Methods and systems for presenting time series for analysis. A method may receive a first input defining a metric that indicates a relationship between a first and a second time series that are each associated with at least a first data object of a plurality of data objects, generate a first plot depicting the metric as determined from the first and the second time series, receive, via the user interface, a second input of a selection of a second data object of the plurality of data objects, determine, via an ontology, a relationship of the second data object with a third and a fourth time series that, respectively, are associated with series types that match series types associated with the first and the second time series, and generate and display, in the user interface, a second plot depicting the metric as determined from the third and the fourth time series.

Systems and methods to extract and utilize textual semantics are described. The system receives item information that describes an item for sale on a network-based marketplace and analyzes the item information to generate application information that identifies a plurality of applications. The plurality of applications includes a first application that further includes the item as a first component of the first application. The system stores a listing in a database that includes the application information and the item information and publishes the listing on the network-based marketplace to sell the item via the network-based marketplace.

Systems and methods to extract and utilize textual semantics are described. The system receives item information that describes an item for sale on a network-based marketplace and analyzes the item information to generate application information that identifies a plurality of applications. The plurality of applications includes a first application that further includes the item as a first component of the first application. The system stores a listing in a database that includes the application information and the item information and publishes the listing on the network-based marketplace to sell the item via the network-based marketplace.

Data analytics systems and methods are disclosed herein. A parser can parse reference data from various data sources to store in a data structure. An uploader can receive study data designated by a researcher and store the study data in the data structure. A matcher can compare analyte nameset data in the study data with analyte nameset data from the reference data to generate one or more links each correlating an instance of an analyte in the study data with an instance of that analyte in the reference data. Library overlays each include one or more modules to access reference data to generate organized associations of reference data. A calculation engine can receive a selection of one or more library overlay(s) and manipulate the reference data and study data according to the organized associations of the selected library overlay(s) to generate configured data stored in a collection of data caches for presentation to a researcher via a user interface.

Provided is a method for querying questions. The method includes: acquiring input information of a user; acquiring intention information of the user based on the input information of the user; determining an answer generation rule; and generating, based on the input information and the intention information, a first answer in accordance with the answer generation rule, and providing the first answer to the user.

An apparatus generates an index including positions of morphemes included in a target text data and semantic attributes between the morphemes corresponding to the positions. The apparatus gives information including positions of morphemes included in an input query and semantic attributes between the morphemes corresponding to the positions to the query, and executes a retrieval on the target text data, based on the information given to the query and the index.

Systems and methods for retrieval of information from read-only databases that hold taxonomic-related and sequence-related data. A method may include receiving organism names from a taxonomy database and detecting new organism names. The method may also include retrieving hierarchical data and assigning the new organism names to buckets based on the hierarchical data. The method may further include receiving sequence data elements from a nucleotide database, identifying particular buckets to correspond to a screener data set, querying organism names assigned to the particular buckets with names of reference sequences of the sequence data elements, generating a mapping between the sequence data elements and organism names returned as a result of the queries, and storing the mapping.

Systems and methods for retrieval of information from read-only databases that hold taxonomic-related and sequence-related data. A method may include receiving organism names from a taxonomy database and detecting new organism names. The method may also include retrieving hierarchical data and assigning the new organism names to buckets based on the hierarchical data. The method may further include receiving sequence data elements from a nucleotide database, identifying particular buckets to correspond to a screener data set, querying organism names assigned to the particular buckets with names of reference sequences of the sequence data elements, generating a mapping between the sequence data elements and organism names returned as a result of the queries, and storing the mapping.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a deep neural network. One of the methods includes generating a plurality of feature vectors that each model a different portion of an audio waveform, generating a first posterior probability vector for a first feature vector using a first neural network, determining whether one of the scores in the first posterior probability vector satisfies a first threshold value, generating a second posterior probability vector for each subsequent feature vector using a second neural network, wherein the second neural network is trained to identify the same key words and key phrases and includes more inner layer nodes than the first neural network, and determining whether one of the scores in the second posterior probability vector satisfies a second threshold value.

One embodiment provides a method that includes determining candidate ontologies for alignment from multiple available knowledge bases. An initial target ontology is selected from the candidate ontologies and correcting the initial selected ontology with received refinement input. Concepts in the selected initial ontology are aligned with concepts of the target ontology using a deep learning hierarchical classification with received review input. A user is assisted to build, change and grow the selected initial ontology exploiting both the target ontology and new facts extracted from unstructured data.