Assertional Knowledge Engineering

Information Extraction

• 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/

Markus Schröder 

• 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

Contributors:

• Diego Collarana (FIT)
• Sven Hertling (FIZ)
• Desiree Heim (DFKI)
• Markus Schröder (DFKI)
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KG Completion (A-Box)

Link Prediction

Relation Prediction

Fact Checking / Triple Testing

Literal Completion (labels/comments/descriptions)

Entity Linking (between KGs)

• 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)
• Desiree Heim (DFKI)
• Markus Schröder (DFKI)
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Entity Disambiguation

Desiree Heim Markus Schröder 

• 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 disam-
  biguation 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)
• Michael Wetzel (Coreon)
• Sabine Mahr (word b sign)
• ... 

Competency Question (CQ) Generation

User Stories / Personas Generation

Ontology Learning (Automated ontology design from text)

Ontology Evaluation

Contributors:

• Sven Hertling (FIZ)
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Competency Question (CQ) generation (from given ontologies)

CQ to SPARQL

Ontology Mapping

Contributors:

• Sven Hertling (FIZ)
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Ontology Documentation

Contributors:

• Daniel Baldassare (doctima)
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Class and Relation Descriptions/Labels

Reasoning

Approx/Probabilistic Reasoning via LLMs (LLM supported)

Contributors:

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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

Contributors:

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Please add other Downstream Tasks ...

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User Interface / Access

Natural Language Interface to KG

• Natural Language to SPARQL

Desiree Heim 

• 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)
• Desiree Heim (DFKI)
• Markus Schröder (DFKI)
• Sabine Mahr (word b sign)
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• ... 

KG to Natural Language (verbalization)

• 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)
• Desiree Heim (DFKI)
• Markus Schröder (DFKI)
• Sabine Mahr (word b sign)
• ... 

Multilingual Translation of Literals

Contributors:

• Michael Wetzel (Coreon)
• Sabine Mahr (word b sign)
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