Systems and methods for processing information
Systems and methods can be provided for processing information. Multiple categories of ties for multiple types of assets can be identified, where a tie can comprise a relationship between assets. A relative importance for each tie category can be determined. A category weight for each tie category can be assigned using a determined relative importance for each tie category. A tie value can be combined with a tie category weight to create a weighted tie value for each tie. All tie weighted values can be combined into a meta tie value.
1 . A method for processing information, comprising:
receiving, by a processor, a data set comprising information from multiple sources including patents, scientific articles, company data, and financial data, wherein the data is associated with a company;
extracting, by the processor utilizing natural language processing, attributes from the information, wherein the extracting comprises removing stopwords, performing stemming, and generating word frequency distributions of the data set, wherein the natural language processing comprises tokenizing the data set;
generating, by the processor, a graph database with nodes corresponding to the information and ties corresponding to the attributes, wherein a value of the tie corresponds to at least one of a co-occurrence, structural equivalents, or relationship between the attributes, wherein the value is calculated using a similarity algorithm selected from cosine similarity, Euclidean distance, Manhattan distance, Minkowski distance, and Jaccard similarity, wherein generating the graph database further comprises:
identifying, by the processor in the graph database, multiple tie categories for multiple types of information that can be categorized, a tie comprising a relationship between information;
determining, by the processor, a relative definition for each tie category based on at least one of centrality, eigenvector centrality, and betweenness centrality of nodes in the graph database;
assigning, by the processor, a category weight for each tie category using the determined relative definition for each tie category, wherein the category weight represents a probability of information transfer between nodes;
combining, by the processor, the value of the tie with the tie category weight to create a weighted tie value for each tie, wherein the weighted tie value represents a cumulative strength of connection between nodes; and
combining, by the processor, all weighted tie values into a meta tie value to identify clusters of similar nodes and to rank nodes based on their importance and influence within the graph database, wherein the meta tie value is further configured to identify bridges comprising nodes that connect otherwise unconnected clusters of nodes in the graph database; and
predicting, by the processor using a machine learning model, a future outcome of success for the company based on an input comprising the weighted tie value for each tie and the meta tie value.
2 . The method of claim 1 , wherein the multiple sources further include social media data and news articles.
3 . The method of claim 1 , wherein the attributes extracted from the information include patent citations, scientific article citations, company financial metrics, and founder information.
4 . The method of claim 1 , wherein the relative definition for each tie category is determined based on a combination of centrality, eigenvector centrality, and betweenness centrality of nodes in the graph database.
5 . The method of claim 1 , further comprising filtering the nodes and ties in the graph database based on predefined criteria before combining the weighted tie values.
6 . The method of claim 1 , wherein the machine learning model comprises at least one of a gradient boosted classifier, a logistic regression classifier, a neural network classifier, and a support vector machine classifier.
7 . The method of claim 1 , further comprising visualizing the graph database using a network visualization tool to display relationships between nodes.
8 . The method of claim 1 , wherein the future outcome of success for the company is predicted for a specific time period.
9 . The method of claim 1 , further comprising adjusting the prediction based on a stage of maturity of the company.
10 . The method of claim 1 , further comprising determining potential sources of innovation comprising at least one of people, companies, technologies, concepts, or themes based on the meta tie value.
11 . A system for processing information, comprising:
a processor; and
a memory storing instructions that, when executed by the processor, cause the processor to:
receive a data set comprising information from multiple sources including patents, scientific articles, company data, and financial data, wherein the data is associated with a company;
extract, utilizing natural language processing, attributes from the information, wherein the extracting comprises removing stopwords, performing stemming, and generating word frequency distributions of the data set, wherein the natural language processing comprises tokenizing the data set;
generate a graph database with nodes corresponding to the information and ties corresponding to the attributes, wherein a value of the tie corresponds to at least one of a co-occurrence, structural equivalents, or relationship between the attributes, wherein the value is calculated using a similarity algorithm selected from cosine similarity, Euclidean distance, Manhattan distance, Minkowski distance, and Jaccard similarity, wherein the instructions that cause the processor to generate the graph database further cause the processor to:
identify, in the graph database, multiple tie categories for multiple types of information that can be categorized, a tie comprising a relationship between information;
determine a relative definition for each tie category based on at least one of centrality, eigenvector centrality, and betweenness centrality of nodes in the graph database;
assign a category weight for each tie category using the determined relative definition for each tie category, wherein the category weight represents a probability of information transfer between nodes;
combine the value of the tie with the tie category weight to create a weighted tie value for each tie, wherein the weighted tie value represents a cumulative strength of connection between nodes; and
combine all weighted tie values into a meta tie value to identify clusters of similar nodes and to rank nodes based on their importance and influence within the graph database, wherein the meta tie value is further configured to identify bridges comprising nodes that connect otherwise unconnected clusters of nodes in the graph database; and
predict, using a machine learning model, a future outcome of success for the company based on an input comprising the weighted tie value for each tie and the meta tie value.
12 . The system of claim 11 , wherein the multiple sources further include social media data and news articles.
13 . The system of claim 11 , wherein the attributes extracted from the information include patent citations, scientific article citations, company financial metrics, and founder information.
14 . The system of claim 11 , wherein the relative definition for each tie category is determined based on a combination of centrality, eigenvector centrality, and betweenness centrality of nodes in the graph database.
15 . The system of claim 11 , wherein the instructions further cause the processor to filter the nodes and ties in the graph database based on predefined criteria before combining the weighted tie values.
16 . The system of claim 11 , wherein the machine learning model comprises at least one of a gradient boosted classifier, a logistic regression classifier, a neural network classifier, and a support vector machine classifier.
17 . The system of claim 11 , wherein the instructions further cause the processor to visualize the graph database using a network visualization tool to display relationships between nodes.
18 . The system of claim 11 , wherein the future outcome of success for the company is predicted for a specific time period.
19 . The system of claim 11 , wherein the instructions further cause the processor to adjust the prediction based on a stage of maturity of the company.
20 . The system of claim 11 , wherein the instructions further cause the processor to determine potential sources of innovation comprising at least one of people, companies, technologies, concepts, or themes based on the meta tie value.
21 . The method of claim 1 , wherein the bridges are identified using at least one of betweenness centrality, Katz centrality metric, Freeman metric, or Burt's constraint metric to determine nodes positioned at intersections of previously disconnected networks.
22 . The system of claim 11 , wherein the bridges are identified using at least one of betweenness centrality, Katz centrality metric, Freeman metric, or Burt's constraint metric to determine nodes positioned at intersections of previously disconnected networks.