Semantic views — Interactive hierarchical exploration for patent landscaping

Abstract

In this paper, we investigate whether a semantic representation of patent documents provides added value for a multi-dimensional visual exploration of a patent landscape compared to traditional approaches that use tf–idf (term frequency–inverse document frequency). Word embeddings from a pre-trained word2vec model created from patent text are used to calculate pairwise similarities in order to represent each document in the semantic space. Then, a hierarchical clustering method is applied to create several semantic aggregation levels for a collection of patent documents. For visual exploration, we have seamlessly integrated multiple interaction metaphors that combine semantics and additional metadata for improving hierarchical exploration of large document collections.