Abstract: Methods, systems, and computer program products for composing applications on a mobile device are provided herein. A method includes exposing multiple capabilities from a set of multiple applications installed on an operating system of a user device to a configuration module executing on the operating system of the user device; defining one or more rules associated with using each of the multiple exposed capabilities; and invoking a combination of two or more of the multiple exposed capabilities, based on said one or more defined rules, to execute a user-defined task, wherein said invoking is executed by a super application executing on the operating system of the user device.

Abstract: Methods, systems, and computer program products for generating probabilistic annotations for entities and relations using reasoning and corpus-level evidence are provided herein. A method includes determining candidate entities and candidate relationships between entities from text contained within textual documents associated with a given domain; generating questions to support or refute legitimacy of a given one of the candidate entities and/or a given one of the candidate relationships; automatically determining an answer to each of the questions based on analysis of one or more additional items of knowledge pertaining to the given domain; computing a confidence score for each answer; and assigning a confidence value in support of the legitimacy of the given one of the candidate entities and/or the given one of the candidate relationships, wherein said confidence value is based on an aggregation of the confidence scores for the answers to the one or more questions.

Abstract: A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the first confidence score meets a pre-defined threshold.

Abstract: An embodiment of the invention provides a technique for permitting a machine to discover classes and topics that data contains and to annotate data objects with those identified classes. The technique enables machines to group and annotate data objects in ways that are meaningful and intuitive for a user of the data objects. An interactive method uses clustering, along with feedback from a user on the clustering output, to discover a set of classes. The feedback from the user is used to guide the clustering process in the later stages, which results in better and better discovery of classes and annotations with more and more human feedback. A method can be used to produce labeled data that involves discovering classes and annotating a given dataset with the discovered class labels. This is advantageous for building a classifier that has wide applications, such as call routing and intent discovery.

Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.

Abstract: Methods, systems, and computer program products for composing applications on a mobile device are provided herein. A method includes exposing multiple capabilities from a set of multiple applications installed on an operating system of a user device to a configuration module executing on the operating system of the user device; defining one or more rules associated with using each of the multiple exposed capabilities; and invoking a combination of two or more of the multiple exposed capabilities, based on said one or more defined rules, to execute a user-defined task, wherein said invoking is executed by a super application executing on the operating system of the user device.

Abstract: A method and system for evaluating cross-domain clusterability upon a target domain and a source domain. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by the target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability.

Abstract: An approach is provided for creating a new document. Keywords specifying a subject matter of the new document are received. Metadata of documents is determined to match keyword(s) included in the received keywords and the documents are retrieved. Based on a section being created in the new document, a ranked list of the retrieved documents is generated. A selection of a document included in the ranked list is received. The selected document is added to the new document. The keywords are refined based on the added document. Based on the subject matter and the refined keywords, the new document is completed by repeating the steps of determining the metadata, retrieving the documents, generating the ranked list, receiving the selection, and adding the selected document.

Abstract: An approach is provided for creating a new document. Keywords specifying a subject matter of the new document are received. Metadata of documents is determined to match keyword(s) included in the received keywords and the documents are retrieved. Based on a section being created in the new document, a ranked list of the retrieved documents is generated. A selection of a document included in the ranked list is received. The selected document is added to the new document. The new document is determined to be not complete. The keywords are refined based on the added document. Based on the subject matter and the refined keywords, the new document is completed by repeating the steps of determining the metadata, retrieving the documents, generating the ranked list, receiving the selection, and adding the selected document.

Abstract: Techniques for detecting one or more documents that are duplicate or near-duplicate of a first document are provided. The techniques include obtaining a first document, obtaining one or more additional documents, retrieving a set of one or more document signatures for each document, and detecting one or more documents that are duplicate or near-duplicate of the first document by detecting each of the one or more additional documents that have at least a minimum number of signatures in common with the first document, wherein detecting each of the one or more additional documents that have at least a minimum number of signatures in common with the first document comprises dynamically using at least one of a user-configurable similarity definition and a user-configurable similarity threshold value.

Abstract: A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities.

Abstract: A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities.

Abstract: Methods and arrangements for enhancing content in discussion forums. Access to an online discussion is provided. A posting by an author participating in the discussion is accepted, and a recommendation is automatically produced for the author for amending the posting to increase the likelihood of response to the posting by other individuals participating in the discussion.

Abstract: Methods and arrangements for enhancing content in discussion forums. Access to an online discussion is provided. A posting by an author participating in the discussion is accepted, and a recommendation is automatically produced for the author for amending the posting to increase the likelihood of response to the posting by other individuals participating in the discussion.

Abstract: A system and method are described for generating semantically similar sentences for a statistical language model. A semantic class generator determines for each word in an input utterance a set of corresponding semantically similar words. A sentence generator computes a set of candidate sentences each containing at most one member from each set of semantically similar words. A sentence verifier grammatically tests each candidate sentence to determine a set of grammatically correct sentences semantically similar to the input utterance. Also note that the generated semantically similar sentences are not restricted to be selected from an existing sentence database.

Abstract: An approach is provided for creating a new document. Keywords specifying a subject matter of the new document are received. Metadata of documents is determined to match keyword(s) included in the received keywords and the documents are retrieved. Based on a section being created in the new document, a ranked list of the retrieved documents is generated. A selection of a document included in the ranked list is received. The selected document is added to the new document. The new document is determined to be not complete. The keywords are refined based on the added document. Based on the subject matter and the refined keywords, the new document is completed by repeating the steps of determining the metadata, retrieving the documents, generating the ranked list, receiving the selection, and adding the selected document.

Abstract: Methods, systems, and computer program products for identifying one or more utterances that are likely to carry the intent of a speaker are provided herein. A method includes providing a transcript of utterances to a word weight scoring module to perform inverse document frequency based scoring on each word in the transcript, thereby generating a weight for each word; calculating a weight for each utterance in the transcript to generate weighted utterances by summing the weights or each constituent word in each utterance; comparing at least one weighted utterance to pre-existing to example utterances carrying the intent of a speaker to determine a relevancy score for the at least one weighted utterance; and generating a ranked order of the at least one weighted utterance from highest to lowest intent relevancy score, wherein the highest intent relevancy score corresponds to the utterance which is most likely to carry intent of the speaker.

Abstract: Techniques, an apparatus and an article of manufacture identifying one or more utterances that are likely to carry the intent of a speaker, from a conversation between two or more parties. A method includes obtaining an input of a set of utterances in chronological order from a conversation between two or more parties, computing an intent confidence value of each utterance by summing intent confidence scores from each of the constituent words of the utterance, wherein intent confidence scores capture each word's influence on the subsequent utterances in the conversation based on (i) the uniqueness of the word in the conversation and (ii) the number of times the word subsequently occurs in the conversation, and generating a ranked order of the utterances from highest to lowest intent confidence value, wherein the highest intent value corresponds to the utterance which is most likely to carry intent of the speaker.

Abstract: Techniques, systems, and articles of manufacture for utilizing dependency among internet search results. A method includes associating a user search query with a search task, identifying multiple information documents that correspond to the search task, and generating a recommended sequence of the multiple information documents to present to the user in response to the user search query, wherein the recommended sequence is based on dependency information associated with the multiple information documents.

Abstract: A plurality of segments in an e-mail collection by parsing content of e-mails is generated. Corresponding segment signature for each segment is created and a signature index is populated using the generated segment signatures. After receiving a query e-mail, a plurality of query segments in the query e-mail is generated using content of the query e-mail and corresponding query segment signature for each query segment is generated. A query root segment is identified and corresponding query root segment signature is generated. A set of root segment signatures of the signature index is identified and the query root segment signature is compared with each root segment signature from the signature index. A subset of the signature index is identified, using a match between the root segment signature and the query root segment signature. An e-mail thread hierarchy is built using the identified subset of the signature index.