Systems and methods for contextual retrieval and contextual display of records
Provided are systems and methods for the contextual retrieval and contextual display of records. A search query and/or search results may be contextually enhanced based on (i) natural language processing (NLP) models, (ii) user behavior, and/or (iii) relationships between various entities involved in a search, such as between users, records, and/or fields of expertise. Contextually enhanced search results may be delivered and displayed to a user on a user interface in a contextually relevant order.
1. A computer-implemented method for contextual enhancement of search queries, comprising:
(a) receiving from a user, via a user interface, a search query;
(b) determining, with aid of one or more computer processors, a first natural language processing (NLP) model of a plurality of NLP models corresponding to the search query, wherein the first NLP model corresponds to a first set of one or more keywords;
(c) determining, with aid of one or more computer processors, a second NLP model, of the plurality of NLP models, wherein the second NLP model corresponds to a second set of one or more keywords, wherein the first NLP model and the second NLP model have a proximity relationship with a weight value at or above a predetermined threshold;
(d) enhancing the search query with keywords of the second set of one or more keywords not originally present in the search query, to generate an enhanced search query; and
(e) executing the enhanced search query.
2. The computer-implemented method of claim 1 , wherein the plurality of NLP models are stored in one or more databases.
3. The computer-implemented method of claim 2 , wherein the plurality of NLP models are stored in a graph database.
4. The computer-implemented method of claim 3 , wherein the graph database further stores proximity relationships between the plurality of NLP models.
5. The computer-implemented method of claim 1 , further comprising determining, with aid of the one or more computer processors, an additional one or more NLP models of the plurality of NLP models, wherein each of the additional one or more NLP models and the first NLP model has a respective proximity relationship with a respective weight value at or above the predetermined threshold, wherein each of the additional one or more NLP models corresponds to a respective set of one or more keywords.
6. The computer-implemented method of claim 5 , further comprising enhancing the search query with the respective set of one or more keywords to generate the enhanced search query.
7. The computer-implemented method of claim 5 , wherein the first NLP model is determined as corresponding to the search query by tokenizing the search query into one or more tokens to generate a tokenized query, and processing the tokenized query to the first set of one or more keywords.
8. The computer-implemented method of claim 5 , wherein the search query is processed to each of the plurality of NLP models to determine the first NLP model as corresponding to the search query.
9. A computer system for contextual enhancement of search queries, comprising:
one or more processors;
a memory, communicatively coupled to the one or more processors, including instructions executable by the one or more processors, individually or collectively, to:
(a) receive from a user, via a user interface, a search query;
(b) determine, with aid of one or more computer processors, a first natural language processing (NLP) model of a plurality of NLP models corresponding to the search query, wherein the first NLP model corresponds to a first set of one or more keywords;
(c) determine, with aid of one or more computer processors, a second NLP model, of the plurality of NLP models, wherein the second NLP model corresponds to a second set of one or more keywords, wherein the first NLP model and the second NLP model have a proximity relationship with a weight value at or above a predetermined threshold;
(d) enhance the search query with keywords of the second set of one or more keyword not originally present in the search query, to generate an enhanced search query; and
(e) execute the enhanced search query.
10. The computer system of claim 9 , further comprising one or more databases, wherein the plurality of NLP models are stored in the one or more databases.
11. The computer system of claim 9 , further comprising a graph database, wherein the plurality of NLP models are stored in the graph database.
12. The computer system of claim 11 , wherein the graph database further stores proximity relationships between the plurality of NLP models.
13. The computer system of claim 9 , wherein the one or more processors are further configured to, individually or collectively, determine an additional one or more NLP models of the plurality of NLP models, wherein each of the additional one or more NLP models and the first NLP model has a respective proximity relationship with a respective weight value at or above the predetermined threshold, wherein each of the additional one or more NLP models corresponds to a respective set of one or more keywords.
14. The computer system of claim 13 , wherein the one or more processors are further configured to, individually or collectively, enhance the search query with the respective set of one or more keywords to generate the enhanced search query.
15. The computer system of claim 13 , wherein the one or more processors are further configured to, individually or collectively, determine the first NLP model as corresponding to the search query by tokenizing the search query into one or more tokens to generate a tokenized query, and processing the tokenized query to the first set of one or more keywords.
16. The computer system of claim 13 , wherein the one or more processors are further configured to, individually or collectively, process the search query to each of the plurality of NLP models to determine the first NLP model as corresponding to the search query.