Nec Corporation

Bhushan Kotnis

Carolin Lawrence

Mathias Niepert

Kiril Gashteovski

A method of encoding a query graph having m root nodes and n leaf nodes includes receiving m×n path queries representing the query graph, where each path query starts with a root node and ends with a leaf node, and where the query graph includes one or more missing nodes or edges between nodes. Positions of each node and edge within each path query are encoded independently, where the encoded positions include a positional order, and positional counter values that are reset at a start position of each path query. The missing nodes or edges are masked to produce a masked query that is fed to a transformer encoder. The method can be used for, but is not limited to, medical uses, such as for predicting protein-drug interactions or medical conditions, as well as other applications, to support decision making or optimizing processes or predictions.

Answering complex queries in knowledge graphs with bidirectional sequence encoders usable to support decision making and having medical use applications

A memory for storing data for access by an application program includes an encoded sequential query representing m×n path queries of a query graph having elements including m root nodes and n leaf nodes, and a missing element, wherein m and n are integer values. Each of the path queries includes a root node as a start position and a leaf node as an end position, wherein positional encodings of the elements include a positional order within each path query, wherein the positional encodings of the elements include counter values that are reset at a start position of each of the path queries, and wherein the missing element is masked. The memory can be used for, but is not limited to, medical uses, such as for predicting protein-drug interactions or medical conditions, as well as other applications, to support decision making or optimizing processes or predictions.

A method for encoding a query graph into a sequence representation includes receiving m×n path queries representing a query graph having m root nodes and n leaf nodes, each path query beginning with a root node and ending with a leaf node, and encoding positions of each node and each edge between nodes within each path query independently, wherein the encoded positions include a positional order within each path query. The positional encodings may include no positional ordering between different path queries. The query graph may include one or more missing entities and each missing entity may be masked to produce a masked sequential query, which may be fed to a transformer encoder. The invention can be used for, but is not limited to, medical uses, such as for predicting protein-drug interactions or medical conditions, as well as other applications, and significantly outperforms the optimized model.

Answering complex queries in knowledge graphs with bidirectional sequence encoders useable to support decision making and having medical use applications

Method for encoding a query graph into a sequence representation includes receiving a set of m&#xd7;n path queries representing a query graph, the query graph having a plurality of nodes including m root nodes and n leaf nodes, wherein m and n are integer values, each of the m&#xd7;n path queries beginning with a root node and ending with a leaf node, and encoding positions of each node and each edge between nodes within each of the m&#xd7;n path queries independently, wherein the encoded positions include a positional order within each path query. The positional encodings may include no positional ordering between different path queries. The query graph may include one or more missing entities and each token representing one of the one or more missing entities may be masked to produce a masked sequential query, which may be fed to a transformer encoder.

Answering complex queries in knowledge graphs with bidirectional sequence encoders

A method is provided for extracting machine readable data structures from unstructured, low-resource language input text. The method includes obtaining a corpus of high-resource language data structures, filtering the corpus of high-resource language data structures to obtain a filtered corpus of high-resource language data structures, obtaining entity types for each entity of each filtered high-resource language data structure, performing type substitution for each obtained entity by replacing each entity with an entity of the same type to generate type substituted data structures, and replacing each entity with an equivalent a corresponding low-resource language data structure entity to generate code switched sentences. The method further includes generating an augmented data structure corpus, training a multi-head self-attention transformer model, and providing the unstructured low-resource language input text to the trained model to extract the machine readable data structures.

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Heidelberg, Germany

Bhushan Kotnis