International Business Machines Corporation

University College Cork

Lea Deleris

Yassine Lassoued

Radu Marinescu

Elizabeth Daly

Yufang Hou

Killian Levacher

Charles Arthur Jochim

Stephane Deparis

Francesca Bonin

Peter Nicholas Wilson

Julien Monteil

Martin Stephenson

Debasis Ganguly

Abdul Razak

Razak Abdul

Embodiments for relationship graph expansion and extraction from a collection of unstructured text data by a processor. A query relating to one or more concepts may be received. The query may be expanded according to a logical reasoning operation and a domain ontology having a set of logical rules. A relationship graph between one or more concepts from a plurality of unstructured text data may be extracted based on an expanded query according to a domain ontology and the set of logical rules.

Logic-based relationship graph expansion and extraction

Embodiments for co-reference resolution and entity linking from unstructured text data by a processor. Semantic co-references and mentions of one or more entities may be resolved occurring in unstructured text data by linking the one or more entities using a domain knowledge ontology.

Co-reference resolution and entity linking

Embodiments for discovery and analysis of interpersonal relationships from a collection of unstructured text data by a processor. A relationship between one or more entities and extracted text data from a plurality of unstructured text data may be identified such that the relationship includes a sentiment of the relationship, a type of relationship, temporal information, or a combination thereof. The one or more entities may be associated with a knowledge graph based on an ontology of concepts representing a domain knowledge. The extracted information and the identified relationship may be automatically aggregated into a multi-graph representation.

Discovery, characterization, and analysis of interpersonal relationships extracted from unstructured text data

Embodiments for extraction and summarization of decision discussions of a communication by a processor. The decision elements may be grouped together according to similar characteristics. The decision elements may be linked, and sentiments of the discussion participants towards each of the decision elements may be analyzed. A summary of the plurality of the decision elements may be provided via an interactive graphical user interface (GUI) on one or more Internet of Things (IoT) devices. The summary of the decision elements may be linked to domain knowledge. The summary may be enhanced using a domain knowledge.

Extraction and summarization of decision elements from communications

Embodiments for semantic hierarchical grouping of short text fragments by a processor. Sub-terms are extracted from a plurality of input text fragments according to a lexical sub-term hierarchy. Each of the sub-terms in the lexical sub-term hierarchy are matched with concepts based on an ontology of concepts representing a domain knowledge. The input text fragments are automatically grouped into a hierarchy of concepts based on the matching and a semantical relationship between each concept and matching sub-term.

Semantic hierarchical grouping of text fragments

There is provided a method, a system and a computer program product for supporting a decision making process. The system receives a decision model from a decision maker, the decision model used for determining a solution to a decision problem based on attributes and uncertainties of the decision problem. The decision problem includes information about a plurality of outcome vectors that represent all possible outcomes and the uncertainties associated with the decision problem. The system determines whether the received decision model can be solved without receiving any preference information from the decision maker. The system receives partially specified preference information from the decision maker if the received decision model cannot be solved without any preference information. The system solves the decision model with the partially specified preference information. The system recommends, based on the solution, one or more decisions to the decision maker.

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Paris, France

Lea Deleris