International Business Machines Corporation

Nandana Sampath Mihindukulasooriya

Alfio Massimiliano Gliozzo

Gaetano Rossiello

Nicolas Rodolfo Fauceglia

Md Faisal Mahbub Chowdhury

Sarthak Dash

Ruchi Mahindru

Robert G Farrell

Yu Deng

Pavan Kapanipathi Bangalore

Salim E Roukos

Oktie Hassanzadeh

Sugato Bagchi

Michael Robert Glass

Dinesh Garg

Ibrahim Abdelaziz

Alexander Gray

Srinivas Ravishankar

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

A computer-implemented method according to one embodiment includes identifying a natural language query; translating the natural language query into an intermediate representation; converting the intermediate representation into one or more query triples; and performing relation linking between each of the one or more query triples and a plurality of knowledge base triples.

Implementing relation linking for knowledge bases

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process for generating the classification of files to allow for file system organization and/or query augmentation. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a generating component that generates a keyphrase based on a context derived from evaluation of an input file, wherein the generating component employs a public repository of files annotated with a plurality of keyphrases, including the keyphrase, to generate the keyphrase based on the context, and an execution component that classifies the input file based on the keyphrase. In one or more embodiments, the input file can comprise a query, and classification of the input file can comprise augmenting the query based on the keyphrase.

Keyphrase generation leveraging public repository categories

One or more computer processors parse a received natural language question into an abstract meaning representation (AMR) graph. The one or more computer processors enrich the AMR graph into an extended AMR graph. The one or more computer processors transform the extended AMR graph into a query graph utilizing a path-based approach, wherein the query graph is a directed edge-labeled graph. The one or more computer processors generate one or more answers to the natural language question through one or more queries created utilizing the query graph.

Knowledge base question answering

Systems and techniques that facilitate spurious relationship filtration from external knowledge graphs based on distributional semantics of an input corpus are provided. In one or more embodiments, a context component can generate a context-based word embedding of one or more first terms in a document collection. The embedding can yield vector representations of the one or more first terms. The one or more first terms can correspond to knowledge terms in one or more first nodes of a knowledge graph. In one or more embodiments, a filtering component can filter out a relationship between the one or more first nodes and a second node of the knowledge graph based on a similarity value being less than a threshold. The similarity value can be a function of the vector representations of the one or more first terms. In various embodiments, cosine similarity can be used to compute the similarity value.

Filtering spurious knowledge graph relationships between labeled entities

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 method, system, and computer program product for automated evaluation of information retrieval systems are provided. The method accesses a natural language query from a set of natural language queries. The natural language query is associated with a query difficulty level. The method generates one or more natural language responses to the natural language natural language query. Each natural language response is associated with at least one facet of the plurality of facets. The method generates a set of feedback cues. A set of search results for the natural language query are returned. The set of search results include a highest ranked natural language response of the one or more natural language responses. The method generates an evaluation result for the HCIR system for the query difficulty level based on the one or more natural language responses, the set of search results, and the set of feedback cues.

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Somerville, MA, United States of America

Nandana Sampath Mihindukulasooriya