Assertional Knowledge Engineering

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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• 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)

• 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

Markus Schröder 

• 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)
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Entity Disambiguation

Desiree Heim Markus Schröder 

• 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: Golnaz Shapurian. Large language models and knowledge graphs for astronomical entity disambiguation. arXiv preprint arXiv:2406.11400, 2024; Shaojun Liu and Yanfeng Fang. Use large language models for named entity disambiguation in academic knowledge graphs. In 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023), pages 681–691. Atlantis Press, 2023

Contributors:

• Desiree Heim (DFKI)
• Markus Schröder (DFKI)
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Terminological Knowledge Engineering

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

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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

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Ontology Mapping

Contributors:

• Sven Hertling (FIZ), Harald Sack (FIZ), Heike Fliegl (FIZ)
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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

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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?)

Contributors:

• Robert David
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Downstream Tasks

KG/Ontology Embeddings

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User Interface / Access

Natural Language Interface to KG

• Natural Language to SPARQL

Desiree Heim 

• 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: Jacopo D’Abramo, Andrea Zugarini, and Paolo Torroni. Dynamic few-shot learning for knowledge graph question answering. arXiv preprint arXiv:2407.01409, 2024; ] Dmitrii Pliukhin, Daniil Radyush, Liubov Kovriguina, and Dmitry Mouromtsev. Improving subgraph extraction algorihtms for one-shot sparql query generation with large language models. In QALD/SemREC@ ISWC, 2023.

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?)

Desiree Heim 

• 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: Phillip Schneider, Manuel Klettner, Elena Simperl, and Florian Matthes. A comparative analysis of conversational large language models in knowledge-based text generation. arXiv preprint arXiv:2402.01495, 2024; Sven Hertling and Heiko Paulheim. Olala: Ontology matching with large languagemodels. In Proceedings of the 12th Knowledge Capture Conference 2023, pages 131–139, 2023

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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