311 results about "Search engine" patented technology

Techniques for determining whether retrieved content is sufficient for a language model to generate a response to an input are described. In some embodiments, a system may determine a retrieval complexity (RC) metric based on a question, a reference answer and document results retrieved from a retrieval component (e.g., a search engine, a knowledge search, etc.). The RC metric may be based on whether one of the document results corresponds to the reference answer and whether the reference answer can be determined from the set of the document results (e.g., using two or more document results). The RC metric can be used to determine when retrieved content is to be used by a language model for responding to an input corresponding to the question.

The invention relates to the technical field of document processing, and discloses a document knowledge retrieval method and system, electronic equipment and a storage medium. Based on a language model, synchronously generating abstract contents and a semantic association query set for each text fragment, and aggregating the abstract contents of all text fragments corresponding to the same document to form a fragment abstract set; constructing a multi-path retrieval pool based on the text fragment, the abstract content and the semantic association query set, and respectively constructing a plurality of independent retrieval data sources; the method comprises the following steps: constructing a dual-granularity abstract index system, receiving a query request of a user, executing query routing processing based on dual-granularity abstract index and a hierarchical retrieval process based on a metadata structure, returning retrieval result data, performing fusion calculation and duplicate removal processing on the retrieval result data recalled by multiple paths, and obtaining a retrieval result. And meanwhile, the document abstract short sentence group is injected into a search suggestion function of a search engine. According to the method, the retrieval accuracy can be improved, and the user experience is improved.

Various embodiments improve search technologies and computer information retrieval by executing a query via ranking a set of search result candidates higher than another set search result candidates based at least in part on the query and determining that a first set of search result candidates are indicative of a sub-accessory to an accessory or an accessory itself.

The invention belongs to the technical field of search engines, and particularly relates to an optimized search engine construction method and system based on feature deep learning, and the system comprises a search operation unit, a monitoring and early warning unit and a management and control terminal, the search operation unit comprises a multi-dimensional semantic feature mining module, a user implicit intention analysis module, a deep feature fusion modeling module and a dynamic retrieval strategy generation module; a multi-dimensional semantic feature set and a user intention feature set are formed through a multi-dimensional semantic feature mining module and a user implicit intention analysis module, and a deep feature fusion modeling module performs deep fusion of data features and user intention features through an attention mechanism and cross validation optimization. The dynamic retrieval strategy generation module is used for providing high-quality feature support for retrieval strategy generation, the accuracy of retrieval results is guaranteed, the dynamic retrieval strategy generation module generates dynamic strategies adaptive to different requirements and data states on the basis of reinforcement learning, and the retrieval accuracy and retrieval efficiency of a search engine are remarkably improved.

The invention relates to the field of document search, and particularly discloses a knowledge document search engine based on word vectors and contextual abstract summarization, which comprises the following steps: firstly, carrying out multi-modal analysis on an original heterogeneous document, constructing a keyword weight index by calculating multi-dimensional features of the document, and constructing an abstract vector index by utilizing a contextual abstract generation technology; in the retrieval stage, the core lies in introducing a query understanding and dynamic weight generation mechanism, deeply analyzing a query intention (such as accurate search or concept exploration) by extracting linguistic features queried by a user, and predicting and generating a dynamic fusion weight in real time. According to the mechanism, the proportion of semantic recall and keyword recall in final sorting can be adaptively adjusted according to the query type, the defect that a traditional static fusion strategy is cut at a time is effectively overcome, finally, a local large language model is combined to generate accurate questions and answers, and the search recall ratio and precision ratio in a complex scene are remarkably improved.

The invention discloses a retrieval enhancement generation system and method based on multi-view semantic fusion. According to the method, a three-classification mechanism is provided for solving the problem that false positive documents cannot be effectively recognized through an existing RAG reordering technology, and candidate documents are accurately divided into negative examples, false positive examples and positive examples. Semantic representation is extracted from three perspectives of global interaction, query intention and document content, and multi-perspective information is adaptively fused by adopting a dynamic routing mechanism of a capsule network, so that the document correlation is accurately judged. Experiments show that compared with a traditional reordering method, the method has the advantages that the Recall (at) 10 is improved by 8.07%, the accurate matching (EM) is improved by 1.27%, and the reasoning throughput is improved by 60.3% on a Natral Questones data set. The method can be widely applied to the fields of intelligent questions and answers, search engines, knowledge management and the like.

A search engine of a data exchange may receive a query comprising a set of search terms, retrieve a plurality of data listings based on the search terms of the query, compare a first embedding generated by a large language model (LLM) from the search query to second embeddings generated by the LLM for each of the plurality of data listings to determine a respective relevance for each of the plurality of data listings to the search query, and rank the plurality of data listings based on the respective relevance for each of the plurality of data listings to the search query.

Technology is disclosed for dynamically creating and / or validating new URLs based on a user's query and / or contextual data. This enables the discovery of relevant content that may not be pre-existing in the search engine's index. This process enhances the search engine's ability to deliver the most current and relevant results by generating potential content sources in real-time, validating them, and incorporating the discovered content into the index for immediate query execution. Various embodiments are additionally or alternatively directed to a process that involves iteratively discovering, crawling, and indexing new content by following links from seed URLs, which may be either pre-existing or generated. This process deepens the search engine's index by continuously exploring outlinks, fetching new content, and updating the index in real-time.

Systems, methods, and non-transitory computer-readable media implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, one or more embodiments involve receiving an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and one or more embodiments involve retrieving the image search results utilizing a weighted combination of the queries. Some implementations involve generating an input embedding for the search input (e.g., the multi-modal search input) and retrieving the image search results using the input embedding.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for indexing actions for resources. In an aspect, a method receiving, for each resource a plurality of resources that are each accessible by a user device, action data describing actions supported by each resource, each action being an action that can be performed by the user device processing the resource; for each resource: determining, from the action data, an action type for each action supported by the resource, determining, from the action data, action parameters for each action supported by the resource, and indexing the action data, action types and action parameters for the resource in an index that is searchable by a search engine.

A method for integrating streaming platforms, website search engines, and social media is disclosed. The method may include a computer system sending a particular content recommendation to a user's device. The computer system may receive information corresponding to the user's decision regarding their interest in the particular content recommendation, and may determine a different content recommendation using a history of the user's decisions regarding other content recommendations. The computer system may then send the different content recommendation to the user's device.

This application relates to the field of document search, specifically disclosing a knowledge document search engine based on word vectors and contextual summarization. First, it performs multimodal parsing on the original heterogeneous documents, constructing a keyword weight index by calculating the multidimensional features of the documents, and then using contextual summarization generation technology to construct a summary vector index. In the retrieval stage, the core lies in introducing a query understanding and dynamic weight generation mechanism. By extracting the linguistic features of user queries to deeply analyze query intent (such as precise search or concept exploration), it predicts and generates dynamic fusion weights in real time. This mechanism can adaptively adjust the weight of semantic recall and keyword recall in the final ranking according to the query type, effectively solving the one-size-fits-all defect of traditional static fusion strategies. Finally, it combines a local large language model to generate accurate question answers, significantly improving the recall and precision of searches in complex scenarios.

A system (10) for use in medical device servicing includes a database (30, 31) storing medical device schematics (32) and annotations (33) to the medical device schematics; a search engine (14) configured to receive information (34) related to servicing of a medical device (12) and search the annotations to the medical device schematics in the database using a search query based on the received information to retrieve one or more medical device schematics in the database; and a navigation device (18) including a display (24) and at least one input device (22), the navigation device operatively connected to receive the one or more medical device schematics from the search engine and configured to display the one or more medical device schematics annotated with the annotations to the one or more medical device schematics.

This invention provides an artificial intelligence-based method and system for automatically generating online educational content, relating to the fields of internet big data services and cloud computing technology. By constructing a semantic energy envelope interference engine, this invention achieves deep coupling of online educational resources in terms of temporal activity and spatial logic. This not only enhances the logical continuity and academic freshness of interdisciplinary generated content but also effectively suppresses redundant computation of invalid resources through energy coherence judgment logic. While ensuring content purification, it significantly reduces the invalid load on the cloud search engine, enhances operational robustness under conditions of interruption or fluctuation in massive information flow, and provides a highly stable technical foundation for next-generation intelligent search of massive information and cloud services for educational content.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for indexing actions for resources. In an aspect, a method receiving, for each resource a plurality of resources that are each accessible by a user device, action data describing actions supported by each resource, each action being an action that can be performed by the user device processing the resource; for each resource: determining, from the action data, an action type for each action supported by the resource, determining, from the action data, action parameters for each action supported by the resource, and indexing the action data, action types and action parameters for the resource in an index that is searchable by a search engine.

Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.

The invention provides a search method and device, equipment, a storage medium and a program product. The search method comprises the steps that multiple search results returned by multiple search engines for at least one query are obtained; the release time, the website domain name and the content feature of each search result are extracted; based on a pre-trained large language model, the release time, the website domain name and the content feature of each search result and the query corresponding to each search result, obtaining an evaluation result of each search engine; and determining a target search engine from the plurality of search engines based on the evaluation result of each search engine, and carrying out search based on the target search engine. By means of multi-dimensional features and a large language model, automatic evaluation of the search effect of the search engine is achieved, and evaluation objectivity, accuracy and comprehensiveness are improved; meanwhile, the engine is automatically selected for searching based on the evaluation result, and the hit rate of searching is increased.

The application relates to the technical field of artificial intelligence, and discloses a research report generation method, device, equipment and storage medium, the method comprising: in response to an input research topic, planning an agent to plan research steps based on the research topic, obtaining a multi-step research plan with a logical progression relationship, and based on the multi-step research plan, gradually searching in a search engine by a research agent to obtain research-related information corresponding to each research step, and generating a research report corresponding to the research topic according to the research-related information corresponding to each research step. In the application, multi-agents work together to generate a multi-step research plan with a logical progression relationship and gradually search in a search engine to generate a research report, thereby realizing intelligent planning of research report generation and improving research efficiency and knowledge integration capability.

The application provides a kind of intelligent environmental damage questionnaire generation and semantic analysis method and system, comprising: obtaining structured environmental data through government department interface, combining AI search engine to capture network public data;Data is cleaned and normalized, and a standardized environmental database is constructed;Based on NLP technology, a semantic model is constructed, customized survey questions are dynamically generated according to the type of interviewee and event scene classifier, and question priority is optimized;Key entities are extracted from interview text using a pre-trained model;Through the environmental damage ontology library, the entity is mapped to the preset pollution category, and a dynamic causal correlation graph is generated;The integrity and consistency of the graph are verified using logical verification rules, and a structured survey report is automatically output.The application solves the defects of traditional questionnaire design rigidity, information fragmentation and insufficient implicit association mining, significantly improves the accuracy and efficiency of environmental damage investigation, and provides intelligent support for pollution tracing and responsibility definition.

Embodiments of the present application provide a data retrieval method, device and equipment and storage medium, relating to the technical field of data analysis. The method comprises: in response to a retrieved request, determining a target search term and a request object; based on an object search term library corresponding to the request object and a pre-constructed business system search term library, performing word segmentation processing on the target search term respectively, and based on the word segmentation processing result corresponding to the business system search term library and a pre-set priority label, establishing a corresponding search index; and based on the business system corresponding to the request object, the word segmentation result obtained based on the object search term library and the search index, obtaining a search result. The embodiments of the present application solve the problem that the search result cannot meet the user's expectation when the search engine searches diversified data in the prior art.

A search and retrieval data processing system for retrieving classified data for execution against a cyber-security logic rules includes one or more hardware processors and memory, networked to communicably couple through a secure connection to a classified data database, the classified data database including an aggregated plurality of classified data records captured from a plurality of classified data sources; and the memory in communication with the processor, the memory storing an execution environment and the execution environment including an input port for receiving, from an entity, data associated with requested operations; a search engine for searching the classified data records of the classified database; and a logic execution module. The logic execution module is operable for executing a set of cyber-security logic rules against the received data and classified data records; and based on a positive comparison between the received data to the classified data records, writing to memory one or more instructions for initiation of one or more termination actions of the one or more requested operations.

A search engine-based user portrait construction method, system, medium and device, relating to the technical field of Internet, the method comprises: obtaining target search behavior data of a user; determining data corresponding to static feature labels and dynamic feature labels of the target user according to the target search behavior data; calculating first feature weights of the user according to the data corresponding to each dynamic feature label; correcting the first feature weights based on a preset time decay formula to obtain second feature weights; and generating a user portrait of the user based on each static feature label and the second feature weights. The technical solution provided by the present application can construct a more accurate user portrait according to the search behavior of the user, thereby facilitating subsequent recommendation of more accurate related information to the user based on the user portrait.

This specification provides a method and apparatus for determining the relevance of query-item pairs. The method includes: obtaining a neighbor set of a target query-target item pair based on historical user behavior data of a target search engine; the neighbor set including at least one of the following sets: a first set of first items corresponding to the target query, and a second set of first queries corresponding to the target items; constructing target hint information based on the target query-target item pair and the neighbor set; and inputting the target hint information into a large language model to obtain a relevance score of the target query-target item pair output by the large language model.

A method for displaying a search result to a user in a client device is provided. The method includes receiving, in a server, a search query from a user, the search query including a string of text characters. The method also includes converting the search query into a sequence of numbers based on a semantic content of the string of text characters, the sequence of numbers defining a vicinity in a multidimensional space, identifying a text string associated with a point within the vicinity in the multidimensional space, ranking the text string according to a similarity value with the search query, and providing a link to a media file associated with the text string to the user as a search result. A system, a memory storing instructions which, when executed by a processor cause the system to perform the above method, and the processor are also provided.

In one embodiment, an illustrative method herein comprises: obtaining, by a process, a file having a given size; assigning, by the process, the file to a particular size-range bucket of a plurality of size-range buckets of a data ingestion pipeline, the plurality of size-range buckets having a corresponding size-based configuration; and forwarding, by the process, the file into a particular size-based service mesh ingress gateway of the data ingestion pipeline according to the particular size-range bucket to cause processing of the file within the data ingestion pipeline according to the corresponding size-based configuration of the particular size-range bucket.

A method of operating a search service includes observing, on a per-user basis with respect to each user in a group of users, usage of features of a search engine over a period of in association with search queries submitted to the search engine. The method continues with identifying, on a per-user basis with respect to each of the group of users, one or more insights into the usage of the features over the period of time. The service may then enable display of the one or more insights in a user interface.

The invention provides a semantic segmentation-based paper evidence-based question and answer optimization method, which relates to the technical field of natural language processing, and comprises the following steps: performing rule segmentation on a paper text to obtain independent sentence units, and then performing recursive merging based on a dependency relationship between sentences and semantic relevancy to obtain a semantic segmentation result; forming a plurality of text blocks, and performing semantic vector representation and storage; a question input by a user is read and converted into a question semantic vector, and evidence fragments are extracted from the multiple text blocks based on semantic similarity and a text matching fusion strategy; and splicing the user input question and the evidence fragment into a unified input sequence, and inputting the unified input sequence into the generative model to generate an enhanced answer. Through the method, the technical problem that in the prior art, due to the fact that a traditional search engine is insufficient in semantic understanding and a mechanical fixed-length segmentation mode is adopted, evidence extraction is fragmented, and then the answer credibility is affected can be solved, and the accuracy and efficiency of paper evidence-based question answering are improved by fusing semantic segmentation.

This application discloses a corpus processing method and system for a large-scale model search engine, relating to the field of data processing technology. The corpus processing method for a large-scale model search engine includes: obtaining a target word segmentation result based on a first sentence; obtaining the semantic contribution degree corresponding to each word element based on the target word segmentation result; filtering out word elements in the target word segmentation result whose semantic contribution degree is lower than a first preset value to obtain an optimized word segmentation result; obtaining the priority of the first sentence based on the optimized word segmentation result; and determining whether to input the first sentence as valid corpus into the large-scale model based on the priority. This application overcomes the limitations of traditional word segmentation and filtering, accurately identifying high-quality, low-frequency corpus with reliable sources and core semantics, preventing it from being overwhelmed by high-frequency, low-quality information, and simultaneously eliminating false, low-quality corpus based on keyword stuffing from the source, thereby improving the quality of the input corpus for the large-scale model.

The invention provides an electric vehicle charging pile recommendation method based on a heterogeneous information network. The method comprises the steps that historical charging POI page view data, charging pile scoring data of a user and charging plan search trend data and event search trend data from a search engine are collected and subjected to data preprocessing; constructing a heterogeneous information network; on the basis of the heterogeneous information network and the preprocessed data, the charging page view of the charging pile is predicted through a charging prediction model, and a charging page view prediction result is generated; based on the charging page view prediction result, through a collaborative filtering algorithm based on matrix decomposition, generating prediction scores of all unused charging piles by a user; the prediction score is corrected according to the weighted score of the POI around the charging pile, and a final recommendation score is obtained; and generating a charging pile recommendation list according to the final recommendation score. The accuracy and practicability of charging facility recommendation are improved.