Abstract: Provided is a knowledge extracting apparatus for extracting knowledge information related to a knowledge-extraction target from an electronic document distributed continually in a state where the electronic document is not associated with the knowledge-information extraction target. A knowledge extracting apparatus according to one embodiment is a knowledge extracting apparatus including: an information receiving section for receiving an electronic document; a knowledge extracting section for extracting a concept from the electronic document based on a target word to extract knowledge information and a clue word to extract knowledge information and forming knowledge information in which the concept thus extracted and the target word are associated with each other; a storage section for storing the knowledge information thus extracted; and an information analysis section for, after the knowledge information is stored, analyzing the electronic document based on the knowledge information in the storage section.

Abstract: A pattern extracting apparatus includes a first storing section storing plural target information pieces, a candidate pattern producing section producing candidate patterns each including two or more items different from each other based on each of the items included in the plural target information pieces, a candidate evaluation value calculating section calculating an extraction evaluation value of the candidate pattern based on a frequency of appearance at which the produced candidate pattern appears in the plural target information pieces, a pattern extracting section determining and extracting any of the candidate patterns having the calculated extraction evaluation value satisfying a predetermined threshold value, and a second storing section storing an association degree between the items.

Abstract: An evaluation target of interest extraction apparatus of one embodiment extracts a plurality of items for each piece of text information. When the items include an item which matches an evaluation target expression or related expression, the apparatus allocates the evaluation target expression or that of the related expression to the text information as an evaluation target item. The apparatus distinguishes a class from a change in numeric information related to the evaluation target item, and allocates the class to the text information. The apparatus generates a class-attached transaction including the evaluation target item, the items, and the class. The apparatus discovers a pattern from a set of the transactions.

Abstract: A thread ranking system includes: a thread collector for collecting a thread from a bulletin board website, a thread information storage for storing the collected thread, an event extractor for extracting the event from respective threads by using the event knowledge stored in the event knowledge storage, a related expression extractor for extracting a related expression characterizing the thread from expressions contained in the thread every thread, an input/output controller for presenting information that calls upon the user to input an evaluation of a predetermined thread to the user and inputting an evaluation of the user, a related expression importance calculator for calculating importance based on the input evaluation of the user every related expression, and a thread ranking unit 8 for deciding ranks of respective threads based on the extracted event and the calculated importance every related expression.

Abstract: A data search apparatus includes an obtaining unit that obtains a content and a first metadata corresponding to the content and including at least a search key indicating an object of the content; a feature amount computing unit that computes a feature amount indicating a feature of the content from the obtained content; a learning-data storing unit that stores a learning-data that correspondingly includes the first metadata corresponding to each of the obtained content and the computed feature amount; a learning-data reconstructing unit that reconstructs the learning-data by generating a second metadata from the first metadata included in the learning-data stored in the learning-data storing unit so that the second metadata includes all search keys in the first metadata of all the learning-data, and by replacing the first metadata by the second metadata in learning-data; and a model generating unit that generates a model from the learning-data, the model being a coefficient matrix indicating a relation betwe

Abstract: Constraining sequential data expressing sequential data which a sequential pattern to be extracted must include is specified in advance. Sequential pattern candidates with sequence length 1 are initially determined from among a plurality of input sequential data. Next, a set of sequential pattern candidates is generated by determining a plurality of new sequential pattern candidates by elongating the sequence length of the sequential pattern candidates. In this sequential pattern candidate set, after sequential pattern candidates which can generate only a sequential pattern which does not include constraining sequential data are eliminated, sequential data which include constraining data, and frequently appear are extracted as a new sequential pattern.

Abstract: According to one embodiment, the Web pages that match a user's designated collection condition are collected from a plurality of Web sites. The collected Web pages are divided into a plurality of clusters, based on URL information of the Web pages. A date expression is extracted from Web pages included in each of the clusters. A typical date expression form is determined for each of the clusters, based on the extracted date expression. The Web pages included in each of the clusters are divided into a plurality of items, based on the date expression form. The items are sorted for each of the clusters in order of time, based on date expressions corresponding to the items. Time-series data is generated for each of the clusters by sorting the items.

Abstract: A text data storage unit stores a plurality of text data having attribute data and time data. A dictionary storage unit stores a plurality of events each associated with text data. An analysis condition indication unit indicates an analysis target as attribute data and an analysis condition as an event sequence. A time series data generation unit assigns an event to each of the plurality of text data by referring to the dictionary storage unit, extracts a group of text data each having the same attribute data as the analysis target from the plurality of text data, and generates time series data each representing the event assigned to the text data of the group in order of the time data of the text data. A time series data analysis unit analyzes the time series data each having the same event sequence as the analysis condition.

Abstract: By using event extracting knowledge, a plurality of events are extracted from a text. One of the events is extracted as a targeting event, and also one or more of the events other than the targeting event are extracted as targeted events, so that one or more combinations of the targeting event and the one or more targeted events are generated. A distance between a first text and a second text from which one or more of the combinations have been generated is calculated, so that a certainty factor of each of the combinations is calculated. One of the combinations is selected based on the certainty factors, so that the selected combination is displayed.

Abstract: A time series pattern generation unit extracts a key concept corresponding to a text of each data element of a plurality of time series data by referring to an extraction rule memory, and generates a plurality of time series patterns. A colony generation unit unifies a plurality of data elements having the same key concept and substantially the same time duration, and generates colonies of unified elements. In response to a selection of a first colony, a colony extraction unit specifies a second colony having a key concept contrary to the first colony, and extracts colonies earlier than the first colony and the second colony. A time reason extraction unit extracts a time reason as a divergence condition of the first colony from the second colony from the extracted colonies.

Abstract: A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Abstract: Constraining sequential data expressing sequential data which a sequential pattern to be extracted must include is specified in advance. Sequential pattern candidates with sequence length 1 are initially determined from among a plurality of input sequential data. Next, a set of sequential pattern candidates is generated by determining a plurality of new sequential pattern candidates by elongating the sequence length of the sequential pattern candidates. In this sequential pattern candidate set, after sequential pattern candidates which can generate only a sequential pattern which does not include constraining sequential data are eliminated, sequential data which include constraining data, and frequently appear are extracted as a new sequential pattern.

Abstract: A time-series pattern finding apparatus, includes decomposing unit decomposing a condition, which indicates a condition of time-series relationship between first elements each defined as including first events each including an attribute and an attribute value of the attribute, into a partial condition, determining unit determining whether the partial condition holds for each potential events, each potential characteristic event sets, and each potential time-series patterns, calculating unit calculating frequency of occurrence in the time-series data items only for fourth events in the potential events, first sets in the potential characteristic event sets, and first patterns in the potential time-series patterns, which satisfy the partial condition, and extracting unit extracting a potential event, a potential event set and a potential time-series pattern from the fourth events, the first sets and the first patterns, respectively, based on the frequency of occurrence not less than a threshold value, as a tim

Abstract: A thread ranking system includes: a thread collector for collecting a thread from a bulletin board website, a thread information storage for storing the collected thread, an event extractor for extracting the event from respective threads by using the event knowledge stored in the event knowledge storage, a related expression extractor for extracting a related expression characterizing the thread from expressions contained in the thread every thread, an input/output controller for presenting information that calls upon the user to input an evaluation of a predetermined thread to the user and inputting an evaluation of the user, a related expression importance calculator for calculating importance based on the input evaluation of the user every related expression, and a thread ranking unit 8 for deciding ranks of respective threads based on the extracted event and the calculated importance every related expression.

Abstract: A sequential pattern detecting apparatus includes a first combining unit configured to combine a plurality of characteristic event sets detected from sequential data containing elements which comprise a plurality of events and which are arranged in sequential order, to generate a characteristic primary sequential pattern with a sequence size of “1”, a second combining unit configured to combine a plurality of characteristic ith-length (i=1, 2, . . . ) sequential patterns with a sequence size of “i” to generate a candidate (i+1)th-length sequential pattern, a checking unit configured to check validity of the candidate (i+1)th-length sequential pattern on the basis of the attributes to detect valid (i+1)th-length sequential patterns, and a detecting unit configured to detect a characteristic (i+1)th-length sequential pattern from the valid (i+1)th-length sequential patterns with reference to the sequential data.

Abstract: Thread-ranking apparatus includes unit collecting threads from a bulletin-board site, the thread each including a set of identifiers, articles each related to book-information items, and the book-information items, unit detecting, for each article, whether a reference part that refers to a part of a posted article of the articles is included, unit extracting the reference part, unit computing a first-article-importance degree based on number of reference parts, unit setting the first-article-importance degrees as book-information-importance degrees, unit acquiring an additional thread from the bulletin-board site, unit setting, as a second-article-importance degree, a book-information-importance degree corresponding to book information of each of the additional articles and an identifier, unit setting, as a thread-importance degree, a sum of the second-article-importance degrees to thread-importance degrees, unit rearranging the thread-importance degrees in a descending order, and unit storing, in relation to

Abstract: A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Abstract: A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Abstract: A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Abstract: A classification model learning apparatus for learning a classification model for extracting a particular event from a text includes an evaluation unit for evaluating the existence or nonexistence of the particular event for a plurality of learning texts having both a text and information on the existence or nonexistence of the particular event by applying an event related expression for evaluating the existence or nonexistence of the particular event to each learning text of the plurality of learning texts, an extracting unit for extracting a learning text in accordance with the existence or nonexistence of the particular event evaluated by the evaluation unit, and a learning unit for learning a classification model based on the learning text extracted by the extracting unit.