59 results about "Query expansion" patented technology

The invention relates to the technical field of literature information retrieval, and particularly discloses a literature information retrieval analysis system and method based on AI intelligence. The system comprises a representation vector output module, a preliminary retrieval result output module, a retrieval result updating module, a dynamic knowledge graph construction module, an enhanced literature representation output module, a recommendation list output module and a knowledge graph feedback updating module. A representation vector output module extracts literature text features; a preliminary retrieval result output module executes semantic enhancement retrieval and query expansion; a retrieval result updating module optimizes a preliminary result; a dynamic knowledge graph construction module constructs a graph based on the retrieval result; the recommendation list output module provides personalized recommendation, and the knowledge graph feedback updating module updates the knowledge graph according to the user interaction flow data. According to the method, the accuracy, intelligence and dynamic adaptability of literature retrieval are comprehensively improved, and user requirements can be deeply met.

The invention provides a query method, system and device based on iterative retrieval generation verification and a medium, and belongs to the technical field of data verification query. The method comprises the steps that based on a user query statement, an initial expansion query is obtained through a large language model; an initial retrieval document set is obtained from a corpus based on an initial expansion query and a retrieval model. And filling the user query statement and the initial retrieval document set into the text prompt template to obtain text prompt information. And based on the text prompt information, utilizing a large language model to obtain a target generation document set. And obtaining a target retrieval document set from the corpus based on the target generation document set and the retrieval model. And expanding the user query statement based on the target retrieval document set and the target generation document set to obtain a target expansion query. According to the method and the device, the extended document can be optimized and screened, the phenomenon of excessive extension is prevented, and the query extension quality is improved.

The invention provides a question answering method and device based on a large language model, electronic equipment and a medium. The method comprises the following steps: performing knowledge base retrieval on an original question to obtain a plurality of first retrieval results and retrieval scores of the plurality of first retrieval results; under the condition that the multiple first retrieval results have the first retrieval results with the retrieval scores smaller than the score threshold value, whether the original question is a simple question or not is determined through a large language model; if the original problem is a simple problem, performing query expansion processing on the original problem to obtain a new problem; performing knowledge base retrieval on the new question to obtain a second retrieval result; according to the second retrieval result and the first retrieval result with the retrieval score larger than or equal to the score threshold value in the multiple first retrieval results, answer information is determined.

The present disclosure relates to systems, devices, and methods for suggesting geographic results to a user. The systems, devices, and methods may include receiving a prefix for a geographic query and ranking received candidate results for the prefix of the geographic query. The systems, devices, and methods may provide a query expansion based on a highest ranked candidate result. The systems, devices, and methods may determine a likelihood of the user selecting the query expansion, and if the likelihood exceeds a threshold, provide the query expansion.

The invention discloses a retrieval enhancement generation method and system based on multi-dimensional reordering, and relates to the technical field of information retrieval, and the method comprises the following steps: S1, constructing a tensor index, a keyword index and a compression abstract; s2, driving a Qwen large model to execute query expansion and hypothetical answer generation through a dual-task query processing template; s3, mixed retrieval is executed through a semantic and keyword double-path index, and two-stage reordering is executed by using an improved DistilBERT model and a Qwen large model; s4, performing multi-dimensional evaluation through sub-problem decomposition; s5, iterating according to the sequence of the sub-questions, and gradually generating answers to the sub-questions; and S6, executing multi-dimensional evaluation correction, and generating a final answer. According to the method, the limitation of a traditional retrieval enhancement generation technology on information matching precision, context correlation evaluation and answer generation quality is overcome, and an efficient and accurate solution is provided.

The invention relates to the technical field of information retrieval, and discloses a term standardization query method and system based on a large language model, and the method comprises the steps: receiving original query, dialogue history and multi-modal input; analyzing the multi-modal input to generate an extended context; candidate aliases are extracted through natural language processing, and a domain term knowledge base is inquired in combination with a term list; adjusting the priority weight according to the user role and the real-time context; constructing a structured prompt including dialogue history, original query, extended context, candidate schemes and task instructions, and inputting a large language model to generate a replacement decision; updating the knowledge base based on user interaction data and model feedback; historical query records are stored to assist in follow-up reasoning. According to the invention, the picture, the PDF and the voice input are analyzed through the multi-modal context enhancement module, the extended context containing the term list and the semantic clue is generated, comprehensive context support is provided for term standardization, and query processing is ensured to adapt to various enterprise scenes.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for generating domain-specific queries that are semantically similar to a search query by spell-correcting and tokenizing a search query, and then generating, using an embeddings dictionary data object associated with one or more domain vocabulary data objects, queries semantically related to the search query based on proximity of one or more similar embeddings to an embedding associated with the tokenized query within a domain vector space.

The invention relates to the technical field of digital archive management and information retrieval, and discloses a digital archive multi-modal data semantic enhancement fusion retrieval method and system.The method comprises the steps that a policy cycle time axis and a policy term evolution graph are constructed, tense logical reasoning is conducted on archive seals, and permission effectiveness evolution is derived; the temporal permission feature vector and the content semantic vector are fused to generate a multi-modal representation vector, and cross-policy-cycle semantic enhancement retrieval is realized by combining query expansion and temporal permission filtering, so that the problems of missing detection and misjudgment of policy and regulation archives in seal permission historical evolution and term cross-cycle retrieval are solved.

A system and method for personalizing health information content provided to a user. The techniques include obtaining user profile data associated with the user in response to a query submitted by the user. The user profile data includes health data of the user and application use data by the user. The techniques further include expanding the query into a plurality of queries based on the health data of the user, performing semantic searches for the plurality of queries, ranking results from the performed semantic searches, aggregating the results from the performed semantic searches based on the ranking to generate an aggregated health information response, and providing the aggregated health information response to the user.

The invention discloses a query expansion method and system for power grid knowledge search, and the method comprises the steps: obtaining an initial query word of a user, and retrieving an initial document set from a power grid document set; segmenting the document into text fragments by using a preset power grid entity boundary dictionary, and calculating a context comprehensive weight of each fragment; calculating the semantic similarity between the initial query word and each fragment through the word vector, and screening out candidate expansion words in high-similarity fragments; a weighted co-occurrence graph is constructed based on the co-occurrence relation of the candidate expansion words, and edge weights are determined by accumulating context comprehensive weights of co-occurrence fragments; and combining the word vector similarity with the initial query word to carry out comprehensive sorting, selecting a word ranked in the top as an expansion word, and combining the expansion word with the initial query word to form an expansion query.

The invention discloses an intelligent file searching method and system based on natural language processing and a knowledge graph, and relates to the technical field of information retrieval, the method comprises the following steps: preprocessing acquired file data, including file format conversion, noise filtering and text extraction; semantic analysis is carried out on the extracted text content by using a natural language processing technology, and semantic support is provided for construction of a knowledge graph and subsequent search processing; based on a semantic analysis result, constructing a knowledge graph of the file field; when a user inputs a query statement, performing semantic understanding, query expansion and query conversion on the query statement, and converting the query statement into a query expression which can be understood by the knowledge graph; and searching in the knowledge graph according to the query expression, calculating a sorting result according to the correlation, and visually presenting the sorting result. According to the invention, the accuracy, correlation and intelligence of file search can be improved.

The invention provides an API sequence recommendation method based on user intention perception. The method comprises the steps of firstly constructing a training sample based on multi-source information; then carrying out joint modeling on the natural language description and the program structure representation by utilizing a pre-training language model to obtain unified representation fusing user intention and code semantics; secondly, predicting a candidate API sequence in an autoregression mode through a sequence generation model; secondly, in the reasoning stage, semantic enhancement is carried out on the input natural language query by adopting a query expansion technology so as to generate an expansion query; and finally, jointly inputting the extended query and the current code context into the sequence generation model, and outputting an API calling sequence conforming to the user intention. Natural language query and code context information are comprehensively utilized, the development intention is obtained from the natural language description input by the user, modeling is conducted on the programming environment in combination with the current code context, and the API recommendation result better meeting the actual development requirement is generated.

The invention discloses a building material semantic retrieval method based on a vector database, and relates to the technical field of building informatization management, and the technical scheme is as follows: the method comprises the following steps: obtaining building material data and carrying out cleaning and standardization processing, extracting material fields and business attribute information, and establishing synonym mapping information or material and product name mapping information; carrying out blocking and vectorization processing on the processed building material data, and storing the processed building material data to a vector database; performing query understanding on the natural language query of the user, complementing missing key fields and performing unified name expansion; performing query expansion based on the processed query field to generate a retrieval expression; and performing vector retrieval and keyword retrieval, fusing and reordering to obtain a target retrieval result, and performing extended re-retrieval when the result is empty or insufficient. The method has the beneficial effects that the retrieval accuracy, the recall capability and the result availability under the scenes of different names, unified names and material substitutive names of the building materials can be improved, and the empty result rate and the manual screening cost are reduced.

The invention provides an international business regulation intelligent retrieval method, which relates to the technical field of international business regulation retrieval, and specifically comprises the following steps: collecting and processing data, constructing a multi-language international regulation retrieval model, inputting a retrieval problem, and extracting key information. According to the method, the multi-language international regulation retrieval model is constructed, so that quick retrieval of cross-language and cross-regulation systems can be realized, the cumbersome process that a user looks up regulations one by one is avoided, and the user experience is improved. And the query intention of the user is understood by utilizing a natural language processing technology, the accuracy and comprehensiveness of the retrieval result are improved by combining a multi-language query expansion technology, and the user is helped to better understand and apply laws and regulations by displaying the retrieval result in a manner which is easy to understand by the user and providing auxiliary information such as explanation and cases of laws and regulations.

A system and method for personalizing health information content provided to a user. The techniques include obtaining user profile data associated with the user in response to a query submitted by the user. The user profile data includes health data of the user and application use data by the user. The techniques further include expanding the query into a plurality of queries based on the health data of the user, performing semantic searches for the plurality of queries, ranking results from the performed semantic searches, aggregating the results from the performed semantic searches based on the ranking to generate an aggregated health information response, and providing the aggregated health information response to the user.

The invention relates to the technical field of natural language processing, and discloses a query expansion method, computer equipment, a storage medium and a program product. Performing question-answer extension, title extension and keyword extension on the initial query question to generate a question-answer extension result, a title extension result and a keyword extension result; and generating an abstract based on the question and answer extension result, the title extension result and the keyword extension result to determine a target query extension question. According to the method, query expansion is abstracted into three sub-tasks with different granularities, the three sub-tasks are respectively used for acquiring document-level, sentence-level and word-level key information, finally, the three results are integrated, a final target query expansion problem is output, the final target query expansion problem can be used as expansion and supplement of an initial query problem, and the original intention of a user is reflected more accurately.

This invention provides a method for injecting industrial knowledge based on retrieval enhancement, belonging to the field of industrial knowledge technology. This invention establishes a time-series data flow matrix by collecting multi-source heterogeneous data, constructs a time-series causal knowledge graph using Granger causality tests, establishes an industrial knowledge document library with a hybrid index structure, performs time-series-aware query expansion on the query input, performs a hybrid retrieval of dense vectors and sparse inverted indexes and cross-encodes and reorders the data, associates the refined documents with the time-series causal knowledge graph to extract event evolution paths and fuses them to generate a time-series enhanced knowledge representation, inputs it into a time-series knowledge enhancement model to generate answer text containing fault analysis and prediction suggestions, injects it into an industrial decision support system after quality assessment, and stores feedback data for continuous model optimization. This solves the technical problem of industrial knowledge retrieval being disconnected from real-time temporal status, resulting in a lack of time-series causal reasoning ability in the generated answers.

The invention provides a query processing method which can be applied to the technical field of artificial intelligence. The query processing method comprises the steps that an original query text is acquired and preprocessed; inputting the preprocessed original query text into a pre-trained target model to obtain an entity of the original query text; performing standard normalization on the entity of the original query text; performing query expansion and reconstruction on the entity after standard normalization to obtain executable query representation; based on a target query template determined by the intention of the executable query representation, filling placeholders of the target query template with the executable query representation to obtain a target query statement for retrieval; wherein the target model is a double-architecture model containing a generation path and a compatible path. The invention further provides a query processing device and equipment, a storage medium and a program product.

This invention discloses a semantic retrieval method for building materials based on a vector database, belonging to the field of building information management technology. The technical solution includes: acquiring building material data and performing cleaning and standardization processing; extracting material fields and business attribute information; establishing synonym mapping information or material-name mapping information; segmenting and vectorizing the processed building material data and storing it in a vector database; performing query understanding on user natural language queries, completing missing key fields and expanding general terms; expanding the query based on the processed query fields to generate retrieval expressions; performing vector retrieval and keyword retrieval, fusing and re-ranking to obtain the target retrieval results, and performing expanded re-retrieval when the results are empty or insufficient. The beneficial effects of this invention are: improving the retrieval accuracy, recall, and result usability in scenarios involving synonyms, general terms, and material abbreviations of building materials, while reducing the empty result rate and manual screening costs.

The embodiment of the application discloses a research report generation method, which comprises the following steps: obtaining original text data from at least two data sources, generating overlapping text slices by using a sliding window slicing algorithm, and storing the text slices in a vector database through a text embedding model; performing multi-dimensional weighted scoring on each data source, combining a time decay factor and a Bayesian historical accuracy factor to calculate a final confidence score; providing a three-layer tree hierarchical report template, supporting user selection or semantic recommendation; performing query expansion and multi-stage semantic retrieval for an analysis problem, detecting semantic contradiction by using natural language reasoning, and performing conflict resolution based on confidence and correlation, and finally generating a complete research report by assembling the answer content with traceability annotation generated by a large language model after weighted fusion. The technical scheme of the application can realize quantitative evaluation of data confidence, has a multi-source data conflict resolution mechanism, and the content generation is traceable.

The invention belongs to the technical field of natural language processing, and discloses a query expansion method and a related device based on large language model thinking chain distillation, and the method comprises the following steps: obtaining an original query statement; inputting the original query statement into a query expansion model to obtain a query expansion thinking chain; splicing the original query statement and the query expansion thinking chain to obtain a query expansion statement; applying the query extension statement to a retrieval system for query retrieval to obtain a query result; wherein the query expansion model is obtained by the following steps: obtaining a plurality of training samples based on a large language model; and adopting a plurality of training samples to carry out supervised fine tuning on the small-parameter language model through a knowledge distillation technology to obtain a query expansion model. Potential requirements of users can be covered more comprehensively, and comprehensiveness and accuracy of query are effectively improved. The small-parameter language model has low requirements on hardware resources during deployment and use, the operation speed is high, the calculation cost can be reduced, and the response requirements of a real-time retrieval system are met.

The invention discloses an intelligent operation and maintenance disposal behavior monitoring method based on artificial intelligence, and the method comprises the steps: firstly, carrying out the judicial evaluation of the value of an initial result through preliminary retrieval and content validity judgment, and avoiding unnecessary query rewriting; only if necessary, the screened high-quality key text fragments are utilized to carry out conditional and constrained query expansion on the original alarm problem. Through the controlled expansion mode, the semantic deviation risk caused by unconstrained rewriting of a general model is avoided from the source, and it is ensured that secondary retrieval can more accurately focus on the core intention of original warning. Therefore, key information with higher signal-to-noise ratio and stronger correlation can be provided for subsequent scheme generation, and the accuracy and practicability of the finally generated disposal scheme are remarkably improved.

The invention discloses a water surface ship target re-identification method based on deep learning, and the method comprises the following steps: S1, obtaining a training data set of a water surface ship image, and carrying out the fogging processing of the image in the training data set according to a proportion; s2, using a YOLO model to carry out training on the training data set after fog adding processing; s3, training on the fogged training data set by using a visual auxiliary re-identification network with a defogging module; s4, performing detection by using the trained YOLO model to obtain a prediction frame, and performing prediction frame de-weighting by using a non-maximum suppression method based on pseudo-overlapping rate joint confidence; and S5, cutting each input frame of image according to a prediction frame, inputting the cut image into the visual angle auxiliary re-identification network to extract ship features and visual angle features, and identifying the ship identity by using a visual angle auxiliary adaptive query expansion method to obtain a re-identification result. The method significantly improves the precision of ship target re-identification, and still has good robustness in a foggy environment.

The present invention discloses a cultural relic image retrieval system based on contrastive learning. First, a supervised contrastive learning algorithm is used to train the model to obtain a good feature extractor, so that the extracted features can accurately express the semantic information contained in the image. Then, the similarity between the feature representations is calculated for retrieval, and the accuracy of the retrieval results is further improved by average query expansion and database-side feature enhancement. The retrieval system of the present invention trains the network to obtain a feature extractor through a supervised contrastive learning algorithm, which can extract effective and discriminative feature representations from images, and further improves the accuracy of retrieval through average query expansion and database-side data feature enhancement.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for generating domain-specific queries that are semantically similar to a search query by spell-correcting and tokenizing a search query, and then generating, using an embeddings dictionary data object associated with one or more domain vocabulary data objects, queries semantically related to the search query based on proximity of one or more similar embeddings to an embedding associated with the tokenized query within a domain vector space.

The present application belongs to the field of data encryption, and relates to a privacy protection multi-dimensional range query method and device and a storage medium, wherein the method comprises the following steps: a data owner generates a key required for an entire ciphertext range query; the data owner constructs an HHCB tree by using a layered Hilbert encoded ciphertext; a data user encodes a query range according to a layered Hilbert encoding mode, and generates a query threshold by using a hash function to perform hash mapping on a range encoding set; and a cloud database performs query expansion on the HHCB tree by using the query threshold, and returns a query result obtained to the data user. Compared with the prior art, the present application introduces a layered Hilbert encoding mode, reduces a cumbersome encryption and decryption calculation process during query, improves query efficiency, introduces an approximate encoding division method in index construction, improves traversal efficiency, and is more suitable for an actual cloud ciphertext database scene.

The application discloses a retrieval enhancement generation method and system based on multi-dimensional reordering, relates to the technical field of information retrieval, and comprises the following steps: S1, constructing a tensor index, a keyword index and a compressed abstract; S2, driving a Qwen large model to perform query expansion and hypothetical answer generation through a double-task query processing template; S3, performing mixed retrieval through a semantic and keyword double-path index, and performing two-stage reordering by using an improved DistilBERT model and the Qwen large model; S4, performing multi-dimensional evaluation through sub-problem decomposition; S5, iteratively generating sub-problem answers according to the sub-problem order; and S6, performing multi-dimensional evaluation correction to generate a final answer. The application overcomes the limitations of traditional retrieval enhancement generation technology in information matching accuracy, context relevance evaluation and answer generation quality, and provides an efficient and accurate solution.

This specification provides a question-answering method, apparatus, electronic device, and medium based on a large language model. The method includes: performing a knowledge base retrieval on the original question to obtain multiple first retrieval results and the retrieval scores of the multiple first retrieval results; in the case where there are first retrieval results with retrieval scores less than the score threshold among the multiple first retrieval results, using the large language model to determine whether the original question is a simple question; if the original question is a simple question, performing query expansion processing on the original question to obtain a new question; performing a knowledge base retrieval on the new question to obtain second retrieval results; and determining an answer message based on the second retrieval results and the first retrieval results among the multiple first retrieval results with retrieval scores greater than or equal to the score threshold.

A computing system uses a large language model (LLM) to generate one or more synthetic queries for each document of a set of documents. For a user query, the computing system: selects one or more of the synthetic queries related to the user query; generates an adaptive few-shot prompt to instruct the LLM to generate a response to the query, wherein the adaptive few-shot prompt comprises an example query-response pair for each of the selected one more synthetic queries; provides the adaptive few-shot prompt to the LLM as an input; and generates an amended query based on the output of the LLM in response to the adaptive few-shot prompt.

The present invention discloses an information retrieval query expansion method based on ConceptNet, comprising the following steps: S1: Use BM25 for retrieval to obtain a set of feedback documents and terms; S2: Obtain a set of semantic descriptions of terms by taking the semantic expansion set of terms from ConceptNet; S3: Measure the semantic relevance between candidate expansion terms and the query; S4: Obtain the weights of candidate expansion terms; S5: Construct a new query and conduct a secondary retrieval. The beneficial effect of the present invention is: It can optimize query expansion to improve the average precision rate of retrieval results.