知识图谱问答

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1. requirements

Functional Requirements:

  • Natural language question input

  • Accurate answers based on knowledge graph information

  • Support for complex multi-hop reasoning

  • Handle different question types (factoid, relationship, comparison)

Non-Functional Requirements:

  • Low latency (<500ms response time)

  • High availability (99.9%)

  • Scalability to handle large knowledge graphs

  • Support multiple languages

  • Privacy and security compliance

2. ML task & pipeline

a) Question Understanding:

  • Question type classification

  • Entity recognition and linking

  • Relation extraction

  • Query intent classification

b) Graph Processing:

  • Graph embedding generation

  • Subgraph retrieval

  • Path ranking

c) Answer Generation:

  • Answer extraction/generation

  • Confidence scoring

  • Evidence compilation

3. data collection

Sources:

  • Knowledge Graphs:

    • Wikidata

    • DBpedia

    • Domain-specific KGs

    • Company internal KGs

  • Training Data:

    • WebQuestions

    • ComplexWebQuestions

    • LC-QuAD 2.0

    • KQA Pro

    • MetaQA

Data Processing:

  • Entity normalization

  • Relation alignment

  • Graph completion

  • Question-answer pair generation

4. feature

Question Features:

  • BERT/RoBERTa embeddings

  • Dependency parsing features

  • Named entity mentions

  • Question type indicators

Graph Features:

  • Node embeddings (TransE, RotatE)

  • Structural features

  • Path features

  • Subgraph features

5. model

6. evaluation

Metrics:

  • Accuracy

  • F1 Score

  • Hits@K

  • MRR (Mean Reciprocal Rank)

  • Path validity

  • Answer completeness

  • Reasoning correctness

Testing Approaches:

  • Unit tests for each component

  • Integration tests

  • A/B testing

  • Human evaluation

  • Adversarial testing

7. deployment & serving

Infrastructure:

  • Containerization with Docker

  • Kubernetes for orchestration

  • GPU support for inference

  • Load balancing

  • Auto-scaling

8. monitor & maintenance

Monitoring:

  • Model performance metrics

  • System health metrics

  • Error rates and types

  • Latency distribution

  • Resource utilization

reference

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