Semantic views — Interactive hierarchical exploration for patent landscaping

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