METHODS AND SYSTEMS FOR AUTOMATED GENERATION OF PERSONALIZED MESSAGES
A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.
1 . A computer-implemented method comprising:
determining, by a processing system, a recipient list based on a recipient profile and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships;
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data,
extracting, by the processing system, a new entity from digital documents; creating, by the processing system, a new relationship relating to the new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node; and
updating, by the processing system, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship, wherein the new edge connects the new node to the existing node.
2 . The method of claim 1 , wherein the recipient list identifies individuals that are more likely to result in a successful outcome given the recipient profile and information represented in the knowledge data structure.
3 . The method of claim 1 , wherein the knowledge data structure stores a plurality of nodes and a plurality of edges that connect respective nodes from the plurality of nodes, wherein each node represents a respective entity of a respective entity type and each edge corresponds to a respective relationship of a respective relationship type.
4 . The method of claim 1 , wherein the knowledge data structure stores event data relating to a plurality of detected events relating to the entities and the relationships represented in the knowledge data structure; and the method further comprising:
extracting, by the processing system, a new event corresponding to the new entity, wherein extracting the new relationship is further based on the new event.
5 . The method of claim 4 , wherein the new event is extracted using an event classification model that is trained to identify events indicated in documents.
6 . The method of claim 1 , wherein the knowledge data structure is a knowledge graph.
7 . The method of claim 1 , wherein the new entity is extracted using an entity classification model that is trained to identify entities indicated in the digital documents.
8 . The method of claim 1 , wherein the recipient profile indicates attributes of an ideal recipient of the message to be sent on behalf of a user.
9 . The method of claim 8 , wherein the determining the recipient list comprises filtering entities from the plurality of entities represented in the knowledge data structure based on the attributes.
10 . The method of claim 8 , wherein the determining the recipient list comprises, for each individual of a subset of the individuals represented in the knowledge data structure, determining a lead score of each individual based on the attributes of the recipient profile using a machine-learned scoring model.
11 . The method of claim 10 , wherein the lead score of each individual is further based on an event related to an organization of each individual.
12 . The method of claim 1 , wherein the generating the personalized message for the individual comprises:
generating directed content based on retrieved entity data retrieved from the knowledge data structure, wherein the directed content comprises a phrase with information corresponding to the retrieved entity data, wherein the personalized message is generated based upon the directed content and a message template.
13 . The method of claim 12 , wherein the directed content is generated based on a machine-learned generative model that is trained to generate text given entity data of an entity and a particular objective of the personalized message, and wherein message data indicates the particular objective of the personalized message.
14 . The method of claim 1 , wherein the knowledge data structure stores event data relating to a plurality of detected events relating to the entities and the relationships represented in the knowledge data structure, wherein the personalized message is generated using directed content and a message template, and wherein the directed content is derived from the event data.
15 . The method of claim 14 , wherein the directed content is generated based on a machine-learned generative model that is trained to generate text given a particular objective of the personalized message and the event data relating to a known event that occurred with respect to an entity and the particular objective of the personalized message, and wherein message data indicates the particular objective of the personalized message.
16 . The method of claim 1 , wherein the processing system uses natural language processing to extract the new entity from the one or more digital documents obtained from a crawler that crawls a data source.
17 . A system comprising:
memory comprising instructions; and
a processor configured to execute the instructions to perform operations comprising:
determining, by a processing system, a recipient list based on a recipient profile and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships;
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data,
extracting, by the processing system, a new entity from digital documents; creating, by the processing system, a new relationship relating to the new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node; and
updating, by the processing system, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship, wherein the new edge connects the new node to the existing node.
18 . The system of claim 17 , wherein the knowledge data structure stores event data relating to the plurality of entities, and wherein the event data is used to generate the personalized message.
19 . The system of claim 17 , wherein the digital documents include documents obtained from public internet websites.
20 . A computer-implemented method comprising:
determining, by a processing system, a recipient list based on a recipient profile and a knowledge data structure that stores entity data relating to a plurality of entities and relationship data relating to a plurality of relationships;
generating and providing, by the processing system, a personalized message personalized to an individual in the recipient list based on the entity data and the relationship data,
extracting, by the processing system, a new entity from digital documents; creating, by the processing system, a new relationship relating to the new entity and an existing entity based on the digital documents and the knowledge data structure representing the existing entity by an existing node; and
updating, by the processing system, the knowledge data structure with a new node representing the new entity and a new edge corresponding to the new relationship, wherein the new edge connects the new node to the existing node.