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/

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), Harld Sack (FIZ), Heike Fliegl (FIZ)
• Desiree Heim (DFKI)
• Markus Schröder (DFKI)
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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

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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 disam-
  biguation disambiguation in academic knowledge graphs. In 2023 3rd International Conference on
  Educationon Education, Information Management and Service Science (EIMSS 2023), pages 681–
  691681–691. Atlantis Press, 2023

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• 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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Draft from Daniel Baldassare :

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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?)

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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

• 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.

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KG to Natural Language (verbalization)

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• OWL constructs to natural language (usually deterministic and no LLM involved?)

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