Semantic views — Interactive hierarchical exploration for patent landscaping

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. Word embeddings from a pre-trained model created from patent text are used to calculate pairwise similarities for representing 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.