International Business Machines Corporation

Mohammad Sadoghi Hamedani

Bishwaranjan Bhattacharjee

Mustafa Canim

Kenneth Andrew Ross

Oktie Hassanzadeh

Adam J Storm

Vijayshankar Raman

Guy Maring Lohman

Ronald Jason Barber

Richard Sefton Sidle

Chandrasekaran Mohan

Xun Xue

Meinolf Sellmann

Robert G Farrell

Yuan-Chi Chang

Achille Belly Fokoue-Nkoutche

Timothy Ray Malkemus

Mariano Rodriguez Muro

Alfio Massimiliano Gliozzo

Ping Zhang

Ippokratis Pandis

Hans-Arno Jacobsen

Bassem Makni

Yuan Chi Chang

Nicolas R Fauceglia

Thien H Nguyen

Oliver Lehmberg

Tilmann Rabl

Amos Cahan

Yishai Abraham Feldman

Padmanabhan Santhanam

Ibrahim Abdelaziz

Ehab Abdelhamid Mohammed Abdelhamid

Fabian Nagel

Gonzalo Ignacio Diaz Caceres

A method includes storing an anchor row vector identification for an anchor row to a local memory. It is determined whether the anchor row vector identification is visible based on isolation requirements. The anchor row vector identification is accessed upon a determination that the anchor row vector identification is visible, and the row vector identification is re-read from the local memory. It is determined whether the anchor row vector identification has not changed since a start of the accessing. Upon a determination that the anchor row vector identification has not changed, read anchor row fields are returned. A first check history is performed on an anchor row history tuple sequence number (TSN) for the anchor row.

In-place updates with concurrent reads in a decomposed state

A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Joint learning of local and global features for entity linking via neural networks

Entities and relations associated with source code of a program are extracted. An entity completion on the extracted entities and relationships is performed to produce a knowledge graph of the source code. Repeated patterns of relationships are identified from the knowledge graph across the source code.

Extracting a knowledge graph from program source code

A method, system, and recording medium for knowledge graph augmentation using data based on a statistical analysis of attributes in the data, including a ranking device configured to rank semantically similar input data elements to create a ranked list of attributes to augment an input of structured data and populate with a data string corresponding to the instances, where the ranking device further combines a set of filters to refine the ranked list of attributes, the set of filters including a first filter according to column ranges of columns, a second filter according to a column uniqueness of the columns, a third filter according to a type of data in a column of the columns, and a fourth filter according to a distribution of values in the columns.

Knowledge graph augmentation through schema extension

A semantic embedding model using geometrical set-centric approach to capture both ABox and TBox representational models is disclosed. The model transforms a semantic-rich knowledge graph into a set of overlapping, disjoint, and/or subsumed n-dimensional spheres that captures and represents semantics embedded in the knowledge graph.

Set-centric semantic embedding

Techniques are provided for pre-filtering of join execution over multi-column range summaries and other synopses. An exemplary method comprises maintaining a synopsis for a plurality of data tables, wherein a given synopsis summarizes a set of records in a corresponding data table; and, in response to a request for a join operation for a set of the data tables: joining the synopses associated with the set of data tables to generate a joined synopsis; for joined records in the joined synopsis, obtaining corresponding records from the set of data tables as candidate records; and joining the candidate records. Two or more of the set of data tables can be distributed across a plurality of nodes and the synopses can be replicated and/or broadcasted across the plurality of nodes. Incremental updates to broadcasted and/or replicated synopses are optionally provided to at least one node.

Pre-filtering of join execution over multi-column range summaries and other synopses

A knowledge graph is traversed by receiving a knowledge graph at a deep neural network, the knowledge graph including a plurality of nodes connected by a plurality of edges, each respective edge of the plurality of edges being associated with a corresponding distance representing embedded semantic information. The deep neural network is trained to capture the embedded semantic information. A path query is received at the deep neural network. A context is determined for the received path query at the deep neural network. The deep neural network performs the traversing of the knowledge graph in response to the received path query, based upon the determined context and the embedded semantic information.

Determining context-aware distances using deep neural networks

A computer-implemented method of analyzing text documents, includes identifying a relationship in a text document associated with an entity, building a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error, and determining whether to store the identified relationship in memory, based on the prediction error.

Analyzing text documents

Methods, systems, and computer program products for expressive temporal predictions over semantically-driven time windows are provided herein. A computer-implemented method includes identifying, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determining, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantifying a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and computing a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples.

Expressive temporal predictions over semantically driven time windows

Embodiments include method, systems and computer program products for predicting adverse drug events on a computational system. Aspects include receiving a personalized data set including a plurality of real-time drug doses for a first drug or drug combination and a plurality of corresponding real-time adverse drug reaction tolerance data for the first drug or drug combination for a patient. Aspects also include receiving known drug data for a candidate drug or drug pair. Aspects also include calculating, based upon the known drug data and the personalized data set, a predicted adverse drug reaction tolerance for the candidate drug or drug pair at a candidate dosage, wherein the predicted adverse drug reaction tolerance is personalized to the patient.

Personalized tolerance prediction of adverse drug events

Methods and systems for performing database transactions include executing a first transaction request in a preplay mode that locks the requested data with a prefetch-lock and reads one or more requested data items from storage into a main memory buffer; locking the requested data items with a read/write lock after said data items are read into the main memory buffer; and performing the requested transaction on the data items in the main memory buffer using a processor.

Preplaying transactions that mix hot and cold data

A method includes logically organizing, by an object hierarchy processor, data objects in a first hierarchy. A portion of the data objects in the first hierarchy logically includes groupings of other data objects. The object hierarchy processor physically organizes the data objects across two or more types of memory in a second hierarchy. Another portion of the data objects in the second hierarchy physically includes groupings of other data objects. Groupings of the data objects in the second hierarchy are dynamically moved across the two or more types of memory. Levels of access of the data objects are tracked using a data structure that maps groupings of the data objects in the first hierarchy onto metadata information including combined access frequencies of the data objects, and current number of accessors to the data objects, in each grouping of the data objects.

Efficient performance of insert and point query operations in a column store

Methods, systems, and computer program products for linkage prediction through similarity analysis are provided herein. A computer-implemented method includes extracting multiple features from (i) one or more attributes of a set of source nodes within a knowledge graph and (ii) one or more attributes of a set of target nodes within the knowledge graph, wherein at least one extracted feature satisfies a designated complexity level; performing a similarity analysis across the at least one extracted feature by applying one or more similarity measures to the at least one extracted feature; predicting one or more sets of links between the source nodes and the target nodes based on the similarity analysis, wherein one or more sets of predicted links satisfy a pre-determined accuracy threshold; and outputting the one or more sets of predicted links to a user.

Linkage prediction through similarity analysis

Methods, systems, and computer program products for iterative and targeted feature selection are provided herein. A computer-implemented method includes generating a first prediction value for a variable attribute of a set of objects by executing a predictive model that comprises a set of features for the set of objects; evaluating the prediction error of the predictive model based on said first prediction value; generating additional features upon a determination that the prediction error exceeds a threshold; incorporating the additional features into the predictive model, generating an updated predictive model; generating a second prediction value for the variable attribute by executing the updated predictive model; evaluating the prediction error of the updated predictive model based on said second prediction value; and outputting the second prediction value to a user upon a determination that the prediction error of the updated predictive model is below the threshold.

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Chappaqua, NY, United States of America

Mohammad Sadoghi Hamedani