This disclosure relates to predicting and managing supply chain network risks. In one embodiment, a processor-implemented method obtains identifiers for supply chain contributors and parameters; and a query. The method performs a natural language processing algorithm on the query to extract text components, which it analyzes to identify supply chain component clusters and risk identifiers. It also includes executing a machine learning technique for learning of the risk identifiers and generating co-occurrence rules between the risk identifiers, as well as associated rule support and rule confidence parameters. It further includes sorting the co-occurrence rules to generate a prioritized rules list, and generating a risk prediction model for the supply chain using the prioritized rules list, using a classifier algorithm. The method further includes training the risk prediction model using a machine learning techniques for incremental learning, and generating a supply chain element modification using the trained risk prediction model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning prediction system, comprising: a hardware processor, and a memory storing instructions executable by the hardware processor for: obtaining, via the hardware processor, for a supply chain, identifiers for one or more supply chain contributors, one or more supply chain parameters including a supply chain type and an identification of a supply chain process flow; and one or more user parameters including a user query; performing, via the hardware processor, a natural language processing algorithm on the user query to extract one or more text components including: one or more keywords, one or more names, one or more nouns, and one or more named entities; analyzing, via the hardware processor, the extracted text components to identify one or more supply chain component clusters; identifying, via the hardware processor, one or more risk identifiers by comparing the extracted text components to risk identifiers included in the one or more supply chain component clusters; executing, via the hardware processor, a machine learning technique for learning of the one or more identified risk identifiers; generating, via the hardware processor, one or more co-occurrence rules between the identified risk identifiers, as well as associated rule support and rule confidence parameters; sorting, via the hardware processor, the one or more co-occurrence rules to generate a prioritized rules list; generating, via the hardware processor, a risk prediction model for the supply chain using the prioritized rules list, using a classifier algorithm; training, via the hardware processor, the risk prediction model using a machine learning techniques for incremental learning; and generating, via the hardware processor, a supply chain element modification using the trained risk prediction model. 2. The system of claim 1, the memory further storing instructions executable by the hardware processor for: deleting, via the hardware processor, one or more words from the user query using a text analyzing algorithm. 3. The system of claim 2, wherein the text analyzing algorithm is further utilized to identify at least one co-reference relationship between two or more sentences included in the user query. 4. The system of claim 1, wherein the one or more co-occurrence rules are generated based on a apriori item-set generation algorithm. 5. The system of claim 1, the memory further storing instructions executable by the hardware processor for: storing, via the hardware processor, the one or more identified risk identifiers in a structured database. 6. The system of claim 1, wherein generating, via the hardware processor, the risk prediction model uses a probabilistic classifier algorithm. 7. The system of claim 1, the memory further storing instructions executable by the hardware processor for: upon detecting presence of a predicted risk in the supply chain, providing an alert for the one or more supply chain contributors. 8. A machine learning prediction method, comprising: obtaining, via a hardware processor, for a supply chain, identifiers for one or more supply chain contributors, one or more supply chain parameters including a supply chain type and an identification of a supply chain process flow; and one or more user parameters including a user query; performing, via the hardware processor, a natural language processing algorithm on the user query to extract one or more text components including: one or more keywords, one or more names, one or more nouns, and one or more named entities; analyzing, via the hardware processor, the extracted text components to identify one or more supply chain component clusters; identifying, via the hardware processor, one or more risk identifiers by comparing the extracted text components to risk identifiers included in the one or more supply chain component clusters; executing, via the hardware processor, a machine learning technique for learning of the one or more identified risk identifiers; generating, via the hardware processor, one or more co-occurrence rules between the identified risk identifiers, as well as associated rule support and rule confidence parameters; sorting, via the hardware processor, the one or more co-occurrence rules to generate a prioritized rules list; generating, via the hardware processor, a risk prediction model for the supply chain using the prioritized rules list, using a classifier algorithm; training, via the hardware processor, the risk prediction model using a machine learning techniques for incremental learning; and generating, via the hardware processor, a supply chain element modification using the trained risk prediction model. 9. The method of claim 8, further comprising: deleting, via the hardware processor, one or more words from the user query using a text analyzing algorithm. 10. The method of claim 9, wherein the text analyzing algorithm is further utilized to identify at least one co-reference relationship between two or more sentences included in the user query. 11. The method of claim 8, wherein the one or more co-occurrence rules are generated based on a apriori item-set generation algorithm. 12. The method of claim 8, further comprising: storing, via the hardware processor, the one or more identified risk identifiers in a structured database. 13. The method of claim 8, wherein generating, via the hardware processor, the risk prediction model uses a probabilistic classifier algorithm. 14. The method of claim 8, further comprising: upon detecting presence of a predicted risk in the supply chain, providing an alert for the one or more supply chain contributors. 15. A non-transitory computer-readable medium storing instructions executable by a hardware processor for: obtaining, via the hardware processor, for a supply chain, identifiers for one or more supply chain contributors, one or more supply chain parameters including a supply chain type and an identification of a supply chain process flow; and one or more user parameters including a user query; performing, via the hardware processor, a natural language processing algorithm on the user query to extract one or more text components including: one or more keywords, one or more names, one or more nouns, and one or more named entities; analyzing, via the hardware processor, the extracted text components to identify one or more supply chain component clusters; identifying, via the hardware processor, one or more risk identifiers by comparing the extracted text components to risk identifiers included in the one or more supply chain component clusters; executing, via the hardware processor, a machine learning technique for learning of the one or more identified risk identifiers; generating, via the hardware processor, one or more co-occurrence rules between the identified risk identifiers, as well as associated rule support and rule confidence parameters; sorting, via the hardware processor, the one or more co-occurrence rules to generate a prioritized rules list; generating, via the hardware processor, a risk prediction model for the supply chain using the prioritized rules list, using a classifier algorithm; training, via the hardware processor, the risk prediction model using a machine learning techniques for incremental learning; and generating, via the hardware processor, a supply chain element modification using the trained risk prediction model. 16. The medium of claim 15, further storing instructions executable by the hardware processor for: deleting, via the hardware processor, one or more words from the user query using a text analyzing algorithm. 17. The medium of claim 16, wherein the text analyzing algorithm is further utilized to identify at least one co-reference relationship between two or more sentences included in the user query. 18. The medium of claim 15, wherein the one or more co-occurrence rules are generated based on a apriori item-set generation algorithm. 19. The medium of claim 15, further storing instructions executable by the hardware processor for: storing, via the hardware processor, the one or more identified risk identifiers in a structured database. 20. The medium of claim 15, wherein generating, via the hardware processor, the risk prediction model uses a probabilistic classifier algorithm.