A computer-implemented method, computer program product, and a technical system for generating triples including providing a plurality of log entries from respective log files, wherein each log entry of the plurality of log entries includes at least one text message, generating at least one template based on the plurality of log entries using unsupervised clustering, wherein the at least one template includes at least one variable part and at least one fixed part, assigning each log entry of the plurality of log entries to one respective template based on the generated at least one template using a similarity measure, extracting the corresponding at least one variable and at least one fixed part of each text message of the plurality of text messages as key/value pairs using the respective assigned at least one template based on the plurality of log entries, and providing the text messages, keys and values as triples.

A method and an apparatus for constructing an object relationship network and an electronic device are provided by the present disclosure, relating to the field of artificial intelligence technologies, such as deep neural networks, deep learning, etc. A specific implementation solution is: extracting keywords in respective text contents corresponding to a plurality of objects to obtain keywords corresponding to respective objects; and according to the keywords corresponding to the objects, a similarity between the plurality of objects is determined; and then according to the similarity between the plurality of objects, an object relationship network between the plurality of objects is constructed. Since the object relationship network constructed by means of the similarity between the plurality of objects can accurately describe a closeness degree of a relationship between the objects, thus, the plurality of objects can be managed effectively by means of the constructed object relationship network.

A method trains a neural network to identify an event based on discrepancies in answers to factoid questions at different times. One or more processors identify answers to a series of factoid questions. The processor(s) compare the answers from the series of factoid questions in order to determine discrepancies in the answers at different times, and then train a neural network to identify an event based on the discrepancies in the answers at the different times.

A system allows users to perform analysis of objects processed by systems, for example, requests, traces, logs, and so on. The system allows users to use an object tagging language to categorize objects. Tagging rules specified using the object tagging language are executed to tag the objects processed. The system created a tagging metadata index based on the tagged objects. The tagging metadata index allows efficient execution of queries used for analyzing the objects. The system may be used for analyzing execution of systems, for example, to compare execution of replicas of a system to determine whether there are differences in the execution of different replicas.

Provided herein are systems, methods, and computer readable media for document hierarchy permissions. This may include providing a permission database comprising a plurality of users, a plurality of advisors, and a plurality of categories. A candidate document is received at a network device. A candidate user, a candidate routing action, and a candidate document category are identified from the candidate document. A candidate document permission attribute is generated identifying the candidate user, candidate file location, and the candidate document category. The candidate document is stored, and the candidate document permission attribute corresponding to the candidate document is stored.

A POI popularity derivation device (10) includes: a dictionary generation unit (11) that assigns a feature word used as a co-occurrence word of a POI name to each popularity-assigned POI name serving as a popularity assignment target to generate a popularity-assigned POI dictionary in which a popularity-assigned POI name and a feature word are associated with each other; an extraction unit (12) that extracts posted data serving as a search target from posted data on the basis of predetermined criteria; and a popularity derivation unit (18) that searches for the posted data on the basis of a predetermined rule regarding feature words while referring to the popularity-assigned POI dictionary, to extract posted data linked to the popularity-assigned POI name, and derives the popularity of each popularity-assigned POI name on the basis of the number of pieces of extracted posted data for each popularity-assigned POI name.

A computer extracts from contact records that each include a contact identifier, a group identifier for each group with which the contact has had an interaction, and interaction information that indicates a number of interactions and a timing of a most recent interaction. The contact data records are processed to generate a contact profile record for each contact including group metric values and a corresponding value for each group metric value based on an interaction history of groups the contact has interacted with. An interaction analytics databases stores a set of contact profile records and group profile records for groups that include metric values associated with the group and an interaction history. They are processed with at least thousands of the contact profile records to determine group-contact compatibility factors. A compatibility parameter is generated and communicated for each of at least thousands of contacts based on the group-contact compatibility parameters.

Systems and methods are described for training an artificial intelligence model to infer a log sourcetype of a log. For example, logs may have different log sourcetypes, and logs having the same log sourcetypes may have different messagetypes. The artificial intelligence model may be a machine learning model, and can be trained using training data that includes logs with known log sourcetypes. Each log can be tokenized, filtered, converted into a vector, and applied to a machine learning model as an input to perform the training. The machine learning model may output an inferred log sourcetype, which can be compared with the known log sourcetype to update model parameters to improve the machine learning model accuracy. The trained machine learning model may be trained to infer a log sourcetype of a log regardless of the messagetype of the log.

Dynamic categorization of documents from a semi-static classification taxonomy through the use of key terms, concepts, and entities. Dynamic categorization is a method for retrieving documents that are relevant to a specific category, which can be defined at the time the documents are needed. This is in contrast to a priori sorting and tagging (identifying) documents as to what categories they belong. The categories can be defined not just as a set of key words but may also include phrases, entities and/or relationships found in the document(s), complex field queries, weighted queries against words, as well as exclusion conditions.

An information processing apparatus includes a processor. The processor receives an input of a graph structure. The graph structure has nodes including text and edge. The processor assigns the nodes to one or more clusters. The processor partitions the text into words. The processor classifies the words into 1) a word representing a subject or target of an operation, 2) a word representing a content or state of the operation, and 3) other words. The processor extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the subject or target of the operation and extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the content or state of the operation, for the respective clusters.