Abstract: The input means 181 accepts inputs of test data, a hierarchical structure in which a node of bottom layer represents a target class, and a classification score of a seen class as the classification score indicating a probability that the test data is classified into each class. The unseen class score calculation means 182 calculates the classification score of an unseen class based on uniformity of the classification score of each seen class. The matching score calculation means 183 calculates a matching score indicating similarity between the test data and each class label. The final classification score calculation means 184 calculates a final classification score indicating a probability that the test data is classified into the class so that the larger the classification score of each class, and the matching score, the larger the final classification score.

Abstract: A column meaning candidate selection means 303 selects a candidate for meaning of a column whose meaning is to be inferred. A column similarity computation means 304 computes, for each candidate for meaning selected by the column meaning candidate selection means 303, a score indicating a similarity between the selected candidate for meaning and meaning of each column other than the column whose meaning is to be inferred contained in a table. A column meaning identification means 305 identifies meaning of the column whose meaning is to be inferred from the candidates for meaning of the column with use of the score computed by the column similarity computation means 304.

Abstract: An information processing apparatus (1) includes: a data acquiring section (11) for acquiring target data, which is data to be classified into one of a plurality of categories; and a classifying section (12) for classifying the target data into one of the plurality of categories in accordance with a similarity between target relevant information relevant to the target data and category relevant information relevant to each of the plurality of categories, so that data is automatically classified without use of a classifier constructed by machine learning.

Abstract: In order to make it possible to correctly merge dissimilarly expressed character strings, an information processing apparatus (1) includes: a data acquisition section (11) that acquires a data set including a plurality of character string pairs in each of which whether or not character strings therein indicate the same object is known; and a conversion pattern decision section (12) that decides, based on results of trials to convert each of the plurality of character string pairs included in the data set, a conversion pattern that heightens accuracy in determining whether or not the character string pair included in the data set indicates the same object.

Abstract: In order to automatically classify data without using a classifier constructed by machine learning, an information processing apparatus (1) includes: a data acquiring section (11) for acquiring target data, which is data to be classified into one of a plurality of categories in a hierarchical structure; and a classifying section (12) for classifying the target data into one of the plurality of categories in accordance with (i) a matching degree indicating a degree to which the target data matches that category and (ii) an upper-level matching degree indicating a degree to which the target data matches an upper-level category of that category.

Abstract: It is possible to detect an insight between a plurality of data sets. An information processing apparatus (1) includes: a classification unit (11) that groups, by insight to be detected, a plurality of insight subjects each being data generated from each of a plurality of data sets by associating a plurality of data items contained in each of the plurality of data sets with each other; and an evaluation unit (12) that calculates, for a combination of the plurality of insight subjects which have been grouped, an evaluation value for determining the presence or absence of an insight.

Abstract: An information processing device acquires target data, and converts the target data to an embedded vector indicating a latent feature quantity of the target data. Also, the information processing device searches for data similar to the latent feature quantity of the target data as candidate data, and applies a predetermined process to the candidate data to assign a rank to be combined with the target data, for each of the candidate data. Then, the information processing device outputs a result obtained by combining the target data and the candidate data based on the assigned rank.

Abstract: A subgraph extraction means 71 extracts, from a document structure graph indicating a document structure, a subgraph as a part of the document structure graph on the basis of inter-word relationship information indicating a relationship between a word and a word. A rule creation means 72 creates a rule for extracting a subgraph having the same structure as the subgraph from the document structure graph. A knowledge addition means 73 extracts a subgraph from the document structure graph in accordance with the rule and adds the information indicated by the subgraph to the inter-word relationship information.

Abstract: A coupling table search device 530 includes: input means 531 that receives an input of a coupling index from a coupling index storage unit 520; search plan generation means 532 that calculates, for each coupling index, the number of results that is the number of couplable record pairs estimated in a case where a record in a target column is found using the coupling index for each similarity function, and generates a search plan that defines a use order of the coupling index in descending order of the calculated number of results; and record search means 533 that searches for a record in the target column by using the coupling index in the order defined in the search plan, and specifies the external table in which records whose number is equal to or larger than a predetermined threshold have been found as a coupling table.

Abstract: Input means 611 receives an input of a size-specific correlation index. Index specification means 612 specifies the size-specific correlation index to be used for searching among size-specific correlation indexes based on a predetermined threshold of the number of record pairs determined to be couplable when coupling a base table including a target column and an external table. Candidate column search means 613 searches for a column whose distance to the target column is equal to or less than a distance threshold from the external table by using the specified size-specific correlation index, calculates a correlation value indicating the degree of correlation between the found column and the target column, and searches for a column whose correlation value is equal to or larger than a correlation value threshold as a candidate column.

Abstract: Input means 731 receives an input of a coupling table, a candidate column, and a base table. Coupling means 732 couples a column corresponding to the candidate column in the coupling table to the base table. Feature selection means 733 selects a feature that improves performance of a task based on data included in the coupled candidate column. Integrated table output means 734 outputs an integrated table obtained by coupling a column including the selected feature and the base table.

Abstract: The data linkage system 90 includes a learning device 70 which generates a score function, which is a function that scores proximity between a user with an action history in a first domain and a user with an action history in a second domain, and an estimation device 80 which estimates a user matched in the first domain and the second domain. The score calculation means 82 calculates a score for a pair of users who have an action history in a first domain and an action history in a second domain using a score function. The matching means 83 estimates the same user as the user in the first domain from among the users in the second domain based on magnitude of the calculated score.

Abstract: The conversion rule storage means 75 stores multiple conversion rules for converting one or more meanings of data to other one or more meanings of data. The analysis means 76 derives, based on one or more given meanings of data and the conversion rules, information indicating what kind of analysis can be performed using the data with the meaning.

Abstract: The acquiring unit which acquires for each item name, one or more words composing an item name from the item name belonging to a group including a plurality of item names, respectively. The computing unit which computes for each item name, relevance that is a degree to which the acquired word is related to the item name, respectively. The determination unit which determines words among the acquired words as candidates for a classification name of each item represented by the plurality of item names. The sum over the plurality of item names of the computed relevance of the determined word is up to the top Mth (M is a natural number).

Abstract: The group determination means 13 determines a group of item names from a set of item names of unclassified items. The display control means 14 displays individual item names belonging to the group, and displays a plurality of candidates of a classification name for each item represented by each item name belonging to the group in a user-specifiable manner. When one of the plurality of candidates of the classification name is specified by a user, the classification determination means 16 determines that each item represented by each item name belonging to the group is classified by the classification name specified by the user.

Abstract: A data index unit 80 manages data of source tables. A data searching unit 90 searches the joinable source tables for the query table. A group indexing unit 81 divides each the source table into a collection of sets, and generates, for each divided set, a group of similar cells included in the set using a similarity function for calculating cell similarity. A data filtering unit 91 searches the source table including a set in which the number of pairs of a first cell included in a divided query table set and a second cell in a group including cells similar to the first cell exceeds a predetermined threshold.

Abstract: An information processing apparatus (1) includes a storage unit (11) that stores knowledge information (111) containing a relationship regarding the correspondence between a plurality of types of element information (1111 to 111n) to be used for referring to a specified element value and conceptual information (1110) indicating a concept of the element value, an adding unit (12) that adds, to each of a plurality of graphs representing a processing structure in each of a plurality of source codes where any one of the element information (1111 to 111n) is described, the conceptual information (1110) identified from the element information corresponding to each node in the graph based on the knowledge information (111) as attribute information related to the node, and an extraction unit (13) that extracts a subgraph common to the graphs after the adding based on the conceptual information (1110).

Abstract: The acquisition unit 52A acquires task request information S1, which is request information regarding a utilization of data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53A determines, on a basis of the task request information S1, whether or not there is a task that can be performed by use of the data. The notification unit 54A notifies the owner terminal 2 of recommended task information S2 that is information regarding the above-described task when the determination unit 53A determines that there is the above-described task.

Abstract: The acquisition unit 52A acquires request information S1, which is information regarding possession data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53A determines whether or not there is shortage data other than the possession data required to perform a designation task that is designated. The notification unit 54A notifies, when the determination unit 53A determines that there is the shortage data, the owner terminal 2 of shortage data information S2 that is information regarding shortage information.

Abstract: The acquisition unit 52B acquires sales request information S1, which is request information regarding a sale of data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53B determines whether or not there is a user who has a past record of performing a task or who is expected to perform the task, the task being subjected to a positive effect when the data is used for the task. The notification unit 54B notifies, in a case where the determination unit 53B determines that there is a user who has a past record of performing the task or who is expected to perform the task, the owner terminal 2 of candidate buyer information S2 indicating information regarding the user.