Abstract: A method for executing multiple chemical experiments in parallel may be provided. The method comprises receiving a list of actions to be performed for synthesizing a chemical product. Thereby, the actions correspond to at least two chemical partial reactions and the list comprises a delimiter symbol separating two chemical partial reactions, determining identical chemical partial reactions, and building a reaction commonality tree (RCT) of the chemical reactions. Furthermore, the method comprises executing a plurality of the identical chemical partial reactions independent of a sequence of chemical partial reactions of the reaction commonality tree only once. Each of the identical chemical partial reactions is executed in a different chemical reactor and each resulting intermediate product has a quantity of the sum of the related identical chemical partial reactions.

Abstract: A computer-implemented method for determining a set of target items to be annotated for training a machine learning application. The method comprises providing a training data set with a set of data samples and an auto-encoder with a classifier. The auto-encoder comprises an embedding model that maps the set of data samples to a set of compressed feature vectors. The set of compressed feature vectors define a compressed feature matrix. Further provided are: a definition of a graph associated to the compressed feature matrix, applying a clustering-algorithm to identify node clusters of the graph and applying a centrality algorithm to identify central nodes of the node clusters, retrieving from an annotator node labels for the central nodes, propagating the annotated node labels to other nodes of the graph and performing a training of the embedding model and the classifier with the annotated and the propagated node labels.

Abstract: A computer-implemented method for processing a digital image. The digital image comprises one or more text cells, wherein each of the one or more text cells comprises a string and a bounding box. The method comprises receiving the digital image in a first format, the first format providing access to the strings and the bounding boxes of the one more text cells. The methods further comprises encoding the strings of the one or more text cells as visual pattern according to a predefined string encoding scheme and providing the digital image in a second format. The second format comprises the visual pattern of the strings of the one or more text cells. A corresponding system and a related computer program product is provided.

Abstract: A method, system and computer program product to generate a training data set for image segmentation applications, comprising providing a set of input documents of a first format. The input documents each comprise one or more pages. The input documents are split into individual document pages and parsed. Parsing comprises identifying a predefined set of items including position information of the position of the predefined set of items in the individual document pages; generating a bitmap image of a second format for each individual document page of the first format. The bitmap image comprises a predefined number of pixels. A mask is generated for each individual document. The mask comprises the predefined number of pixels of the corresponding bitmap image. Generating the mask comprises assigning an encoded class label to each pixel of the mask based on the position information of identified items of the predefined set of items.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for performing a multiplication of a matrix with an input vector. The method includes one or more processors subdividing a matrix into logical segments, the matrix being given in a sparse-matrix data format. The method further includes one or more processors obtaining one or more test vectors. The method further includes one or more processors performing an optimization cycle. In an additional aspect, performing the optimization cycle further comprises, for each of the test vectors, one or more processors, performing a cache performance test.

Abstract: The invention is notably directed to a computer-implemented method for processing a plurality of documents. The method comprises providing the plurality of documents in a first format and splitting each of the plurality of documents of the first format into one or more individual pages. The method further comprises individually parsing the one or more individual pages of the plurality of documents. The parsing comprises identifying a predefined set of items of the one or more individual pages. Further processing comprises gathering the predefined set of items of each of the one or more individual pages of the plurality of documents into individual page files of a second format and performing the document processing service with the individual page files of the second format. The invention further concerns a corresponding computing system and a related computer program product.

Abstract: A method for building a new knowledge graph may be provided. The method comprises providing an existing knowledge graph, sampling random walks through the existing knowledge graph, determining embedding vectors for vertices and edges of the sampled random walks, and training of a machine-learning model taking as input sequences of the embedding vectors of the random walks. Furthermore, the method comprise receiving a set of documents determining sequences of terms from phrases from the documents the documents, building sequences of embedding vectors from the determined sequences of terms from the phrases, and using the built sequences of embedding vectors from the determined sequences of terms from the phrases as input for the trained machine-learning model for predicting second sequences of terms. Finally, the method comprises merging the predicted second sequences of terms thereby building the new knowledge graph.

Abstract: In an approach for automatic knowledge graph construction, a processor receives a text document and trains a first machine-learning system to predict entities in the text document. Thereby, the text document with labeled entities is used as training data. A processor trains a second machine-learning system to predict relationship data between the entities, wherein, as training data, entities and edges of an existing knowledge graph and determined embedding vectors of the entities and edges are used. A processor receives a set of second text documents, determines second embedding vectors therefrom, and predicts entities and edges; thereby using the set of second text documents, the determined second embedding vectors, and the predicted entities and associated embedding vectors of the predicted entities as input for the first and second trained machine-learning model. A processor builds triplets of the entities and the edges representing a new knowledge graph.

Abstract: A computer-implemented method for generating an organic synthesis procedure from a simplified molecular-input line-entry system (SMILES) string may be provided. The method includes receiving a plurality of SMILES strings describing a desired chemical product and required reactants, and predicting procedure steps for an organic synthesis procedure for producing the desired chemical product by a deep machine-learning model system trained with sets of SMILES strings describing respective desired chemical products, reactants and related procedure steps as training data. The sets can be extracted from a corpus of associated chemical documents, and the predicted procedure steps are human readable. The method includes further receiving a modification signal for a modification to the predicting procedure steps, storing the plurality of received SMILES strings, the predicted procedure steps and the modification of the predicting procedure steps.

Abstract: A method for executing multiple chemical experiments in parallel may be provided. The method comprises receiving a list of actions to be performed for synthesizing a chemical product. Thereby, the actions correspond to at least two chemical partial reactions and the list comprises a delimiter symbol separating two chemical partial reactions, determining identical chemical partial reactions, and building a reaction commonality tree (RCT) of the chemical reactions. Furthermore, the method comprises executing a plurality of the identical chemical partial reactions independent of a sequence of chemical partial reactions of the reaction commonality tree only once. Each of the identical chemical partial reactions is executed in a different chemical reactor and each resulting intermediate product has a quantity of the sum of the related identical chemical partial reactions.

Abstract: A method for executing multiple chemical programs in parallel in an array of chemical reactors using a single array of substance containers may be provided. The method includes receiving a plurality of chemical programs, building a plurality of records comprising each a chemical program. Thereby, each record includes a key and a data field, wherein the key is indicative of the reactants required for the respective chemical reaction, and wherein the data field includes the chemical program. The method further includes creating an ordered data structure of the data records based on the keys, selecting a next record from the ordered data structure, assigning the selected next record to selected ones of the array of chemical reactors, repeating the steps of selecting and assigning until, as a maximum, each chemical reactor has a defined record assigned to it, and executing the chemical programs according to their defined records in parallel.

Abstract: In an approach for a dynamic in-memory construction of a knowledge graph structure, the knowledge graph structure comprising a plurality of nodes and edges linking selected nodes to each other, a processor receives a record comprising a plurality of strings. The plurality of strings relates to a command combined with a set of strings. A processor determines content records relating to nodes relating to each of the strings. A processor assigns node identifiers for respective determined content records. A processor appends the node identifiers to a dynamic in-memory knowledge graph structure. A processor modifies an edge between selected ones of the node identifiers based on the command combined with the set of strings. A processor builds the dynamic in-memory knowledge graph structure.

Abstract: A computer-implemented method for processing a digital image. The digital image comprises one or more text cells, wherein each of the one or more text cells comprises a string and a bounding box. The method comprises receiving the digital image in a first format, the first format providing access to the strings and the bounding boxes of the one more text cells. The methods further comprises encoding the strings of the one or more text cells as visual pattern according to a predefined string encoding scheme and providing the digital image in a second format. The second format comprises the visual pattern of the strings of the one or more text cells. A corresponding system and a related computer program product is provided.

Abstract: The invention is notably directed to a computer-implemented method for processing a plurality of documents. The method comprises providing the plurality of documents in a first format and splitting each of the plurality of documents of the first format into one or more individual pages. The method further comprises individually parsing the one or more individual pages of the plurality of documents. The parsing comprises identifying a predefined set of items of the one or more individual pages. Further processing comprises gathering the predefined set of items of each of the one or more individual pages of the plurality of documents into individual page files of a second format and performing the document processing service with the individual page files of the second format. The invention further concerns a corresponding computing system and a related computer program product.

Abstract: A computer-implemented method for determining a set of target items to be annotated for training a machine learning application. The method comprises providing a training data set with a set of data samples and an auto-encoder with a classifier. The auto-encoder comprises an embedding model that maps the set of data samples to a set of compressed feature vectors. The set of compressed feature vectors define a compressed feature matrix. Further provided are: a definition of a graph associated to the compressed feature matrix, applying a clustering-algorithm to identify node clusters of the graph and applying a centrality algorithm to identify central nodes of the node clusters, retrieving from an annotator node labels for the central nodes, propagating the annotated node labels to other nodes of the graph and performing a training of the embedding model and the classifier with the annotated and the propagated node labels.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for performing a multiplication of a matrix with an input vector. The method includes one or more processors subdividing a matrix into logical segments, the matrix being given in a sparse-matrix data format. The method further includes one or more processors obtaining one or more test vectors. The method further includes one or more processors performing an optimization cycle. In an additional aspect, performing the optimization cycle further comprises, for each of the test vectors, one or more processors, performing a cache performance test.

Abstract: A method, system and computer program product to generate a training data set for image segmentation applications, comprising providing a set of input documents of a first format. The input documents each comprise one or more pages. The input documents are split into individual document pages and parsed. Parsing comprises identifying a predefined set of items including position information of the position of the predefined set of items in the individual document pages; generating a bitmap image of a second format for each individual document page of the first format. The bitmap image comprises a predefined number of pixels. A mask is generated for each individual document. The mask comprises the predefined number of pixels of the corresponding bitmap image. Generating the mask comprises assigning an encoded class label to each pixel of the mask based on the position information of identified items of the predefined set of items.

Abstract: A computer-implemented method for generating ground-truth for natural language querying may include providing a knowledge graph as data model, receiving a natural language query from a user and translating the natural language query into a formal data query. The method can also include visualizing the formal data query to the user and receiving a feedback response from the user. The feedback response can include a verified and/or edited formal data query. The method can also include storing the natural language query and the corresponding feedback response as ground-truth pair. Corresponding system and a related computer program product may be provided.

Abstract: A plurality of electronic documents comprising one or more document pages are received. First position markers, second position markers and page identifiers are inserted to the pages. The plurality of electronic documents are printed, thereby generating a printed corpus comprising a plurality of printed documents. The plurality of printed documents are scanned, thereby generating a scanned corpus comprising a plurality of scanned images. Scanning frame positions of the first and the second position markers are detected and the detected scanning frame positions and the page positions are used to define affine transformations between the plurality of scanned images and the corresponding document pages. The affine transformations are applied to the plurality of scanned images to align the plurality of scanned images with the corresponding document pages of the plurality of electronic documents.

Abstract: A computer-implemented method for digitizing a document, wherein the document has assigned a classification scheme may be provided. A digital image and an identifier of the classification scheme may be received, the image representing a portion of the document. A segmentation of the image may be determined into one or more image segments; for each of the image segments, content information may be captured from the image segment and a category may be assigned to the image segment, the category being selected from the classification scheme. One or more digitization segments may be selected from the segmentation. A graph model of the document may be populated, wherein each of the digitization segments is represented by a segment node of the graph model.