Abstract: Disclosed are methods in which ontologies are automatically (i.e., with little or no human interaction) constructed from data. The constructed ontology may be provided for use by an application or device. An ontology may be constructed as follows. Firstly, a lexical graph is constructed. Secondly, a concept-detection process is performed on the lexical graph to detect concepts (prominent terms) in the lexical graph. Thirdly, a clustering method is performed on the lexical graph to form groups of concepts. Fourthly, relationships between the concepts are determined based on the interconnections of the concepts in the groups. Lastly, the concepts and detected relationships therebetween are structured using a formal ontology specification.

Abstract: Disclosed are methods in which ontologies are automatically (i.e., with little or no human interaction) constructed from data. The constructed ontology may be provided for use by an application or device. An ontology may be constructed as follows. Firstly, a lexical graph is constructed. Secondly, a concept-detection process is performed on the lexical graph to detect concepts (prominent terms) in the lexical graph. Thirdly, a clustering method is performed on the lexical graph to form groups of concepts. Fourthly, relationships between the concepts are determined based on the interconnections of the concepts in the groups. Lastly, the concepts and detected relationships therebetween are structured using a formal ontology specification.

Abstract: A content device may select associated content, such as adverts, for a user selected content item based on textual characterizing data for the associated content and the user selected content item. A term set characterizing the user selected content item is expanded using semantic graphs and similarity values between the expanded term set and term sets describing associated content is calculated. A specific associated content item is then selected based on the similarity values. The semantic graph based term set expansion may allow improved accuracy in selecting appropriate associated content while providing a process that is suitable for resource constrained scenarios. In particular, communication resource, memory resource, and computational resource usage may be kept low.

Abstract: A content device may select associated content, such as adverts, for a user selected content item based on textual characterizing data for the associated content and the user selected content item. A term set characterizing the user selected content item is expanded using semantic graphs and similarity values between the expanded term set and term sets describing associated content is calculated. A specific associated content item is then selected based on the similarity values. The semantic graph based term set expansion may allow improved accuracy in selecting appropriate associated content while providing a process that is suitable for resource constrained scenarios. In particular, communication resource, memory resource, and computational resource usage may be kept low.

Abstract: A method of generating recommendations for content items comprises providing a domain ontology where concepts are characterized by a term vector with terms and associated weights. Associated term sets, each of which comprises a set of terms that characterize a content item, are further provided. A concept set is generated for each associated term set by determining the concepts of the domain ontology that match the terms of the associated term set. In addition, a user profile for a user is provided where the user profile comprises at least some of the concepts of the ontology coupled with preference weights. Recommendations for content items are generated based on the plurality of associated concept sets and the user profile. The invention may allow improved and/or facilitated generation of recommendations from text based characterizing data.