Biographical is a semi-supervised dataset for RE. The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata.

Catalan TimeBank 1.0 was developed by researchers at Barcelona Media and consists of Catalan texts in the AnCora corpus annotated with temporal and event information according to the TimeML specification language.

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The Dr. Inventor Multi-Layer Scientific Corpus (DRI Corpus) includes 40 Computer Graphics papers, selected by domain experts. Each paper of the Corpus has been annotated by three annotators by providing the following layers of annotations, each one characterizing a core aspect of scientific publications: * Scientific discourse: each sentence has been associated to a specific scientific discourse category (Background, Approach, Challenge, Future Work, etc.). * Subjective statements and novelty: each sentence has been characterized with respect to advantages, disadvantages and novel aspects presented. * Citation purpose: to each citation has been associated a purpose specifying the reason why the authors of the paper cited the specific piece of research. * Summary relevance of sentences and hand written summaries: each sentence of the paper has been characterized by an integer score ranging from 1 to 5, to point out the relevance of the same sentence for its inclusion in the summary of the paper. Sentences rated as 5 are the most relevant ones to summarize a paper. For each paper three hand-written summaries (max 250 words) are provided. Source: Dr. Inventor Multi-layer Scientific Corpus

We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents (the named entity recognition part) and link them to their numerical values (the relation extraction part). Challenges include the fuzzy borders of entities and finding the correct numeric value/year pair for each entity.

MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset can be used for NER (Named entity recognition), EL (entity linking) and RE (relation extraction), and thus can be used for joint and multi-task learning of these fundamental information extraction tasks.

MultiTACRED is a multilingual version of the large-scale TAC Relation Extraction Dataset. It covers 12 typologically diverse languages from 9 language families, and was created by the Speech & Language Technology group of DFKI by machine-translating the instances of the original TACRED dataset and automatically projecting their entity annotations. For details of the original TACRED's data collection and annotation process, see the Stanford paper. Translations are syntactically validated by checking the correctness of the XML tag markup. Any translations with an invalid tag structure, e.g. missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances). Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish, Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers. MultiTACRED is released via the Linguistic Data Consortium (LDC License). You can download MultiTACRED from the LDC MultiTACRED webpage. Please see our ACL paper for full details.

NYT-H is a dataset for distantly-supervised relation extraction, in which DS-labelled training data is used and several annotators to label test data are hired. NYT-H can serve as a benchmark of distantly-supervised relation extraction. Source: Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction

PcMSP is a dataset annotated from 305 open access scientific articles for material science information extraction that simultaneously contains the synthesis sentences extracted from the experimental paragraphs, as well as the entity mentions and intra-sentence relations. Source: PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text Image Source: https://arxiv.org/pdf/2210.12401v1.pdf

A human-revised dataset for seven languages that allows for the evaluation of multilingual RE systems.

https://github.com/dialogue-evaluation/RuSentNE-evaluation

X-WikiRE is a new, large-scale multilingual relation extraction dataset in which relation extraction is framed as a problem of reading comprehension to allow for generalization to unseen relations. Source: X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension

A curated evaluation dataset for end-to-end Relation Extraction of relationships between organisms and natural-products. Details about the manual annotation: * For Chemicals: - The chemical labels are annotated as they appear in the abstract. - In abstracts, singular chemicals and classes of chemicals produced by a specific organism were distinguished. - The "type" attribute {“chemical”, “class”} is used to indicate the nature of the mentioned name. - A "class" attribute for chemical entities has also been included if class information is present in the abstract. - A Wikidata and PubChem identifiers were assigned to chemicals and classes when available. * For Organisms: - The organism labels are annotated as they appear in the abstract. - If in an abstract, the genus name was mention first, e.g. "Plakinastrella sp." and then the specie name e.g "Plakinastrella clathrata" is precise, then only the specie name is used. - A Wikidata identifier was assigned to all organisms. - In some abstracts, only the genus name is mentioned. * For Relations: - Only the relations explicitly mentioned in the abstract are reported in the output labels. - Relations are reported in their order of appearance in the abstract.

The AbstRCT dataset consists of randomized controlled trials retrieved from the MEDLINE database via PubMed search. The trials are annotated with argument components and argumentative relations. Paper: Transformer-Based Argument Mining for Healthcare Applications

Bc8BioRED is built upon BioRED 2022 with the addition of directionality annotations. The training and development sets from the original 2022 BioRED corpus were combined and reused as the training set, while the test set was used as the development set. Furthermore, the 400 test abstracts from the BioCreative VIII were utilized for evaluation. Bc8BioRED encompasses seven types of entities and eight types of relationships. Each relationship annotation in the Bc8BioRED corpus is categorized by novelty to indicate whether the relationship represents a significant finding or previously known background knowledge. Initially, the BioRED 2022 corpus comprised 600 abstracts for RE system development, with an additional 400 abstracts annotated to enhance coverage of emerging topics. The dataset encompasses directionality annotations (subject/object roles) for each relation pair, resulting in 10,864 directionality annotations.

Chinese Literature NER RE is a Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text. It is constructed from hundreds of Chinese literature articles. Source: https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset Image Source: https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset

This is the dataset used for classifying Gene-Disease relationship types from sentences. The dataset consists of 3 files: * manually_annotated_set.xlsx - set of 2000 manualy annotated sentences with entities * Unbalanced_dataset.xlsx - set of 12000 sentences, out of which 2000 are from the first set, manually annotated, and the rest have been added using rule based method by adding sentences where extraction had confidence 1. * Balanced_dataset_SUB_PRED.xlsx - balanced dataset generated by taking 2000 manually annotated sentences, but then adding sentences from the rule-based method with confidence 1 in such a way that each relationship class had at least 1400 sentences (for biomarkers, we could obtain 1243 sentences with confidence 1 from a processed portion of the data we had at the time of building the dataset).

DiaKG is a high-quality Chinese dataset for Diabetes knowledge graph. The dataset is derived from 41 diabetes guidelines and consensus, which are from authoritative Chinese journals including basic research, clinical research, drug usage, clinical cases, diagnosis and treatment methods, etc. The dataset covers the most extensive field of research content and hotspot in recent years. All the annotators have a medical background, and finally conduct a high-quality diabetes database which contains 22,050 entities and 6,890 relations in total. Based on this dataset, doctors, researchers, and enterprise developers can develop knowledge bases for clinical diagnosis, knowledge graphs, and auxiliary diagnostics to further explore the mysteries of diabetes.

Dissonance Twitter Dataset is a dataset collected from annotating tweets for dissonance.

The DocRED Information Extraction (DocRED-IE) dataset extends the DocRED dataset for the Document-level Closed Information Extraction (DocIE) task. DocRED-IE is a multi-task dataset and allows for 5 subtasks: (i) Document-level Relation Extraction, (ii) Mention Detection, (iii) Entity Typing, (iv) Entity Disambiguation, (v) Coreference Resolution, as well as combinations thereof such as Named Entity Recognition (NER) or Entity Linking. The DocRED-IE dataset also allows for the end-to-end tasks of: (i) DocIE and (ii) Joint Entity and Relation Extraction. DocRED-IE comprises sentence-level and document-level facts, thereby describing short as well as long-range interactions within an entire document.

DrugProt corpus, where domain experts have exhaustively labeled:(a) all chemical and gene mentions, and (b) all binary relationships between them corresponding to a specific set of biologically relevant relation types (DrugProt relation classes).

The FB1.5M dataset is a benchmark for Knowledge Graph Completion. It is based on Freebase and it contains 30 relations with less than 500 triplets as low-resource relations. Source: https://arxiv.org/pdf/1911.03091.pdf

FinDKG: The Global Financial Dynamic Knowledge Graph Dataset FinDKG is an open-source dataset focused on creating a temporally-resolved Financial Dynamic Knowledge Graph. Designed to bridge the gap in industry-specific knowledge graphs, particularly in the financial sector, FinDKG provides a high-touch, temporally-aware representation of global economic and market dynamics. This repository includes comprehensive details about the dataset, methodology, and schema, aiming to facilitate academic research and actionable insights in global financial markets. ## Background While general-purpose knowledge graphs are abundant, industry-specific ones are comparatively rare, especially in the financial sector. FinDKG aims to fill this void by offering a resource for researchers and professionals looking to leverage knowledge graph technology in finance. ## FinDKG Dataset The dataset's foundation lies in an extensive news corpus curated to capture both qualitative and quantitative indicators in the financial landscape. We utilized the Wayback Machine to amass a dataset comprising global financial news. ## Dataset Structure - Temporal Knowledge Graph (TKG) with daily-resolved event triplets - Event triplets are tagged with specific timestamps corresponding to their release dates - Training, validation, and test splits organized chronologically - Weekly aggregation of event triplets as the basic unit of time ### Data Format /FinDKG is the default study dataset folder including the graph dataset and the corresponding data splits. The graph dataset is organized in the following structure: * 'train.txt', 'valid.txt', and 'test.txt': The first four columns correspond to subject, relation, object, and time. The fifth column is ignored. * 'stat.txt': The first two columns correspond to the number of entities and relations, respectively. Test set is held-out for evaluating the model performance. This should match the results of the original paper regarding the Temporal Link Prediction evaluation. /FinDKG-full: The full dataset including a larger size of the event triplets. This graph dataset adopts the same format as /FinDKG while is left for future extended research. * 'time2id.txt': This time mapping table further provided the mapping from time ID to realistic date for real-world application. ## Usage The dataset is designed for graph-based AI methods aiming to generate actionable insights in the financial domain. It is freely available for academic and research purposes. Refer details to our designated FinDKG website. (Description from Dataset Repo)

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This data set contains annotated text versions of 1635 two-page abstracts published at the Lunar and Planetary Science Conference from 1998 to 2020 of relevance to four Mars missions. The annotations were generated using named entity recognition and relation extraction provided by the MTE processing pipeline (available at https://github.com/wkiri/MTE), followed by manual review. Annotated entities include Element, Mineral, Property, and Target. Annotated relations include Contains(Target, Element | Mineral) and HasProperty(Target, Property). The extracted information (without full texts) is also available as a database (stored in .csv files) at https://pds-geosciences.wustl.edu/missions/mte/mte.htm . The description above is quoted from the source: https://zenodo.org/record/7066107#.ZAo4VOzMIW8

Medical Case Report Corpus is a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central's open access library. Source: Named Entities in Medical Case Reports: Corpus and Experiments

Multi-CrossRE is a broadest multi-lingual dataset for Relation Extraction (RE) including 26 languages in addition to English, and covering six text domains. It is a machine translated version of CrossRE crossre, with a sub-portion including more than 200 sentences in seven diverse languages checked by native speakers. Source: Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction Image Source: Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction

The Part-Whole Relations dataset is a dataset of semantic relations between entities. It contains the following subtypes: - Component-Of - Member-Of - Portion-Of - Stuff-Of - Located-In - Contained-In - Phase-Of - Participates-In Source: https://github.com/pvthuy/part-whole-relations

The corpus contains review sentences mostly of products in electronics domain, annotated and segregated into 4 comparison categories. Each comparison sentence is annotated with names of the products (PROD1 and PROD2), the aspect (ASP) and the predicate (PRED). Dataset contains sentences after auto-labeling on SNAP dataset and manually labeled sentences from the following corpora: - Jindal and Liu, 2006 - Kessler and Kuhn, 2014 - JDPA Corpus (Kessler et al, 2010)

The relational pattern similarity dataset is a new dataset upon the work of Zeichner et al. (2012), which consists of relational patterns with semantic inference labels annotated. The dataset includes 5,555 pairs extracted by Reverb (Fader et al., 2011), 2,447 pairs with inference relation and 3,108 pairs (the rest) without one. Source: Composing Distributed Representations of Relational Patterns

The dataset contains the training and test data for the SOftware Mention Detection challenge. The data is derived from the SoMeSci Knowledge Graph of software mentions. Subtask 1 deals with the recognition of software mentions and the classification of mention (e.g. Usage, Creation,...) and software types (e.g. Application, PlugIn,...) at the same time Subtask 2 requires the recognition of additional meta data of software mentions (e.g. Version, Developer, URL,...) Subtask 3 deals with extracting the relations between the different entities of interest (e.g. Version_of, License_of,...) A detailed description of the dataset including the creation and a baseline for the different subtasks can be found in the following article D. Schindler, F. Bensmann, S. Dietze, and F. Krüger, “SoMeSci—A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles,” in Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), Virtual Event, QLD, Australia: Association for Computing Machinery, Nov. 2021. doi: 10.1145/3459637.3482017.

Green family of datasets for emergent communications on relations. By comparison with other relations datasets, TexRel provides rapid training and experimentation, whilst being sufficiently large to avoid overfitting in the context of emergent communications.

This is two-hop relation extraction dataset derived from WikiHop dataset [1]. [1] Johannes Welbl and Pontus Stenetorp and Sebastian Riedel. Constructing Datasets for Multi-hop Reading Comprehension Across Documents. TACL, 2018.

533 parallel examples sampled from TACRED, translated into Russian and Korean (and 3 additional examples in Russian), accompanied with tranlsation of a list of trigger words collected for the different relations.

TurkQA consists of a selection of sentences from English Wikipedia articles, with questions and answers crowdsourced from workers on Amazon Mechanical Turk. Source: TurkQA

Artificial Relationships in Fiction ## Dataset Description Artificial Relationships in Fiction (ARF) is a synthetically annotated dataset for Relation Extraction (RE) in fiction, created from a curated selection of literary texts sourced from Project Gutenberg. The dataset captures the rich, implicit relationships within fictional narratives using a novel ontology and GPT-4o for annotation. ARF is the first large-scale RE resource designed specifically for literary texts, advancing both NLP model training and computational literary analysis. ## Dataset Configurations and Features ### Configurations - fiction_books: Metadata-rich corpus of 6,322 public domain fiction books (1850–1950) with inferred author gender and thematic categorization. - fiction_books_in_chunks: Books segmented into 5-sentence chunks (5.96M total), preserving narrative coherence via 1-sentence overlap. - fiction_books_with_relations: A subset of 95,475 text chunks annotated with 128,000+ relationships using GPT-4o and a fiction-specific ontology. ### 1. Configuration: fiction_books - Description: Contains the full text and metadata of 6,322 English-language fiction books from Project Gutenberg. - Features: - book_id: Unique Project Gutenberg ID. - title: Title of the book. - author: Author name. - author_birth_year / author_death_year: Author lifespan. - release_date: PG release date. - subjects: List of thematic topics (mapped to 51 standardized themes). - gender: Inferred author gender (via GPT-4o). - text: Cleaned full book text. - Use Case: Supports thematic and demographic analysis of literary texts. ### 2. Configuration: fiction_books_in_chunks - Description: Each book is segmented into overlapping five-sentence text chunks to enable granular NLP analysis. - Features: - book_id, chunk_index: Book and chunk identifiers. - text_chunk: Five-sentence excerpt from the book. - Use Case: Facilitates sequence-level tasks like coreference resolution or narrative progression modeling. ### 3. Configuration: synthetic_relations_in_fiction_books (ARF) - Description: This subset corresponds to the Artificial Relationships in Fiction (ARF) dataset proposed in the LaTeCH-CLfL 2025 paper "Artificial Relationships in Fiction: A Dataset for Advancing NLP in Literary Domains". - Features: - book_id, chunk_index: Identifiers. - text_chunk: Five-sentence text segment. - relations: A list of structured relation annotations, each containing: - entity1, entity2: Text spans. - entity1Type, entity2Type: Entity types based on ontology. - relation: Relationship type. - Use Case: Ideal for training and evaluating RE models in fictional narratives, studying character networks, and generating structured data from literary texts. ## ARF Dataset Structure (config 'synthetic_relations_in_fiction_books') Each annotated relation is formatted as: json { "entity1": "Head Entity text", "entity2": "Tail Entity text", "entity1Type": "Head entity type", "entity2Type": "Tail entity type", "relation": "Relation type" } Example: json { "entity1": "Vortigern", "entity2": "castle", "entity1Type": "PER", "entity2Type": "FAC", "relation": "owns" } ### Entity Types (11) | Entity Type | Description | |-------------|-------------| | PER | Person or group of people | | FAC | Facility – man-made structures for human use | | LOC | Location – natural or loosely defined geographic regions | | WTHR | Weather – atmospheric or celestial phenomena | | VEH | Vehicle – transport devices (e.g., ship, carriage) | | ORG | Organization – formal groups or institutions | | EVNT | Event – significant occurrences in narrative | | TIME | Time – chronological or historical expressions | | OBJ | Object – tangible items in the text | | SENT | Sentiment – emotional states or feelings | | CNCP | Concept – abstract ideas or motifs | ### Relation Types (48) | Relation Type | Entity 1 Type | Entity 2 Type | Description | |----------------------|------------------|-------------------|-------------------------------------------| | parent_father_of | PER | PER | Father relationship | | parent_mother_of | PER | PER | Mother relationship | | child_of | PER | PER | Child to parent | | sibling_of | PER | PER | Sibling relationship | | spouse_of | PER | PER | Spousal relationship | | relative_of | PER | PER | Extended family relationship | | adopted_by | PER | PER | Adopted by another person | | companion_of | PER | PER | Companionship or ally | | friend_of | PER | PER | Friendship | | lover_of | PER | PER | Romantic relationship | | rival_of | PER | PER | Rivalry | | enemy_of | PER/ORG | PER/ORG | Hostile or antagonistic relationship | | inspires | PER | PER | Inspires or motivates | | sacrifices_for | PER | PER | Makes a sacrifice for | | mentor_of | PER | PER | Mentorship or guidance | | teacher_of | PER | PER | Formal teaching relationship | | protector_of | PER | PER | Provides protection to | | employer_of | PER | PER | Employment relationship | | leader_of | PER | ORG | Leader of an organization | | member_of | PER | ORG | Membership in an organization | | lives_in | PER | FAC/LOC | Lives in a location | | lived_in | PER | TIME | Historically lived in | | visits | PER | FAC | Visits a facility | | travel_to | PER | LOC | Travels to a location | | born_in | PER | LOC | Birthplace | | travels_by | PER | VEH | Travels by a vehicle | | participates_in | PER | EVNT | Participates in an event | | causes | PER | EVNT | Causes an event | | owns | PER | OBJ | Owns an object | | believes_in | PER | CNCP | Believes in a concept | | embodies | PER | CNCP | Embodies a concept | | located_in | FAC | LOC | Located in a place | | part_of | FAC/LOC/ORG | FAC/LOC/ORG | Part of a larger entity | | owned_by | FAC/VEH | PER | Owned by someone | | occupied_by | FAC | PER | Occupied by someone | | used_by | FAC | ORG | Used by an organization | | affects | WTHR | LOC/EVNT | Weather affects location or event | | experienced_by | WTHR | PER | Weather experienced by someone | | travels_in | VEH | LOC | Vehicle travels in a location | | based_in | ORG | LOC | Organization based in a location | | attended_by | EVNT | PER | Event attended by person | | ends_in | EVNT | TIME | Event ends at a time | | occurs_in | EVNT | LOC/TIME | Event occurs in a place or time | | features | EVNT | OBJ | Event features an object | | stored_in | OBJ | LOC/FAC | Object stored in a place | | expressed_by | SENT | PER | Sentiment expressed by person | | used_by | OBJ | PER | Object used by person | | associated_with | CNCP | EVNT | Concept associated with event | ## Dataset Statistics | Metric | Value | |----------------------------|------------| | Books | 96 | | Authors | 91 | | Gender Ratio (M/F) | 55% / 45% | | Subgenres | 51 | | Annotated Chunks | 95,475 | | Relations per Chunk | 1.34 avg | | Chunks with No Relations | 35,230 | | Total Relations | ~128,000 | ## Methodology - Source Texts: English-language fiction from PG bookshelves: Fiction, Children & YA, Crime/Mystery. - Annotation Model: GPT-4o via custom prompt integrating strict ontologies. - Sampling: Balanced author gender and thematic distributions. - Ontology Adherence: <0.05% deviation for entities; 2.95% for relations. - Format: Structured JSON, optimized for NLP pipelines. ## Applications - Fine-tuning RE Models: Adapt models to literary domains with implicit, evolving relationships. - Computational Literary Studies: Analyze character networks, thematic evolution, and genre patterns. - Creative AI: Enhance AI-driven storytelling, character consistency, and world-building tools. ## Citation If you use this dataset in your research, please cite: bibtex @inproceedings{christou-tsoumakas-2025-artificial, title = "Artificial Relationships in Fiction: A Dataset for Advancing {NLP} in Literary Domains", author = "Christou, Despina and Tsoumakas, Grigorios", editor = "Kazantseva, Anna and Szpakowicz, Stan and Degaetano-Ortlieb, Stefania and Bizzoni, Yuri and Pagel, Janis", booktitle = "Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)", month = may, year = "2025", address = "Albuquerque, New Mexico", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.latechclfl-1.13/", pages = "130--147", ISBN = "979-8-89176-241-1" }