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Information Extraction
Contributors:
- Diego Collarana (FIT)
- Sven Hertling (FIZ), Harld Sack (FIZ), Heike Fliegl (FIZ)
- Desiree Heim (DFKI)
- Markus Schröder (DFKI)
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Short definition/description of this topic: please fill in ...
- From NLP community: relation extraction
- closed IE vs open IE
- related survey: https://arxiv.org/abs/2312.17617
- related approach: https://aclanthology.org/2024.findings-acl.839/
- extract structural knowledge from natural language texts.
- typical: Named Entity Recognition (NER), Relation Extraction (RE) and Event Extraction (EE)
- References: Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, and Enhong Chen. Large language models for generative information extraction: A survey.CoRR, abs/2312.17617, 2023
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KG Completion (A-Box)
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Link Prediction
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Relation Prediction
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Fact Checking / Triple Testing
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Literal Completion (labels/comments/descriptions)
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Entity Linking (between KGs)
Contributors:
- Definition/description: Given two entity representations as text (verbalized or in RDF serialization), ask LLM if the entities are referring to the same real-world entity.
- candidate generation can be done via Sentence BERT models
- typical: mapping mentions to their corresponding entities given a context
- between KGs: ... ?
- also embedding based entity linking, more general: "Neural entity linking"
- LLM: good for interpreting uncommon mention
- References: Ozge Sevgili, Artem Shelmanov, Mikhail Y. Arkhipov, Alexander Panchenko, and Chris
Biemann. Neural entity linking: A survey of models based on deep learning.Semantic Web, 13(3):527–570, 2022
Contributors:
- Sven Hertling (FIZ), Harald Sack (FIZ), Heike Fliegl (FIZ)
- Desiree Heim (DFKI)
- Markus Schröder (DFKI)Please add yourself if you want to contribute ...
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Entity Disambiguation
Contributors:
- Definition/Description: Disambiguation: given mentions and contexts, need a function which maps them to entities in KG
- Oftentimes also part of IE pipelines (e.g. KG Completion)
- has typical steps: candidate generation, entity ranking
- Direct LLM assistance: Verbalized description of link candidates compared against a text snippet that contains the entity that should be linked/disambiguated + formulated as a classification task
- Indirect LLM assistance: E.g., using LLM-assisted KG IE from texts and compare the found entities and relationships against the neighborhood of the link candidates
- References: https://doi.org/10.48550/arXiv.2406.11400; https://doi.org/10.2991/978-94-6463-264-4_79
Contributors:
- Desiree Heim (DFKI)
- Markus Schröder (DFKI)Please add yourself if you want to contribute ...
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Ontology Design
Contributors:
- Sven Hertling (FIZ), Harald Sack (FIZ), Heike Fliegl (FIZ)
- Michael Wetzel (Coreon)
- Sabine Mahr (word b sign)
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Competency Question (CQ) Generation
Short definition/description of the topic: the process of formulating questions that serve as a benchmark to assess the completeness and adequacy of an ontology
- Possible references:
- Can LLMs Generate Competency Questions? (no DOI) https://www.eurecom.fr/publication/7699 https://2024.eswc-conferences.org/wp-content/uploads/2024/04/ESWC_2024_paper_268.pdf
- The Role of Generative AI in Competency Question Retrofitting (no DOI) https://2024.eswc-conferences.org/wp-content/uploads/2024/05/77770001.pdf
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User Stories / Personas Generation
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Ontology Learning (Automated ontology design from text)
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Ontology Evaluation
Contributors:
- Sven Hertling (FIZ), Harald Sack (FIZ), Heike Fliegl (FIZ)
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Competency Question (CQ) generation (from given ontologies)
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CQ to SPARQL
Short definition/description of the topic: Given a competency question, formulate a SPARQL query to check if the question could be solved.
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Ontology Mapping
Contributors:
- Sven Hertling (FIZ), Harald Sack (FIZ), Heike Fliegl (FIZ)
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- Possible references:
- LLMs4OM: Matching Ontologies with Large Language Models https://doi.org/10.48550/arXiv.2404.10317 https://2024.eswc-conferences.org/wp-content/uploads/2024/05/77770022.pdf
- OLaLa: Ontology Matching with Large Language Models https://doi.org/10.48550/arXiv.2311.03837
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Ontology Documentation
Contributors:
- Daniel Baldassare (doctima)
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Definition/Description: please fill in ...
Draft from Daniel Baldassare :
- Data model description: nodes and relationships classes
- Metadata model description: nodes and relationships' s metadata (identifiers, optional and required metadata)
- Use with LLM:
- How to use it for standard RAG ( which embedding model)
- How to use it for GraphRAG/semantic layer (which embedding model, which additional metadata)
Class and Relation Descriptions/Labels
Reasoning
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Reasoning
Approx/Probabilistic Reasoning via LLMs (LLM supported)
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Constraint Checking
Contributors:
- Robert David
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Data Repairs (→ maybe move to completion?)
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- Robert David
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Downstream Tasks
KG/Ontology Embeddings
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User Interface / Access
Natural Language Interface to KG
Contributors:
- Natural Language to SPARQL
- Definition/Description:
- Text2QueryLanguage (e.g. Text2SPARQL)
- Direct translation of user queries into equivalent knowledge graph query languages by prompting an LLM
- The knowledge graph structure can be given either by including a (sub)schema or a subgraph in the query translation prompt
- KG-RAG
- Can be done by extracting knowledge graph entities and potentially relevant relationships from the user query and retrieving relevant triples from the knowledge graph which are given as a context to the prompt containing the user query
- References:https://doi.org/10.48550/arXiv.2407.01409, https://ceur-ws.org/Vol-3592/paper6.pdf
Contributors:
- Diego Collarana (FIT)
- Sven Hertling (FIZ), Harld Sack (FIZ), Heike Fliegl (FIZ)
- Desiree Heim (DFKI)
- Markus Schröder (DFKI)
- Sabine Mahr (word b sign)
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KG to Natural Language (verbalization)
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- OWL constructs to natural language (usually deterministic and no LLM involved?)
- Typically triple-based verbalization
- Different granularity of verbalizations (very exact subject/predicate/object sentences vs summary of subgraphs)
- ABOX statement verbalization: Often employed in RAG-based KGQA processes
- TBOX statement verbalization: Relevant for Text2QueryLanguage, Ontology-based KG Completion tasks
- References: https://doi.org/10.48550/arXiv.2402.01495; https://doi.org/10.1145/3587259.3627571
Contributors:
- Daniel Baldassare (doctima)
- Sven Hertling (FIZ), Harald Sack (FIZ), Heike Fliegl (FIZ)
- Desiree Heim (DFKI)
- Markus Schröder (DFKI)
- Sabine Mahr (word b sign)
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Multilingual Translation of Literals
Contributors:
- Michael Wetzel (Coreon)
- Sabine Mahr (word b sign)
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