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1. Definition of vocabulary used 

   • Only focus on language (LLMs) vs multi-modal (language + images, audio, etc)?

2. Complementary solution
   1. Downstream tasks
      1. Question answering
      2. Fact checking
      3. Fake news detection
      4. Explainability
3. Improve LLMs by using KGs
   1. KG-enhanced LLM training
      1. Integrating KGs into training objective
      2. Integrating KGs into LLM inputs (verbalize KG for LLM training)
      3. Integrating KGs by fusion modules
   2. Retrieval-augmented Generation (RAG)
      1. Based on KG
   3. KG-enhanced LLM interpretability
      1. KGs for LLM probing
   4. KG-enhanced LLM inference / reasoning
   5. KGs for LLM analysis
4. Improve KGs by using LLMs
   1. Assertional knowledge engineering
      1. Information Extraction
         1. KG completion (A-Box)
            1. Link prediction
            2. Relation prediction
            3. Fact checking / Triple testing
            4. Literal completion (labels/comments/descriptions)
      2. Entity Linking (between KGs)
      3. Entity Disambiguation
   2. Terminological knowledge engineering
      1. Ontology Design
         1. Competency Question (CQ) generation
         2. User stories / personas generation
         3. Ontology learning (Automated ontology design from text)
      2. Ontology Evaluation
         1. Competency Question (CQ) generation (from given ontologies)
         2. CQ to SPARQL
      3. Ontology Mapping
      4. Ontology Documentation
         1. Class and relation descriptions/labels
   3. Reasoning
      1. Aprox/Probabilistic Reasoning via LLMs (LLM supported)
      2. Constraint checking (Robert D.)
      3. Data Repairs (→ maybe move to completion?) (Robert D.)
   4. Downstream tasks
      1. KG/Ontology embeddings
   5. User interface / Access
      1. Natural Language interface to KG
      2. KG to natural language (verbalization)
      3. Multilingual translation of literals

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