Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

Yabing Shi

Ye Jiang

Yong Zhu

Shuangjie Li

Yang Zhang

Chunguang Chai

Haijin Liang

Qian Li

Fang Huang

Yue Hua Zhang

Zhou Fang

Xunchao Song

Miao Yu

Jiandong Sun

Bingyang Yu

Xuefeng Hao

Zhaoji Wang

Chenglong Xue

Jingfeng Li

A method, apparatus, device, and storage medium for constructing a knowledge graph, relates to the field of data processing, and specifically to artificial intelligence technology is provided. The method may include: determining a scene and a scene element of the scene; determining a target tag from attribute tags based on an association relationship between an entity and the scene element, and an association relationship between the entity and each of the attribute tags; and establishing an edge between a scene node and a target tag node, to obtain a knowledge graph including scene information.

Method, apparatus, device and storage medium for constructing knowledge graph

The present disclosure provides a method of processing triple data, a method of training a triple data processing model, an electronic device, and a storage medium. A specific implementation solution includes: performing a triple data extraction on text data to obtain a plurality of field data; normalizing the plurality of field data to determine target triple data, wherein the target triple data contains entity data, entity relationship data, and association entity data; and verifying a confidence level of the target triple data to obtain a verification result.

Method of processing triple data, method of training triple data processing model, device, and medium

Disclosed are an information extraction method, an electronic device and a readable storage medium, which relate to the field of artificial intelligence technologies, and particularly to the field of knowledge graph technologies. The information extraction method includes: acquiring to-be-processed text to obtain a semantic vector of each token in the to-be-processed text; generating a relationship prediction matrix, an entity prediction matrix and an alignment matrix according to each token in the to-be-processed text and the semantic vector of each token; and extracting a target triplet in the to-be-processed text using the relationship prediction matrix, the entity prediction matrix and the alignment matrix, and taking the target triplet as an information extraction result of the to-be-processed text.

Extracting triplets from text with relationship prediction matrix, entity prediction matrix, and alignment matrix

A method and apparatus for generating a temporal knowledge graph, a device and a medium. An embodiment comprises: acquiring corpus including time information; performing multivariate data extraction on the corpus, multivariate data including an entity pair, an entity relationship and a target time interval of the entity relationship, the target time interval being used to indicate a valid period of the entity relationship; and generating a temporal knowledge graph based on the entity pair, the entity relationship and the target time interval of the entity relationship.

Method and apparatus for generating temporal knowledge graph, device, and medium

A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

Method of extracting table information, electronic device, and storage medium

A method and apparatus for processing a table, a device, a storage medium and a product. An implementation of the method comprise: receiving a content query request for a target table; acquiring a target tree structure of the target table according to the content query request; where, the target tree structure is obtained by performing absorbing processing and merging processing on at least one target cell in the target table; acquiring to-be-queried content in the content query request; and querying target content matching the to-be-queried content from the target tree structure.

Method and apparatus for processing table, device, and storage medium

A method and an apparatus for processing information are provided. The method can include: acquiring a word sequence obtained by performing word segmentation on two paragraphs in a text; inputting the word sequence into a to-be-trained natural language processing model to generate a word vector corresponding to a word in the word sequence; inputting the word vector into a preset processing layer of the to-be-trained natural language processing model; predicting whether the two paragraphs are adjacent, and a replaced word in the two paragraphs; and acquiring reference information of the two paragraphs, and training the to-be-trained natural language processing model to obtain a trained natural language processing model, based on the prediction result and the reference information.

Method and apparatus for processing information

Provided by the present disclosure is a new category tag mining method, involving the field of knowledge graph technology, and including: obtaining a plurality of queries during a current preset time period; labeling a category tag on each query of the plurality of queries, by using a pre-trained sequence labeling model, to extract the category tag currently corresponding to the query from the query; and removing a category tag already existing in a preset current category tag library from category tags currently corresponding to all the queries, and determining a remaining category tag as a new category tag. The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium.

Category tag mining method, electronic device and non-transitory computer-readable storage medium

The present disclosure relates to the field of medical data processing based on natural language processing. Embodiments of the present disclosure disclose a method and apparatus for verifying a medical fact. The method may include: acquiring a description text of the medical fact; selecting a relevant paragraph related to the description text of the medical fact from a medical document; and inputting the description text of the medical fact and the corresponding relevant paragraph into a trained discrimination model for authenticity judgment, to obtain a verification result of the medical fact, the discrimination model being pre-trained based on a medical text paragraph pair extracted from the medical document, and being iteratively adjusted using a medical fact sample set including authenticity labeling information after the pre-training.

Method and apparatus for verifying medical fact

Embodiments of the present disclosure provide to a method and a device for acquiring a data model in a knowledge graph, an apparatus and a storage medium. The method includes: receiving a knowledge entry describing a relationship between an entity and an object; determining a plurality of candidate object types of the object according to at least one of the entity, the relationship and the object; determining an object type for generating a data model that matches the knowledge entry from the plurality of candidate object types based on a preset rule; and generating the data model based at least on the object type.

Method and device for acquiring data model in knowledge graph, and medium

The disclosure discloses a method and an apparatus for recognizing an entity word. The method includes: obtaining an entity word category and a document to be recognized; generating an entity word question based on the entity word category; segmenting the document to be recognized to generate a plurality of candidate sentences; inputting the entity word question and the plurality of candidate sentences into a question-answer model trained in advance to obtain an entity word recognizing result; and obtaining an entity word set corresponding to the entity word question based on the entity word recognizing result.

Method and apparatus for recognizing entity word, and storage medium

Embodiments of the present disclosure provide a method, an apparatus and a device for generating entity relationship data, and a storage medium. The method includes: obtaining webpage source data corresponding to a target webpage; identifying at least one key value block from the webpage source data, wherein the key value block comprises at least one key value pair; identifying body values corresponding to the at least one key value block from the webpage source data; and generating entity relationship data corresponding to the target webpage according to the key value blocks and the body values corresponding to the key value blocks. With the technical solution the present disclosure, the webpage universality may be improved, labor cost may be reduced, and output quantity of the entity relationship data may be increased.

Method, apparatus and device for generating entity relationship data, and storage medium

Provided are a method, an apparatus and an electronic device for determining a knowledge sample data set, the method includes: acquiring a preset number of SPO triplet formats and source texts; acquiring, according to the SPO triplet formats, n SPO entries corresponding to the SPO triplet formats; searching, in the source texts, m first texts that match the n SPO entries, and generating a first knowledge sample data set; determining k second texts that meet the SPO triplet formats from the m first texts and generating a second knowledge sample data set; generating a target knowledge sample data set according to the first knowledge sample data set and the second knowledge sample data set. In the embodiments, the knowledge sample data set is automatically generated, the volume generation speed is fast, the cost is low, and the data size that can be produced is large, thus meeting the training requirement.

Method, apparatus and electronic device for determining knowledge sample data set

The present disclosure provides a method, a terminal device and a storage medium for mining an entity description tag. The method includes: acquiring a group of one or more core words corresponding to each field and a first syntax dependent template corresponding to each core word; performing a matching on each data in a first data source by using the first syntax dependent template to determine a first description tag set in each field; performing a recognition on each data in a second data source to determine an entity set; determining a second description tag set based on a matching degree between each description tag in the description tag set of each field and each data in the second data source; and determining an entity description tag set based on a correlation between each entity in the entity set and each description tag in the second descriptive tag set.

Method, terminal device and storage medium for mining entity description tag

A method and device for extracting entity relation based on deep learning and a server are provided. The method includes: preprocessing a text to be mined, to obtain a sentence with entities in the text to be mined; determining an entity pair in the sentence according to the entities, wherein the entity pair includes at least two entities, and determining candidate relations between entities in the entity pair; and determining an entity relation between the entities in the entity pair from the candidate relations.

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Beijing, China

Yabing Shi