Zhu et al., 2024 - Google Patents
Graph structure enhanced pre-training language model for knowledge graph completionZhu et al., 2024
View PDF- Document ID
- 12404001001422339302
- Author
- Zhu H
- Xu D
- Huang Y
- Jin Z
- Ding W
- Tong J
- Chong G
- Publication year
- Publication venue
- IEEE Transactions on Emerging Topics in Computational Intelligence
External Links
Snippet
A vast amount of textual and structural information is required for knowledge graph construction and its downstream tasks. However, most of the current knowledge graphs are incomplete due to the difficulty of knowledge acquisition and integration. Knowledge Graph …
- 238000012549 training 0 title abstract description 42
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/3066—Query translation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
- G06F17/30684—Query execution using natural language analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
- G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
- G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30389—Query formulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30731—Creation of semantic tools
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30613—Indexing
- G06F17/30619—Indexing indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/21—Text processing
- G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhu et al. | Graph structure enhanced pre-training language model for knowledge graph completion | |
| He et al. | VGSG: Vision-guided semantic-group network for text-based person search | |
| Guo et al. | A novel fake news detection model for context of mixed languages through multiscale transformer | |
| Ren et al. | LR-GCN: Latent relation-aware graph convolutional network for conversational emotion recognition | |
| Zeng et al. | Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network | |
| Li et al. | Sentiment analysis of Weibo comments based on graph neural network | |
| Shi et al. | Tgformer: A graph transformer framework for knowledge graph embedding | |
| Liu et al. | Cross-attentional spatio-temporal semantic graph networks for video question answering | |
| Liao et al. | Improved sequence generation model for multi-label classification via CNN and initialized fully connection | |
| Dao et al. | Broadening the view: Demonstration-augmented prompt learning for conversational recommendation | |
| Liu et al. | Transformer and graph convolutional network for text classification | |
| Wang et al. | Improved transformer with multi-head dense collaboration | |
| Zhu et al. | Graph-based approaches and functionalities in retrieval-augmented generation: A comprehensive survey | |
| Yuan et al. | A fine-grained network for joint multimodal entity-relation extraction | |
| Ye et al. | Fast semantic matching via flexible contextualized interaction | |
| Zhao et al. | Fusion with GCN and SE-ResNeXt network for aspect based multimodal sentiment analysis | |
| Feng et al. | Prototypical networks relation classification model based on entity convolution | |
| Wang et al. | Research on the construction and application of retrieval enhanced generation (RAG) model based on knowledge graph | |
| Li et al. | Mrgcddi: Multi-relation graph contrastive learning without data augmentation for drug-drug interaction events prediction | |
| Liu et al. | A hybrid re-fusion model for text classification | |
| Liu et al. | A closed-loop human-computer interactive design method based on sequential human intention prediction and knowledge recommendation | |
| Zhang et al. | Learning hierarchical document graphs from multilevel sentence relations | |
| Zheng et al. | Semantic-enhanced relation modeling for fine-grained aspect-based sentiment analysis | |
| Guo et al. | An Explainable Recommendation Method based on Diffusion Model | |
| Kamil et al. | Advances in transformer-based semantic search: Techniques, benchmarks, and future directions |