Abstract: The disclosure herein pertains to a system and method for management of personalization content. The system and method divide the personalization information into offline personalization information and situational personalization information. Offline personalization information is independent of context and predetermined before a content request. A personalization model can dynamically allocate the selection between offline personalization information and situational personalization information.

Abstract: A method of generating ontologies for a Virtual Assistant across different languages may include extracting a plurality of tokens in a first language from a plurality of web resources in a web domain that includes the Virtual Assistant. The web resources may be made available in a first language and a second language. The method may also include determining a first part-of-speech (POS) for each of the plurality of tokens, where the first POS may be specific to the first language. The method may additionally include mapping the first POS to a second POS from a standardized set of POS's that are general across the first language and the second language, and generating a plurality of lemmas from the plurality of tokens. The method may further include displaying a network representing the ontology.

Abstract: Searches for content may be improved with unique reserved phrases. Reserved phrases may direct queries to specific preselected section of content. Synonyms may be generated and associated with a reserved phrase such that even when queries do not include reserved phrases, the synonyms may be used to map queries to the reserved phrases and the associated content. Synonyms may be generated and filtered such that a synonym is only associated with one reserved phrase thereby reducing search ambiguity.

Abstract: A method of automatically generating a lemma dictionary from a web resource may include extracting a plurality of tokens from text-based documents within the web resource, and generating a plurality of N-grams from the plurality of tokens. The method may additionally include receiving one or more filter definitions that identify valid N-grams, and filtering the plurality of N-grams using the one or more filter definitions to generate a lemma dictionary. The method may further include generating an ontology that comprises the lemma dictionary.

Abstract: Searches for content may be improved with unique reserved phrases. Reserved phrases may direct queries to specific preselected section of content. Synonyms may be generated and associated with a reserved phrase such that even when queries do not include reserved phrases, the synonyms may be used to map queries to the reserved phrases and the associated content. Synonyms may be generated and filtered such that a synonym is only associated with one reserved phrase thereby reducing search ambiguity.

Abstract: A method of generating ontologies for a Virtual Assistant across different languages may include extracting a plurality of tokens in a first language from a plurality of web resources in a web domain that includes the Virtual Assistant. The web resources may be made available in a first language and a second language. The method may also include determining a first part-of-speech (POS) for each of the plurality of tokens, where the first POS may be specific to the first language. The method may additionally include mapping the first POS to a second POS from a standardized set of POS's that are general across the first language and the second language, and generating a plurality of lemmas from the plurality of tokens. The method may further include displaying a network representing the ontology.

Abstract: A method of automatically generating a lemma dictionary from a web resource may include extracting a plurality of tokens from text-based documents within the web resource, and generating a plurality of N-grams from the plurality of tokens. The method may additionally include receiving one or more filter definitions that identify valid N-grams, and filtering the plurality of N-grams using the one or more filter definitions to generate a lemma dictionary. The method may further include generating an ontology that comprises the lemma dictionary.