing the first entity and the second entity for a third time slice relative to a count of documents containing the first entity and the second entity for a fourth time slice. 30. The system of claim 29, wherein the module stored in the memory is further configured to cause the processor to: detect the increase in the count of documents containing the first entity and the second entity based on a slope of a curve in a fixed axis, wherein the curve is based on the time period on an x-axis of the curve and the count of documents on a y-axis of the curve. 31-126. (canceled)