System and method of semantic correlation of rich content

1. A system to recommend content to a user, comprising:

a computer;

a content of interest to the user stored on the computer;

a first semantic abstract representing the content of interest stored on the computer, the first semantic abstract including a first plurality of state vectors;

a second semantic abstract representing a second content, the second semantic abstract including a second plurality of state vectors;

a semantic abstract comparer to compare the first semantic abstract to the second semantic abstract; and

a content recommender to recommend the second content if the first semantic abstract is within a threshold distance of the second semantic abstract,

wherein:

a dictionary includes a directed set of concepts including a maximal element and directed links between pairs of concepts in the directed set, the directed links defining “is a” relationships between the concepts in the pairs of concepts, so that each concept is either a source or a sink of at least one directed link;

for each concept in the directed set other than the maximal element, at least one chain in the directed set includes a set of directed links between pairs of concepts connecting the maximal element and the concept;

a basis includes a subset of the chains; and

each state vector in the first semantic abstract and the second semantic abstract measures how concretely a concept is represented in each chain in the basis by identifying the smallest predecessor in the chain in relation to the concept.

2. A system according to claim 1 , further comprising:

a content receiver to receive the content of interest;

a content presenter to present the content of interest to the user; and

a content of interest identifier to identify the content of interest after receiving a user request for the content of interest.

3. A system according to claim 1 , further comprising:

a content monitor to monitor the content of interest;

a phrase extractor to extract phrases in the content of interest; and

a semantic abstract generator to generate the first semantic abstract for the content of interest using the phrases.

4. A system according to claim 3 , wherein the semantic abstract generator includes:

a state vector constructor to construct state vectors in a topological vector space for each phrase extracted by the phrase extractor; and

a collector to collect the state vectors into the first semantic abstract for the content of interest.

5. A system according to claim 1 , further comprising an interface to receive a level of interest for the content of interest.

6. A system according to claim 1 , further comprising a content library to store at least one of the content of interest and the second content.

7. A system according to claim 6 , wherein the content recommender includes a link to the second content.

8. A system according to claim 1 , wherein the content recommender includes an alert to notify the user of a future presentation of the second content.

9. A system according to claim 1 , further comprising a preferred semantic abstract storage to store the first semantic abstract for the content of interest.

10. A system according to claim 9 , wherein the preferred semantic abstract storage includes a preferred semantic abstract storage for a community of users.

11. A method implemented in a computer, including a processor and a memory, for identifying and recommending content to a user, comprising:

identifying a first semantic abstract corresponding to a content of interest, the first semantic abstract including a first plurality of state vectors, wherein a dictionary includes a directed set of concepts including a maximal element and directed links between pairs of concepts in the directed set, the directed links defining “is a” relationships between the concepts in the pairs of concepts, so that each concept is either a source or a sink of at least one directed link, for each concept in the directed set other than the maximal element, at least one chain in the directed set includes a set of directed links between pairs of concepts connecting the maximal element and the concept, a basis includes a subset of the chains, and each state vector in the first semantic abstract measures how concretely a concept is represented in each chain in the basis by identifying the smallest predecessor in the basis chain in relation to the concept;

identifying a second semantic abstract corresponding to a second content, the second semantic abstract including a second plurality of state vectors, each state vector in the second semantic abstract measuring how concretely a concept is represented in each chain in the basis by identifying the smallest predecessor in the basis chain in relation to the concept;

measuring a distance between the first semantic abstract to the second semantic abstract; and

recommending the second content if the first semantic abstract is within a threshold distance of the second semantic abstract.

12. A method according to claim 11 , further comprising:

receiving the content of interest;

identifying the content of interest after receiving a user request for the content of interest; and

presenting the content of interest.

13. A method according to claim 11 , further comprising:

monitoring the content of interest;

extracting dominant phrases from the content of interest; and

generating the semantic abstract for the content of interest using the dominant phrases.

14. A method according to claim 13 , wherein generating the semantic abstract for the content of interest includes:

identifying the directed set of concepts as the dictionary;

selecting the subset of the chains to form a basis for the dictionary;

measuring how concretely the dominant phrases are represented in each chain in the basis and the dictionary;

constructing at least one state vector in the topological vector space for each dominant phrase in using the measures of how concretely each dominant phrase is represented in each chain in the dictionary and the basis; and

collecting the state vectors into the semantic abstract for the content of interest.

15. A method according to claim 14 , wherein measuring a distance includes measuring a Hausdorff distance between the first semantic abstract and the second semantic abstract.

16. A method according to claim 11 , wherein identifying a second semantic abstract corresponding to a second content includes:

identifying dominant words in the first content; and

searching for the second content using the dominant words.

17. A method according to claim 16 , further comprising:

generating the second semantic abstract for the second content; and

confirming the second content is similar to the content of interest if the first semantic abstract is within a threshold distance of the second semantic abstract.

18. A method according to claim 11 , wherein identifying a first semantic abstract includes identifying the first semantic abstract in a semantic abstract storage.

19. A method according to claim 11 , wherein recommending the second content includes presenting a link to the second content.

20. A method according to claim 11 , wherein recommending the second content includes alerting the user about a future presentation of the second content.

21. An article, comprising a storage medium, said storage medium having stored thereon instructions, that, when executed by a machine, result in:

identifying a first semantic abstract corresponding to a content of interest, the first semantic abstract including a first plurality of state vectors, wherein a dictionary includes a directed set of concepts including a maximal element and directed links between pairs of concepts in the directed set, the directed links defining “is a ” relationships between the concepts in the pairs of concepts, so that each concept is either a source or a sink of at least one directed link, for each concept in the directed set other than the maximal element, at least one chain in the directed set includes a set of directed links between pairs of concepts connecting the maximal element and the concept, a basis includes a subset of the chains, and each state vector in the first semantic abstract measures how concretely a concept is represented in each chain in the basis by identifying the smallest predecessor in the basis chain in relation to the concept;

identifying a second semantic abstract corresponding to a second content, the second semantic abstract including a second plurality of state vectors, each state vector in the second semantic abstract measuring how concretely a concept is represented in each chain in the basis;

comparing the first semantic abstract to the second semantic abstract; and

recommending the second content if the first semantic abstract is within a threshold distance of the second semantic abstract.

22. An article according to claim 21 , further comprising:

receiving the content of interest;

identifying the content of interest after receiving a user request for the content of interest; and

presenting the content of interest,

23. An article according to claim 21 , further comprising:

monitoring the content of interest;

extracting dominant phrases from the content of interest; and

generating the semantic abstract for the content of interest using the dominant phrases.

24. An article according to claim 23 , wherein generating the semantic abstract for the content of interest includes:

identifying the directed set of concepts as the dictionary;

selecting the subset of the chains to form a basis for the dictionary;

measuring how concretely the dominant phrases are represented in each chain in the basis and the dictionary;

constructing at least one state vector in the topological vector space for each dominant phrase in using the measures of how concretely each dominant phrase is represented in each chain in the dictionary and the basis; and

collecting the state vectors into the semantic abstract for the content of interest.

25. An article according to claim 24 , wherein measuring a distance includes measuring a Hausdorff distance between the first semantic abstract and the second semantic abstract.

26. An article according to claim 21 , wherein identifying a second semantic abstract corresponding to a second content includes:

identifying dominant words in the first content; and

searching for the second content using the dominant words.

27. A method according to claim 26 , further comprising:

generating the second semantic abstract for the second content; and

confirming the second content is similar to the content of interest if the first semantic abstract is within a threshold distance of the second semantic abstract.