International Business Machines Corporation

Nternational Business Machines Corporation

Sarthak Dash

Alfio Massimiliano Gliozzo

Md Faisal Mahbub Chowdhury

Michael Robert Glass

Nandana Sampath Mihindukulasooriya

Mustafa Canim

Sugato Bagchi

Gaetano Rossiello

Vittorio R Castelli

Ruchi Mahindru

Robert G Farrell

Yu Deng

Oktie Hassanzadeh

Radu Florian

Avirup Sil

Nicolas Rodolfo Fauceglia

Md Faisal M Chowdhury

Bassem Makni

Chao Shang

An embodiment for encoding permutation-invariant representations of linearized tabular data. The embodiment may receive input including tabular data and linearize a column or row within the received tabular data. The embodiment may automatically assign an increasing sequence of position identifiers to each non-delimiting tokenized cell in the linearized column or row until a header delimiter is reached. The embodiment may, in response to reaching the header delimiter, automatically assign a monotonically increasing sequence of position identifiers for each non-delimiting tokenized cell positioned after the header delimiter, restarting from an integer corresponding to 1 greater than the position identifier assigned to the header delimiter for each non-delimiting tokenized cell positioned after cell delimiters. The embodiment may automatically assign a static position identifier for each of the cell delimiters in the linearized column or row and output an encoded permutation-invariant representation of the linearized column or row.

Permutation invariance for representing linearized tabular data

An embodiment for improved linking of tabular columns to column types in an ontology unseen during training. The embodiment may for a target table, encode a target tabular query column, table headers, and target types independently to generate permutation invariant representations of tabular data associated with the target table. The embodiment may, for each of the target types, extract and further encode auxiliary information. The embodiment may process the encoded tabular data to obtain a first vector and a second vector. The embodiment may concatenate the first vector and the second vector to generate a final query vector. The embodiment may process the encoded target types through a third transformer to obtain a third vector. The embodiment may calculate a score to model interactions between the target tabular query column of the target table and the target types.

Linking tabular columns to unseen ontologies

A system comprises a memory that stores computer-executable components; and a processor, operably coupled to the memory, that executes the computer-executable components. The system includes a receiving component that receives a corpus of data; a relation extraction component that generates noisy knowledge graphs from the corpus; and a training component that acquires global representations of entities and relation by training from output of the relation extraction component.

Deep symbolic validation of information extraction systems

One embodiment of the present invention provides a method comprising receiving a text corpus, and generating a first list of triples based on the text corpus. Each triple of the first list comprises a first term representing a candidate hyponym, a second term representing a candidate hypernym, and a frequency value indicative of a number of times a hypernymy relation is observed between the candidate hyponym and the candidate hypernym in the text corpus. The method further comprises training a neural network for hypernym induction based on the first list. The trained neural network is a strict partial order network (SPON) model.

Hypernym detection using strict partial order networks

Techniques regarding autonomous classification and/or identification of various types of noise comprised within a knowledge graph are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a knowledge extraction component, operatively coupled to the processor, that can classify a type of noise comprised within a knowledge graph. The type of noise can be generated by an information extraction process.

Noise detection in knowledge graphs

One embodiment of the invention provides a method for terminology ranking for use in natural language processing. The method comprises receiving a list of terms extracted from a corpus, where the list comprises a ranking of the terms based on frequencies of the terms across the corpus. The method further comprises accessing a domain ontology associated with the corpus, and re-ranking the list based on the domain ontology. The resulting re-ranked list comprises a different ranking of the terms based on relevance of the terms using knowledge from the domain ontology. The method further comprises generating clusters of terms via a trained model adapted to the corpus, and boosting a rank of at least one term of the re-ranked list based on the clusters to increase a relevance of the at least one term using knowledge from the trained model.

Discovering ranked domain relevant terms using knowledge

A computer-implemented method according to one embodiment includes converting an input question into a vector form using trained word embeddings; constructing a type similarity matrix using a predetermined ontology; and determining a score for all possible types for the input question, based on the input question in vector form and the type similarity matrix.

Performing fine-grained question type classification

Techniques regarding similarity based negative sample analysis are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a similarity component that can determine similarity metrics for respective entities based on a vector space model. The respective entities can be represented by a dataset. Also, the computer executable components can comprise a sampling component that can perform a negative sampling analysis on the dataset based on the similarity metrics.

Similarity based negative sampling analysis

A system, computer program product, and method are provided for employing a graph neural network (GNN) to construct a taxonomy. The GNN is subject to a training cycle and an inference cycle. The training cycle encodes cross-domain terms pairs from a set of noisy cross domain pairs extracted from a corpora, and outputs a preliminary taxonomy. The inference cycle identifies candidate term pairs and selectively subjects the candidate term pairs to selective filtering to produce a system predicted taxonomy from the preliminary taxonomy.

Taxonomy construction via graph-based cross-domain knowledge transfer

A method, system and computer-usable medium are disclosed for reducing labeled data imbalances when training an active learning system. The ratio of instances having positive labels or negative labels in a collection of labeled instances associated with an input category used for learning is determined. A first instance for annotation is selected from a collection of unlabeled instances if a first threshold for negative instances, and a first threshold confidence level of being a positive instance of the input category, have been met. A second instance for annotation is selected if a second threshold for positive instances, and a second threshold confidence level of being a negative instance of the input category, have been met. The first and second instances are respectively annotated with a positive and negative label and added to the collection of labeled instances, which are then used for training.

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Jersey City, NJ, United States of America

Sarthak Dash