Document Semantic Graph
(Redirected from Document Semantic Network)
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A Document Semantic Graph is a semantic graph that represents document semantic structures through document semantic nodes and document semantic edges.
- AKA: Document-Level Semantic Graph, Textual Semantic Graph, Document Semantic Network.
- Context:
- It can typically represent Document Semantic Entities as document semantic graph nodes with document semantic attributes.
- It can typically encode Document Semantic Relations as document semantic graph edges with document semantic edge weights.
- It can typically capture Document Semantic Structures including document semantic hierarchies and document semantic dependencies.
- It can typically enable Document Semantic Graph Sub-Structure Learning through document semantic graph algorithms.
- It can typically support Document Semantic Graph Traversal for document semantic information extraction.
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- It can often be used in Multi-Document Summarization for document semantic graph-based content selection.
- It can often integrate Document Semantic Graph Annotations including document semantic role labels and document semantic type tags.
- It can often employ Document Semantic Graph Construction Methods such as document semantic parsing and document semantic linking.
- It can often utilize Document Semantic Graph Representations including document semantic adjacency matrices and document semantic embeddings.
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- It can range from being a Simple Document Semantic Graph to being a Complex Document Semantic Graph, depending on its document semantic graph node density.
- It can range from being a Shallow Document Semantic Graph to being a Deep Document Semantic Graph, depending on its document semantic graph layer count.
- It can range from being a Sparse Document Semantic Graph to being a Dense Document Semantic Graph, depending on its document semantic graph edge connectivity.
- It can range from being a Static Document Semantic Graph to being a Dynamic Document Semantic Graph, depending on its document semantic graph temporal evolution.
- It can range from being a Single-Document Semantic Graph to being a Cross-Document Semantic Graph, depending on its document semantic graph document scope.
- It can range from being a Domain-General Document Semantic Graph to being a Domain-Specific Document Semantic Graph, depending on its document semantic graph domain specialization.
- It can range from being a Manually-Constructed Document Semantic Graph to being an Automatically-Constructed Document Semantic Graph, depending on its document semantic graph construction method.
- It can range from being a Homogeneous Document Semantic Graph to being a Heterogeneous Document Semantic Graph, depending on its document semantic graph node type diversity.
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- It can be constructed by Document Semantic Graph Construction Systems using document semantic graph construction algorithms.
- It can be analyzed using Document Semantic Graph Analysis Tools for document semantic graph pattern discovery.
- It can integrate with Document Semantic Graph Databases for document semantic graph storage and document semantic graph retrieval.
- It can interface with Document Semantic Graph Visualization Systems for document semantic graph exploration.
- It can support Document Semantic Graph Applications including document semantic graph-based summarization and document semantic graph-based question answering.
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- Example(s):
- Leskovec et al. (2004) Document Semantic Graph, for document semantic sub-structure learning.
- AMR Document Graph, representing abstract meaning representations.
- Knowledge Graph Document Representations, such as:
- DBpedia Document Graph, linking document entities to knowledge base.
- Wikidata Document Graph, connecting document concepts to structured knowledge.
- NLP System Document Graphs, such as:
- Domain-Specific Document Graphs, such as:
- Summarization Document Graphs, such as:
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- Counter-Example(s):
- Syntax Tree, focusing on grammatical structure rather than semantic relations.
- Word Co-occurrence Graph, capturing statistical associations rather than semantic meaning.
- Citation Graph, representing document references rather than internal semantics.
- Topic Model, providing probabilistic topic distributions rather than explicit semantic structure.
- Bag-of-Words Representation, lacking structural information and semantic relations.
- See: Semantic Graph, Knowledge Graph, Document Representation, Semantic Network, Graph-Based NLP, Document Understanding, Semantic Parsing, Graph Neural Network.