Contextually relevant content sharing in high-dimensional conceptual content mapping
An example method disclosed herein includes receiving a request for content items relevant to a concept within a first knowledge space, where the first knowledge space includes a plurality of concept groupings of a plurality of nodes representing a plurality of content items. The method further includes identifying a plurality of content items in a second knowledge space relevant to the concept and receiving a selection of one or more of the content items for addition to the first knowledge space. The method further includes adding the selected content items to the first knowledge space.
1 . A central content management system comprising:
a content bank including a plurality of nodes representing a plurality of content items, the content bank further including a set of relationships between the plurality of nodes;
a content bank manager configured to receive a request from a user associated with an organization to add content items from the content bank to a knowledge space associated with the organization, wherein the knowledge space comprises one or more nodes storing one or more content items positioned in a multidimensional space, wherein the relative position of the one or more nodes in the multidimensional space represent probabilistic relationships between one or more concepts presented by the one or more content items;
a content asset suggestion module configured to identify a group of content items of the content bank similar to the knowledge space; and
a content asset transfer module configured to transfer one or more of the group of content items from the content bank to the knowledge space.
2 . The central content management system of claim 1 , wherein the content asset transfer module is configured to transfer the one or more of the group of content items from the content bank to the knowledge space by placing the one or more of the group of content items within a space represented by the knowledge space.
3 . The central content management system of claim 1 , wherein the content asset suggestion module is configured to identify the group of content items of the content bank similar to the knowledge space using a comparison of concepts included in a plurality of similar knowledge spaces and distances between a spatial center of a missing concept and embedding vectors representing the content items of the group of content items.
4 . The central content management system of claim 1 , wherein the content asset transfer module is configured to transfer the one or more of the group of content items from the content bank to the knowledge space by recontextualizing the knowledge space to include the one or more of the group of content items, wherein recontextualizing the knowledge space comprises:
generating a second set of one or more nodes comprising the one or more nodes of the knowledge space and the one or more group of content items;
generating a probability model defining a probabilistic relationship between words presented in the second set of one or more nodes;
generating one or more concept groupings based on the probability model, wherein each concept grouping of the one or more concept groupings comprise nodes of the second set of one or more nodes that are related to a similar concept;
regenerating the knowledge space based on the probability model; and
positioning the second set of one or more nodes in the multidimensional space of the knowledge space based on the probability model and the one or more concept groupings.
5 . The central content management system of claim 1 , wherein the content asset suggestion module is configured to identify the group of content items of the content bank similar to the knowledge space using distances between a spatial center of the knowledge space and embedding vectors representing the plurality of content items.
6 . The central content management system of claim 1 , wherein the content asset suggestion module is further configured to present prices for the identified group of content items of the content bank, wherein the content bank manager is configured to determine prices for the plurality of content items based on quality of the plurality of content items and an asset type of each of the plurality of content items.
7 . The central content management system of claim 1 , wherein the plurality of content items of the content bank are received from a second knowledge space.
8 . A computer-implemented method for content management, comprising:
generating, via a processor, a first knowledge space comprising one or more nodes representing one or more content items positioned in a multidimensional space, wherein the relative position of the one or more nodes in the multidimensional space represent probabilistic relationships between one or more concepts presented by the one or more content items;
contextualizing, via the processor, the first knowledge space by utilizing a clustering algorithm to identify one or more concept groupings of the first knowledge space, wherein each concept grouping of the one or more concept groupings comprises one or more nodes of the first knowledge space directed to a similar concept;
receiving, via the processor, a request to add an additional content item to the first knowledge space, wherein the request comprises at least one of a request to add an additional content item relevant to a first node of the first knowledge space, a request to add an additional content item relevant to a first concept grouping of the first knowledge space, or a request to add an additional content item relevant to the first knowledge space;
generating, via the processor, a multidimensional request vector representative of the request;
identifying, via the processor, a second node of a second knowledge space relevant to the request by comparing the distance between the request vector and a multidimensional embedding vector representative of the second node;
receiving, via the processor, a selection of the second node for addition to the first knowledge space; and
transferring, via the processor, the second node to the first knowledge space.
9 . The computer-implemented method of claim 8 , wherein the request comprises the request to add an additional content item relevant to the first node of the first knowledge space, and wherein generating the request vector comprises generating the request vector based on a multidimensional embedding vector representative of the first node.
10 . The computer-implemented method of claim 8 , wherein the request comprises the request to add an additional content item relevant to the first concept grouping of the first knowledge space, and wherein generating the request vector comprises:
determining, via the processor, a centroid of the concept grouping; and
generating, via the processor, the request vector based on a multidimensional vector representative of the centroid of the concept grouping.
11 . The computer-implemented method of claim 8 , wherein the request comprises the request to add an additional content item relevant to the first knowledge space, and wherein generating the request vector comprises:
determining, via the processor, a centroid of the first knowledge space; and
generating, via the processor, the request vector based on a multidimensional vector representative of the centroid of the first knowledge space.
12 . The computer-implemented method of claim 8 , wherein transferring the second node to the first knowledge space comprises:
determining, via the processor, one or more similarity scores representing a concept presented by the second node and the one or more concept groupings, wherein the one or more similarity scores are each below a similarity score threshold;
regenerating, via the processor, the first knowledge space comprising the one or more nodes of the first knowledge space and the second node; and
recontextualizing, via the processor, the first knowledge space using the clustering algorithm.
13 . The computer-implemented method of claim 8 , wherein regenerating the first knowledge space comprises:
generating, via the processor, a probability model defining a probabilistic relationship between words presented in the one or more nodes of the first knowledge space and the second node; and
regenerating, via the processor, the first knowledge space based on the probability model.
14 . The computer-implemented method of claim 8 , wherein transferring the second node to the first knowledge space comprises:
determining, via the processor, one or more centroids of the one or more concept groupings;
comparing, via the processor, the position of the embedding vector representative of the second node to the position of the one or more centroids to identify a first centroid of the one or more centroids positioned closest to the embedding vector; and
placing, via the processor, the second node in a concept grouping of the one or more concept groupings corresponding to the first centroid.