知识表示(knowledge representation)是指把知识客体中的知识因子与知识关联起来,便于人们识别和理解知识。知识表示是知识组织的前提和基础,任何知识组织方法都是要建立在知识表示的基础上。知识表示有主观知识表示和客观知识表示两种。

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简介: 计算机视觉研究大多都集中在不重叠的目标对象上,然而,目标对象却不足以描述丰富的视觉知识,于是,研究者就通过语言特征来获取更多的信息。通过图片与文字叙述相结合的多模态信息融合来获取一个场景图谱。

场景要旨的吸引人的想法的困难在于,关于“要旨”的内容尚无共识。 场景中某些对象应至少是要点的一部分。必须将对象之间的某些关系编码为要点。 即使将所有物体都相同,所要表达的含义却不同。

图表示学习无处不在:

对具有独立对象和关系的特征进行学习,将获得一个场景图谱:

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Online behaviors of consumers and marketers generate massive marketing data, which ever more sophisticated models attempt to turn into insights and aid decisions by marketers. Yet, in making decisions human managers bring to bear marketing knowledge which reside outside of data and models. Thus, it behooves creation of an automated marketing knowledge base that can interact with data and models. Currently, marketing knowledge is dispersed in large corpora, but no definitive knowledge base for marketing exists. Out of the two broad aspects of marketing knowledge - representation and reasoning - this treatise focuses on the former. Specifically, we focus on creation of marketing knowledge graph from corpora, which requires identification of entities and relations. The relation identification task is particularly challenging in marketing, because of the non-factoid nature of much marketing knowledge, and the difficulty of forming rules that govern relations. Specifically, we define a set of relations to capture marketing knowledge, propose a pipeline for creating the knowledge graph from text and propose a rule-guided semi-supervised relation prediction algorithm to extract relations between marketing entities from sentences.

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