Functionality is described herein for generating a model on the basis of user-behavioral data and knowledge data. In one case, the user-behavioral data identifies queries submitted by users, together with selections made by the users in response to the queries. The knowledge data represents relationships among linguistic items, as expressed by one or more structured knowledge resources. The functionality leverages the knowledge data to supply information regarding semantic relationships which may not be adequately captured by the user-behavioral data, to thereby produce a more robust and accurate model (compared to a model produced on the basis of only user-behavioral data). Functionality is also described herein for applying the model, once trained. In one case, the model may correspond to a deep learning model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, implemented by one or more computing devices, for generating a model, comprising: sampling user-behavioral data from a repository of user-behavioral data, the user-behavioral data identifying linguistic items submitted by users, together with selections made by the users in response to the linguistic items; sampling knowledge data from one or more structured knowledge resources, the knowledge data representing relationships among linguistic items expressed by said one or more structured knowledge resources; and generating a model on the basis of the user-behavioral data and the knowledge data, using a machine-learning training process, the model providing logic for assessing relevance of linguistic items, said sampling of user-behavioral data, said sampling of knowledge data, and said generating of the model being performed using at least one processing device associated with said one or more computing devices. 2. The method of claim 1, wherein each instance of the user-behavioral data represents at least a query, an answer to the query, and a click value that indicates whether a user selected the answer in response to the query. 3. The method of claim 1, wherein each instance of the knowledge data represents at least a pair of linguistic items, a distance measure that represents a degree of relatedness between the pair of linguistic items, and at least one frequency measure associated with the pair of linguistic items. 4. The method of claim 1, wherein the knowledge data includes: a first subset of instances of knowledge data that represent pairs of related linguistic items; and a second subset of instances of knowledge data that represent pairs of unrelated linguistic items, wherein said one or more structured knowledge resources provide information which indicates whether each pair of linguistic items is related or unrelated. 5. The method of claim 1, further comprising converting the user-behavioral data and the knowledge data into a consistent format. 6. The method of claim 5, wherein said converting comprises converting each instance of the knowledge data into a format which matches a format of the user-behavioral data. 7. The method of claim 5, wherein: each instance of the user-behavioral data represents at least a query, an answer to the query, and a click value that indicates whether a user selected the answer in response to the query, each instance of the knowledge data represents at least a pair of linguistic items, a distance measure that represents a degree of relatedness between the pair of linguistic items, and at least one frequency measure associated with the pair of linguistic items, and wherein said converting comprises converting the distance measure associated with the knowledge data into a click value, and converting said at least one frequency measure into a weight value. 8. The method of claim 1, further comprising integrating the user-behavioral data with the knowledge data to produce master training data, wherein said generating produces the model based on the master training data. 9. The method of claim 8, wherein said integrating involves duplicating each instance of the knowledge data in accordance with a weight value associated with the instance. 10. The method of claim 1, wherein the model corresponds to a deep learning model that uses a multi-layer neural network to project input linguistic items into concept vectors expressed in a semantic space. 11. A computer readable storage medium for storing computer readable instructions, the computer readable instructions providing a semantic transformation module when executed by one or more processing devices, the computer readable instructions comprising: logic configured to use a deep learning model to map an input linguistic item into a concept vector in a high-level conceptual space, the deep learning model capturing semantic relationships learned, in a machine-learning training process, from user-behavioral data and knowledge data, the user-behavioral data identifying linguistic items submitted by users together with selections made by the users in response to the linguistic items, and the knowledge data representing relationships among linguistic items expressed by one or more structured knowledge resources. 12. A computer system, comprising: a search engine, implemented by at least one computing device, for receiving an input linguistic item, and for identifying and delivering at least one output item that has been determined to be relevant to the input linguistic item, the search engine identifying said at least one output item using a model, the model being produced in a machine-learning training process based on user-behavioral data and knowledge data, the user-behavioral data identifying linguistic items submitted by users together with selections made by the users in response to the linguistic items, and the knowledge data representing relationships among linguistic items expressed by one or more structured knowledge resources. 13. The computer system of claim 12, further comprising: a compilation module configured to produce master training data based on the user-behavioral data and the knowledge data; and a training system configured to produce the model on the basis of the master training data, using the machine-learning training process. 14. The computer system of claim 13, wherein the compilation module comprises: a first sampling module configured to sample the user-behavioral data from a repository of user-behavioral data, to produce sampled user-behavioral data; a second sampling module configured to sample the knowledge data from said one or more structured knowledge resources, to produce sampled knowledge data; a conversion module configured to convert the sampled user-behavioral data and the sampled knowledge data into a consistent format, to produce consistent user-behavioral data and consistent knowledge data; and an integration module configured to integrate the consistent user-behavioral data with the consistent knowledge data, to produce the master training data. 15. The computer system of claim 14, wherein each instance of the sampled user-behavioral data represents at least a query, an answer to the query, and a click value that indicates whether a user selected the answer in response to the query. 16. The computer system of claim 14, wherein each instance of the sampled knowledge data represents at least a pair of linguistic items, a distance measure that represents a degree of relatedness between the pair of linguistic items, and at least one frequency measure associated with the pair of linguistic items. 17. The computer system of claim 14, wherein the master training data includes: a first subset of instances of knowledge data that represent pairs of related linguistic items; and a second subset of instances of knowledge data that represent pairs of unrelated linguistic items, wherein said one or more structured knowledge resources provide information which indicates whether each pair of linguistic items is related or unrelated. 18. The computer system of claim 14, wherein said conversion module is configured to convert each instance of the sampled knowledge data into a format which matches a format of the sampled user-behavioral data. 19. The computer system of claim 12, wherein the model corresponds to a deep learning model that uses a multi-layer neural network to project input linguistic items into concept vectors expressed in a semantic space. 20. The computer system of claim 12, wherein the model corresponds to any type of model other than a deep learning model, the deep learning model corresponding to a model that uses a multi-layer neural network to project input linguistic items into concept vectors expressed in a semantic space.