Patent categorization, which is to assign multiple International Patent Classification (IPC) codes to a patent document, relies heavily on expert efforts, as it requires substantial domain knowledge. When formulated as a multi-label text classification (MTC) problem, it draws two challenges to existing models: one is to learn effective document representations from text content; the other is to model the cross-section behavior of label set. In this work, we propose a label attention model based on graph convolutional network. It jointly learns the document-word associations and word-word co-occurrences to generate rich semantic embeddings of documents. It employs a non-local attention mechanism to learn label representations in the same space of document representations for multi-label classification. On a large CIRCA patent database, we evaluate the performance of our model and as many as seven competitive baselines. We find that our model outperforms all those prior state of the art by a large margin and achieves high performance on P@k and nDCG@k.

In recent decades, materials science literature and patents have grown exponentially. This has also contributed to an ever-growing challenge whether the literature is current, as there can be a gap between when the patent was filed and when it was approved. Moreover, it is difficult to ensure that a patent cites the appropriate prior art due to variety and volume of materials science data, especially when it is in two separate sources that have different curation mechanisms and purpose - publications and patents. The existing relational database schema, generally used to store publications, also presents challenges given the strict tabular schema, which may not be appropriate for organizing and querying highly interconnected information about materials in these publications and patents. For example, elements are chemically combined to form a compound, which can then be converted to other compounds via chemical reactions. Furthermore, relational database is not designed for handling combining data from multiple sources and with various formats, thus it makes discover relevance between publications and patents become difficult. In order to explore an alternative approach to represent materials data and combine data from multiple sources into the same repository, in this work, we propose a solution to integrate data from Open Quantum Materials Database (OQMD) and patent data from USPTO1 database into a network and named it heterogeneous materials information network (HMIN). We generalize prior work which based on using meta path-based topological features to explore the network, and we propose features to identify network noise and investigate relatedness between different-typed objects to meet our application needs. We built several machine learning models by using these features to explore relevance between materials science publications and patents. Experiment results show that HMIN can help researchers effectively discover related publications and patents originally kept in different sources. Our work exhibits to materials community a new way of appro-priately representing materials data and discovering connections between data from multiple sources.