Association significance
Systems and techniques for determining significance between entities are disclosed. The systems and techniques identify a first entity having an association with a second entity, apply a plurality of association criteria to the association, weight each of the criteria based on defined weight values, and compute a significance score for the first entity with respect to the second entity based on a sum of a plurality of weighted criteria values. The systems and techniques utilize information from disparate sources to create a uniquely powerful signal. The systems and techniques can be used to identify the significance of relationships (e.g., associations) among various entities including, but not limited to, organizations, people, products, industries, geographies, commodities, financial indicators, economic indicators, events, topics, subject codes, unique identifiers, social tags, industry terms, general term/s, metadata elements, classification codes, and combinations thereof.
1. A method of computing significance between entities comprising: automatically generating, by an identification module, a directed graph representing entities including a first and a second entity identified in a set of documents, the directed graph comprising a structure comprising a set of nodes and vertices, each node representing an entity identified in the set of documents and vertices of each node representing an association between entities, the directed graph providing for improved relationship and relationship significance identification, quantification, and storage of the entities and information associated with the entities represented in the directed graph;
automatically identifying, by the identification module implementing a search using the structure of the directed graph, the first entity and the second entity from a plurality of entities in the set of documents, the first entity having a first association with the second entity, and the second entity having a second association with the first entity, wherein identification and recall of the first entity and the second entity from the plurality of entities and the set of documents is improved based in part on the structure of the directed graph;
automatically, by a context module executing a set of code, generating a set of context pairs representing a contextual relationship existing between the first and second entities, each context pair representing a context that exists between two entities, wherein the context is a circumstance in which the first and second entities are referenced within a document in the set of documents;
assigning a plurality of criteria values to the first association, the plurality of criteria values based on a plurality of association criteria comprising interestingness, recent interestingness, validation, shared neighbor, temporal significance, context consistency, recent activity, current clusters, and surprise element, the plurality of criteria values being stored in a set of contingency tables;
automatically computing, by an association module, a significance score for the first entity with respect to the second entity based on a sum of the plurality of weighted criteria values for the first association, the significance score indicating a level of significance of the second entity to the first entity, wherein the significance score represents a quantification of the contextual relationship between the first and second entities;
generating, by a signal module, a signal associated with the computed significance score; and
transmitting, by the signal module, the signal.
2. The method of claim 1 , further comprising determining by the identification module a frequency and cooccurrence of entities identified in the set of documents.
3. The method of claim 1 , further comprising applying, by the association module, an interestingness criteria to the first association and wherein one of the interestingness measures includes a correlation coefficient that measures the degree of linear interdependency between a pair of entities.
4. The method of claim 1 , further comprising the association module applying one or more from the group consisting of interestingness criteria, recent interestingness criteria, validation criteria, shared neighbor criteria, temporal significance criteria, recent activity criteria, current clusters criteria, and the surprise element criteria.
5. The method of claim 1 , further comprising clustering, by a clustering module, entities identified by the identification module.
6. The method of claim 1 , further comprising weighting the plurality of criteria values.
7. The method of claim 1 , further comprising multiplying, by the association module, a user-configurable value associated with each of the plurality of criteria values.
8. The method of claim 7 , further comprising summing, by the association module, the plurality of multiplied criteria values to compute the significance score.
9. The method of claim 1 , wherein the signal module transmits the generated signal in response to a received request.
10. The method of claim 1 , further comprising storing, in a data store comprising a relational database, information generated by one or more of the association module, the cluster module, the context module, and the identification module.