Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for making content-based recommendations using a user profile likelihood model. In one embodiment, a system is introduced that includes a plurality of models and storage units for storing, managing, and transforming product and user profile data. The system can also include a recommendation engine designed to determine a probability that a product is relevant to a user based on a user profile. In another embodiment, the probability that a product is relevant to a user may be determined based in part on a frequency of interactions with a product and a time of interaction with the products.

Abstract: A data object submitted for storage is analyzed, and a set of values is extracted from the data object that can correspond to a set of attributes. The analysis of the data object can also identify possible new ontology terms. One or more extracted values are presented to the entity which submitted the data object for approval and feedback. This feedback can be used to characterize the data object with appropriate terms, train the extraction process for future extractions, and/or expand the set of known ontology terms.

Abstract: Cross-lingual information retrieval is disclosed, comprising: translating a received query from a source natural language into a target natural language; performing a first information retrieval operation on a corpus of documents in the target natural language using the translated query to retrieve a set of pseudo-feedback documents in the target natural language; re-translating the received query from the source natural language into the target natural language using a translation model derived from the set of pseudo-feedback documents in the target natural language; and performing a second information retrieval operation on the corpus of documents in the target natural language using the re-translated query to retrieve an updated set of documents in the target natural language

Abstract: Compression of extensive, rule-based grammars used to facilitate search queries is provided herein. Rule-based grammars includes a list of rules that each comprise a sequence of token classes. Each token class is a logical grouping of tokens, and each token is a string of characters. A grammar is parsed to identify rules and token classes. Unimportant token classes are identified and sets of unimportant token classes are merged to generated merged token classes. A compressed grammar is generated by substituting the merged token classes into the grammar for corresponding unimportant token classes used to generate the merged token classes.

Abstract: Embodiments of the present invention relate to knowledge representation systems which include a knowledge base in which knowledge is represented in a structured, machine-readable format that encodes meaning.