International Business Machines Corporation

Adobe, Inc.

Sumit Bhatia

Balaji Krishnamurthy

Sameep Mehta

William Scott Spangler

Karthik Visweswariah

Bin He

Qi He

Milan Aggarwal

Debapriyo Majumdar

Vinith Misra

Rachit Bansal

Jivat Neet Kaur

Soma Shekar Naganna

L Venkata Subramaniam

Nikaash Puri

Karthik Sankaranarayanan

Manish Kesarwani

Jaydeep Sen

Hima Prasad Karanam

Anshu Narendra Jain

Shajith I Mohamed

Shettigar Parkala Srinivas

Balaji Ganesan

Ayushi Dalmia

Simra Shahid

Udit Sharma

Sumit Neelam

Daya Vivek

Tanay Anand

Avirup Saha

Nikitha Srikanth

Pradeep Teregowda

Heril Changwal

Sohan Patnaik

Content relevance based table query answering is described. In one or more examples, a query and a table are received. The table includes a plurality of cells. A plurality of scores for calculated that correspond to the plurality of cells based on the query. One or more machine-learning models are then leveraged to generate a search result from the query, table, and scores, which is presented in a user interface for display.

Content relevance based table query answering

A natural language processing approach for generating domain specific reasoning-based meaning representations. The approach may include receiving a user query via structured or unstructured data. The approach may also include generating a structured query from the user query using domain specific ontology and universal facts. Further, the approach many include the structured query may be analyzed to determine if the structured query has been assigned consistent concepts, properties and actions. Additionally, the approach may involve correcting the structured query if it is determined the structured query is inconsistent with the domain ontology and the universal facts.

Reasoning based natural language interpretation

Methods and systems are provided for facilitating generation and utilization of a commonsense contextualizing machine learning (ML) model, in accordance with embodiments described herein. In embodiments, a commonsense contextual ML model is trained by fine-tuning a pre-trained language model using a set of training path-sentence pairs. Each training path-sentence pair includes a commonsense path, identified via a commonsense knowledge graph, and a natural language sentence identified as contextually related to the commonsense path. The trained commonsense contextualizing ML model can then be used to generate a commonsense inference path for a text input. Such a commonsense inference path can include a sequence of entities and relations that provide commonsense context to the text input. Thereafter, the commonsense inference path can be provided to a natural language processing system for use in performing a natural language processing task.

Generating commonsense context for text using knowledge graphs

The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

Language model with external knowledge base

Some techniques described herein relate to generating a hierarchical topic model (HTM), which can be used to generate custom content. In one example, a method includes determining first-level topics in a topic hierarchy related to a corpus of documents. A first-level topic of the first-level topics includes multiple words. The multiple words are grouped into clusters based on word embeddings of the multiple words. The multiple words are then subdivided into second-level topics as subtopics of the first-level topic, such that the number of second-level topics equals the number of clusters. A document of the corpus of documents is assigned to the first-level topic and to a second-level topic of the second-level topics, and an indication is received of access by a user to the document. Custom content is generated for the user based on one or more other documents assigned to the first-level topic and the second-level topic.

Hierarchical topic model with an interpretable topic hierarchy

An embodiment establishes a designated attribute value as a semantic criterion for grouping records in a bucket, identifies a first set of records having attribute values that satisfy the semantic criterion, and adds the first set of records to the bucket. The embodiment detects that the first set of records represent a first series of events that occurred in succession at respective times. The embodiment derives a temporal attribute value representative of a time pattern formed by the times of the first series of events and designates the temporal attribute value as a temporal criterion for grouping records in the bucket. The embodiment identifies a second set of records that represent a second series of events and satisfy the temporal criterion and adds the second set of records to the bucket based at least in part on the second set of records satisfying the temporal criterion.

Bucketing records using temporal point processes

One embodiment provides a computer implemented method, including: receiving, from a user, a natural language query for data contained within at least one data repository; identifying at least one concept from the natural language query, wherein the at least one concept includes an entity and an intent; identifying a plurality of datasets satisfying the natural language query by querying the at least one data repository utilizing the at least one concept; ranking the dataset based on relevance to the query; generating an extract-transform-load script that extracts, transforms, and loads a dataset selected by the user from the plurality of datasets; and retrieving data included in the dataset utilizing the extract-transform-load script, wherein the retrieving includes returning the data to the user.

Extract-transform-load script generation

Methods, systems and computer program products for query processing are provided herein. A computer-implemented method includes receiving a first query from a user, determining whether the first query is capable of being answered using symbolic reasoning performed on data of a symbolic knowledge base, and executing the symbolic reasoning to generate a first query answer in response to a determination that the first query is capable of being answered using the symbolic reasoning. Axioms are extracted from a plurality of documents when it is determined that a second query is not capable of being answered using the symbolic reasoning. The method further includes determining whether the axioms are consistent with the symbolic knowledge base, and generating a second query answer based on the axioms in response to a determination that the one or more axioms are consistent with the symbolic knowledge base.

System and method for approximate reasoning using ontologies and unstructured data

A computer-implemented method includes determining first passages (FP) that provide evidence for one or more first relations (OOMFR) between first entities in a knowledge graph. The method includes generating an evidence index (EI) that associates the FP with the OOMFR and the first entities, receiving a query subsequent to generating the EI, and identifying, using the EI, the FP responsive to receiving the query. The method includes determining presentation aspects of the FP based on similarity information determined for the FP, and determining that second passages of the FP are substantially similar to at least one other passage of the FP. The method includes pruning the FP to generate a pruned set of passages (PSOP) that includes one or more third passages of the second passages and that does not include any other passages of the second passages. The method includes outputting the PSOP according to the presentation aspects.

Providing relevant evidence or mentions for a query

An approach is provided in which a system transforms a set of embedding approximation values corresponding to a set of knowledge graph nodes into a set of binary valued embedding vectors. The system evaluates the set of binary valued embedding vectors against a query and a selects one of the binary valued embedding vectors based on the evaluation. The system then identifies one of the knowledge graph nodes that corresponds to the selected binary valued embedding vector and in turn, provides a result to the query based on the identified knowledge graph node.

Neural bit embeddings for graphs

Methods and arrangements for providing interactive query suggestions. A partial user query phrase is received, the partial query phrase comprising a final word element. A vocabulary is accessed and, from the vocabulary, at least one possible completion of the final word element is ascertained. A phrase index is accessed and, from the phrase index, at least two phrases corresponding to completions of the partial user query phrase with the at least one possible completion of the final word element are ascertained. A probability score is assigned to each of the at least two phrases, and the at least two phrases are ranked based on probability.

Providing effective interactive query suggestions without query logs

Methods and arrangements for providing effective interactive query suggestions without query logs

Embodiments of the invention provide a system, method and computer program products for information retrieval from multiple documents by proximity searching for search queries. A method includes generating an index for the multiple documents, wherein the index includes words in snippets in the documents. An input search query is processed against the index by searching query terms over the snippets to introduce term proximity information implicitly in the information retrieval. Results of multiple sentence level search operations are combined as output.

Systems, methods and computer program products for fast and scalable proximal search for search queries

Embodiments of the invention provide a method and computer program products for information retrieval from multiple documents by proximity searching for search queries. A method includes generating an index for the multiple documents, wherein the index includes words in snippets in the documents. An input search query is processed against the index by searching query terms over the snippets to introduce term proximity information implicitly in the information retrieval. Results of multiple sentence level search operations are combined as output.

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New Delhi, India

Sumit Bhatia