The present invention provides a method a method for learning and using relationships among users and apps on a mobile device. The method includes collecting user profile information and app profile information on the mobile device and obtaining ontology knowledge on contexts and relationships. The user profile information is associated with users including one or more owners of the mobile device and one or more people whose information have been accessed on the mobile device. The method further includes generating a network model based on the user profile information, the app profile information and the ontology knowledge. The network model is a heterogeneous information network model that links the users and the apps. Further, information based on the generated network model are outputted, which at least one of a role of a user, a relationship between two apps or app functions, and a recommended apps list.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for learning and using relationships among users and apps on a mobile device, comprising: collecting user profile information and app profile information on the mobile device, wherein the user profile information is associated with users including one or more owners of the mobile device and one or more people whose information have been accessed on the mobile device; obtaining ontology knowledge on contexts and relationships, wherein the ontology knowledge includes a plurality of predefined concepts, features of the concepts, and relationships between the concepts; generating a network model based on the user profile information, the app profile information and the ontology knowledge, wherein the network model is a heterogeneous information network model that links the users and the apps; and outputting information based on the generated network model, wherein the outputted information includes at least one of a role of a user, a relationship between two apps or app functions, and a recommended apps list. 2. The method according to claim 1, wherein: the network model is defined as a directed graph with an object type mapping function and a link type mapping function, wherein the object type includes one of users and apps; a user has links to one or more other users and links to one or more apps; and an app has links to one or more other apps and links to one or more users. 3. The method according to claim 1, wherein: the user profile information of a user includes a digital representation of unique data about the user and user activity stream app data corresponding to the user; the user activity stream app data is presented as a text snippet when generating the network model; and the app profile information of an app includes text descriptions of the app and is presented as a text snippet when generating the network model. 4. The method according to claim 1, wherein: the ontology knowledge includes a controlled vocabulary which is structured into a hierarchical taxonomy, wherein the controlled vocabulary includes the plurality of concepts represented as classes; a class has at least one of a parent class and one or more child classes; and a class is associated with properties that describe one or more features of the class and one or more restrictions placed upon the class. 5. The method according to claim 3, wherein generating a network model further includes learning relationships between users via a user relationship topic model, comprising: obtaining a user text snippet from at least one of a text message, an email, and text collected by an app on the mobile device; applying a topic model to express probability distribution of topics in the user text snippet, wherein each topic is a probability distribution over words; choosing one or more specific topics based on the probability distribution; and obtaining relationships of users corresponding to the user text snippet based on the specific topics and the ontology knowledge. 6. The method according to claim 1, wherein generating a network model further includes learning app relationships between apps according to correlations between sequentially used apps, wherein: each app is represented as a word, a sequence of apps launched on the mobile device within a preset time period is represented as a sentence; and the word representations and the sentence representations are computed using a neural network to capture hidden linguistic regularities and patterns and to find relationships between apps. 7. The method according to claim 6, wherein: provided that T denotes a number of apps launched on the mobile device within the preset time period, (a1 . . . aT) denotes the sequence of apps launched on the mobile device as training data, and c denotes a size of the training data, a Skip-gram model is used to learn app relationships; and an objective of the Skip-gram model is to maximize an average log probability described by: 1 T  ∑ t = 1 T  ∑ - c ≤ j ≤ c   j ≠ 0  log   p  ( a t + j  a t ) . 8. The method according to claim 1, wherein: the user profile information and the app profile information are implicitly collected periodically; a time interval of collecting the user profile information and the app profile information varies based on a battery level of the mobile device; and based on the collected user profile information and the collected app profile information, user relationships and app connections are updated in the network model. 9. A system for learning and using relationships among users and apps on a mobile device on a mobile device, comprising one or more processors configured to: collect user profile information and app profile information on the mobile device, wherein the user profile information is associated with users including one or more owners of the mobile device and one or more people whose information have been accessed on the mobile device; obtain ontology knowledge on contexts and relationships, wherein the ontology knowledge includes a plurality of predefined concepts, features of the concepts, and relationships between the concepts; generate a network model based on the user profile information, the app profile information and the ontology knowledge, wherein the network model is a heterogeneous information network model that links the users and the apps; and output information based on the generated network model, wherein the outputted information includes at least one of a role of a user, a relationship between two apps or app functions, and a recommended apps list. 10. The system according to claim 9, wherein: the network model is defined as a directed graph with an object type mapping function and a link type mapping function; the network model includes two types of objects: users and apps; a user has links to one or more other users and links to one or more apps; and an app has links to one or more other apps and links to one or more users. 11. The system according to claim 9, wherein: the user profile information of a user includes a digital representation of unique data about the user and user activity stream app data; the user activity stream app data is presented as text snippets when generating the network model; and the app profile information of an app includes text descriptions of the app and is presented as text snippets when generating the network model. 12. The system according to claim 9, wherein: the ontology knowledge includes a controlled vocabulary which is structured into a hierarchical taxonomy, wherein the controlled vocabulary includes a plurality of concepts represented as classes; a class has at least one of a parent class and one or more child classes; and a class is associated with properties that describe one or more features of the class and one or more restrictions placed upon the class. 13. The system according to claim 9, wherein when generating a network model, the one or more processor is further configured to learn relationships between users via a user relationship topic model, comprising: obtaining a user text snippet from at least one of a text message, an email, and text collected by an app on the mobile device; applying a topic model to express probability distribution of topics in the user text snippet, wherein each topic is a probability distribution over words; choosing one or more specific topics based on the probability distribution; and obtain relationships of users corresponding to the user text snippet based on the specific topics and the ontology knowledge. 14. The system according to claim 9, wherein when generating a network model, the one or more processor is further configured to learn app relationships between apps according to correlations between sequentially used apps, wherein: each app is represented as a word, a sequence of apps launched on the mobile device within a preset time period is represented as a sentence; and the word representations and the sentence representations are computed using neural networks to capture hidden linguistic regularities and patterns and to find relationships between apps. 15. The system according to claim 14, wherein provided that T denotes a number of apps launched on the mobile device within the preset time period, (a1 . . . aT) denotes the sequence of apps launched on the mobile device as training data, and c denotes a size of the training data, a Skip-gram model is used to learn app relationships; and an objective of the Skip-gram model is to maximize an average log probability described by 1 T  ∑ t = 1 T  ∑ - c ≤ j ≤ c   j ≠ 0  log   p  ( a t + j  a t ) . 16. The system according to claim 9, wherein: the user profile information and the app profile information are implicitly collected periodically; a time interval of collecting the user profile information and the app profile information varies based on a battery level of the mobile device; and based on the collected user profile information and the collected app profile information, user relationships and app connections are updated in the network.