Abstract: A method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata are encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.

Abstract: A method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata are encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.

Abstract: Method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata is encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.

Abstract: Method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata is encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.

Abstract: Aspects relate to a method for selecting a transplantation donor or selecting a mismatched allele group of a transplantation donor, wherein two-field specific HLA protein typing is unavailable for said donor(s) and/or recipient(s). Other aspects include computer-software and systems for carrying out said method.

Abstract: Embodiments relate to a method, apparatus and program product and for generating a natural language processing model for an information domain. The method derives a skeleton of a natural language lexicon from a source model and uses it to form a dictionary. It also applies a set of syntactical rules defining concepts and relationships to the dictionary and expands the skeleton of the natural language lexicon based on a plurality of reference documents from the information domain. Using the expanded skeleton of the natural language lexicon, it also provides a natural language processing model for the information domain.

Abstract: A method and apparatus for identifying the focus of a document, in a natural language processing application, the natural language processing application comprising a hierarchical concept tree having a plurality of nodes, each node being associated with a term, the method comprising the steps of: mapping an input document to nodes in a concept tree to determine a number of occurrences of a term in the input document which also occur at a node in the concept tree; weighting each node in the concept tree, depending on the determined number of occurrences of the term in the input document and a determined value assigned to each node in the concept tree; traversing the concept tree to identify a heaviest weighted path, in dependence on the weighting of each node in the concept tree; and determining the focus of the input document by identifying a node having the heaviest weight along the most heavily-weighted path.

Abstract: A method and system for language identification are provided. The system includes a feature set of a plurality of character strings of varying length with associated information. The associated information includes one or more significance scores for a character string for one or more of a plurality of languages. Means are provided for detecting character strings from the feature set within a token from an input text. The system uses a finite-state device and the associated information is provided as glosses at the final nodes of the finite-state device for each character string. The associated information can also include significance scores based on linguistic rules.

Abstract: A method and apparatus for identifying the focus of a document, in a natural language processing application, the natural language processing application comprising a hierarchical concept tree having a plurality of nodes, each node being associated with a term, the method comprising the steps of: mapping an input document to nodes in a concept tree to determine a number of occurrences of a term in the input document which also occur at a node in the concept tree; weighting each node in the concept tree, depending on the determined number of occurrences of the term in the input document and a determined value assigned to each node in the concept tree; traversing the concept tree to identify a heaviest weighted path, in dependence on the weighting of each node in the concept tree; and determining the focus of the input document by identifying a node having the heaviest weight along the most heavily-weighted path.

Abstract: A method and system for language identification are provided. The system includes a feature set of a plurality of character strings of varying length with associated information. The associated information includes one or more significance scores for a character string for one or more of a plurality of languages. Means are provided for detecting character strings from the feature set within a token from an input text. The system uses a finite-state device and the associated information is provided as glosses at the final nodes of the finite-state device for each character string. The associated information can also include significance scores based on linguistic rules.