Abstract: A method, system and computer program product are disclosed for recommending terms in a document based on a specified interaction with the document. In one embodiment, the method comprises displaying a document on a device; detecting a specified interaction with the document displayed on the device; identifying text associated with the specified interaction with the document displayed on the device; performing object recognition with the identified text to recognize one or more defined interested objects; and recommending terms in the document based on the recognized defined interested objects. In an embodiment, the object recognition includes identifying one or more objects from the text, and performing the object recognition with these objects to recognize the one or more interested objects. In an embodiment, one or more terms are extracted from the text, and these terms are partitioned to identify the one or more objects from the text.

Abstract: A method, system and computer program product are disclosed for recommending terms in a document based on a specified interaction with the document. In one embodiment, the method comprises displaying a document on a device; detecting a specified interaction with the document displayed on the device; identifying text associated with the specified interaction with the document displayed on the device; performing object recognition with the identified text to recognize one or more defined interested objects; and recommending terms in the document based on the recognized defined interested objects. In an embodiment, the object recognition includes identifying one or more objects from the text, and performing the object recognition with these objects to recognize the one or more interested objects. In an embodiment, one or more terms are extracted from the text, and these terms are partitioned to identify the one or more objects from the text.

Abstract: A method, a system and a computer program product for searching a knowledge base and finding top-k meaningful paths for different concept pairs input by a user in linked open data utilizing the degree of association between concepts as the weight of the two concepts in a knowledge graph and to find the top-k shortest path as meaningful paths. A large corpus is used to train the association of different concept pairs. A deep learning based framework is used to learn a concept vector to represent the concept and the cosine similarity of the concept vector and an input concept vector indicating the degree of association of the vectors as the weight of these two concepts in the knowledge graph. The top-k meaningful paths are determined based on the weights and the shortest paths are provided for use by users as the meaningful paths.