Given two heterogeneous entities, the prevalence of text data provides rich co-occurrence information for them. However, the co-occurrence only is noisy—not only may the co-occurrence just imply an accidental writing, but also it may just reflect the domain-specific common words. Only those strong relevance between entities supported by rich relevance contexts in data can indicate meaningful entity relationships. Strong relevance between heterogeneous entities are mined from their co-occurrences. Drug-disease therapeutic relationships are used as the example to demonstrate an application of this work.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving data associated with a co-occurrence graph among heterogeneous entities, said co-occurrence graph comprising a plurality of nodes, each node representing an entity in said heterogeneous entities, wherein any two nodes in said co-occurrence graph are connected by an edge when they co-occur in a knowledge base, with a weight of said edge being equal to the number of times entities associated with said two nodes co-occur in said knowledge base; receiving a query comprising a query entity name and a target entity type; receiving a plurality of meta paths to constrain co-occurrence scope of any two heterogeneous entities in said co-occurrence graph; generating a subgraph of said co-occurrence graph with path instances of said received meta paths; and outputting entities from said subgraph belonging to said target entity type and having strong relevance with said query entity name based on a probabilistic context-aware relevance model, where said strong relevance is constrained by said received meta paths. 2. The computer-implemented method of claim 1, wherein said query entity name is a disease name and said target entity type is “Drug”. 3. The computer-implemented method of claim 1, wherein said data associated with said co-occurrence graph is built from a plurality of the following: FDA-approved drugs, diseases extracted from human disease ontology, small-molecule chemical compounds with drug indications from a first database, terms in a tree used as a metadata to index documents in a second database, and targets made up of four sub-types: tissue, cell-line, protein, and organism. 4. The computer-implemented method of claim 1, wherein said received meta paths are any of, or a combination of, the following: “Drug-Disease”, “Drug-Drug-Disease”, “Drug-Compound-Disease”, “Drug-Disease-Disease” and “Drug-MeSH Term-Disease”. 5. The computer-implemented method of claim 1, wherein said heterogeneous entities are selected from any of the following: drug, compound, disease, target, and Medical Subject Headings (MeSH). 6. The computer-implemented method of claim 1, wherein said heterogeneous entities are heterogeneous biological and/or chemical entities. 7. The computer-implemented method of claim 1, wherein said knowledge base is accessible over a network. 8. The computer-implemented method of claim 7, wherein said network is any of the following: local area network (LAN), wide area network (WAN), the Internet, or cellular network. 9. A non-transitory, computer accessible memory medium storing program instructions for mining strong relevance between heterogeneous entities from their co-occurrences comprising: computer readable program code receiving data associated with a co-occurrence graph among heterogeneous entities, said co-occurrence graph comprising a plurality of nodes, each node representing an entity in said heterogeneous entities, wherein any two nodes in said co-occurrence graph are connected by an edge when they co-occur in a knowledge base, with a weight of said edge being equal to the number of times entities associated with said two nodes co-occur in said knowledge base; computer readable program code receiving a query comprising a query entity name and a target entity type; computer readable program code receiving a plurality of meta paths to constrain co-occurrence scope of any two heterogeneous entities in said co-occurrence graph; computer readable program code generating a subgraph of said co-occurrence graph with path instances of said received meta paths; and computer readable program code outputting entities from said subgraph belonging to said target entity type and having strong relevance with said query entity name based on a probabilistic context-aware relevance model, where said strong relevance is constrained by said received meta paths. 10. A method comprising: receiving a co-occurrence graph among different entities, wherein (i) each node in said co-occurrence graph represents an entity and (ii) two nodes in said co-occurrence graph are connected by an edge if they occur together in a document within a collection of documents, and wherein a weight on each edge equals the number of times two entities occur together in said collection of documents; receiving a query comprising a query entity name and a target entity type; receiving pre-specified meta paths to constrain a scope of co-occurrence between two different entities in said co-occurrence graph; and outputting entities that (i) belong to said target entity type, and (ii) are functionally relevant to an instance of said query entity name. 11. The method of claim 10, comprising: building a probabilistic context-aware relevance model to measure said functional relevance between said query entity name and said target entity type, in view of said scope, by: (i) profiling said query entity name using a first set of adjacent entities within said scope; (ii) profiling said target entity type using a set of adjacent entities within said scope; (iii) wherein said functional relevance between said query entity name and said target entity type is a weighted product of the functional relevance between all pairs of adjacent entities, wherein one entity comes from said first set of adjacent entities and the other entity comes from said second set of adjacent entities; and (iv) iteratively computing the functional relevance between any pair of adjacent entities according to steps (i), (ii), and (iii); wherein said weight in step (iii) measures an inverse document frequency (IDF) based importance of adjacent entities to said query entity name and said target entity type. 12. The computer-implemented method of claim 10, wherein said query entity name is a disease name and said target entity type is “Drug”. 13. The method of claim 10, wherein data associated with said co-occurrence graph are built from a plurality of the following: FDA-approved drugs, diseases extracted from human disease ontology, small-molecule chemical compounds with drug indications from a first database, terms in a tree used as a metadata to index documents in a second database, and targets made up of four sub-types: tissue, cell-line, protein, and organism. 14. The method of claim 10, wherein said received meta paths are any of, or a combination of, the following: “Drug-Disease”, “Drug-Drug-Disease”, “Drug-Compound-Disease”, “Drug-Disease-Disease” and “Drug-MeSH Term-Disease”. 15. The method of claim 10, wherein said heterogeneous entities are selected from any of the following: drug, compound, disease, target, and Medical Subject Headings (MeSH). 16. The method of claim 10, wherein said heterogeneous entities are heterogeneous biological and/or chemical entities. 17. The method of claim 10, wherein said collection of documents are accessible over a network. 18. The method of claim 18, wherein said network is any of the following: local area network (LAN), wide area network (WAN), the Internet, or cellular network.