KG-to-Text Generation

The WebQuestions dataset is a question answering dataset using Freebase as the knowledge base and contains 6,642 question-answer pairs. It was created by crawling questions through the Google Suggest API, and then obtaining answers using Amazon Mechanical Turk. The original split uses 3,778 examples for training and 2,032 for testing. All answers are defined as Freebase entities. Example questions (answers) in the dataset include “Where did Edgar Allan Poe died?” (baltimore) or “What degrees did Barack Obama get?” (bachelor_of_arts, juris_doctor). Source: Question Answering with Subgraph Embeddings Image Source: Berant et al

The WebNLG corpus comprises of sets of triplets describing facts (entities and relations between them) and the corresponding facts in form of natural language text. The corpus contains sets with up to 7 triplets each along with one or more reference texts for each set. The test set is split into two parts: seen, containing inputs created for entities and relations belonging to DBpedia categories that were seen in the training data, and unseen, containing inputs extracted for entities and relations belonging to 5 unseen categories. Initially, the dataset was used for the WebNLG natural language generation challenge which consists of mapping the sets of triplets to text, including referring expression generation, aggregation, lexicalization, surface realization, and sentence segmentation. The corpus is also used for a reverse task of triplets extraction. Versioning history of the dataset can be found here. Source: Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation Image Source: https://paperswithcode.com/paper/creating-training-corpora-for-nlg-micro/ It's also available here: https://huggingface.co/datasets/web_nlg Note: "The v3 release (release_v3.0_en, release_v3.0_ru) for the WebNLG2020 challenge also supports a semantic parsing task."

Abstract GENeration DAtaset (AGENDA) is a dataset of knowledge graphs paired with scientific abstracts. The dataset consists of 40k paper titles and abstracts from the Semantic Scholar Corpus taken from the proceedings of 12 top AI conferences. Source: Text Generation from Knowledge Graphs with Graph Transformers

GenWiki is a large-scale dataset for knowledge graph-to-text (G2T) and text-to-knowledge graph (T2G) conversion. It is introduced in the paper "GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation" by Zhijing Jin, Qipeng Guo, Xipeng Qiu, and Zheng Zhang at COLING 2020.

Adopts two subsets of Freebase (Bollacker et al., 2008) as Knowledge Bases to construct the PathQuestion (PQ) and the PathQuestion-Large (PQL) datasets. Paths are extracted between two entities which span two hops (es → r1 → e1 → r2 → a, denoted by -2H) or three hops (es→ r1 → e1 →r2 → e2→ r3 → a, denoted by -3H) and then generated natural language questions with templates. To make the generated questions analogical to real-world questions, paraphrasing templates and synonyms for relations are included by searching the Internet and two real-world datasets, WebQuestions (Berant et al., 2013) and WikiAnswers (Fader et al., 2013). In this way, the syntactic structure and surface wording of the generated questions have been greatly enriched. Source: An Interpretable Reasoning Network for Multi-Relation Question Answering

WikiGraphs is a dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark with a subgraph from the Freebase knowledge graph. This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets.

EventNarrative is a knowledge graph-to-text dataset from publicly available open-world knowledge graphs. EventNarrative consists of approximately 230,000 graphs and their corresponding natural language text.

It consists of an extensive collection of a high quality cross-lingual fact-to-text dataset in 11 languages: Assamese (as), Bengali (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Oriya (or), Punjabi (pa), Tamil (ta), Telugu (te), and monolingual dataset in English (en). This is the Wikipedia text <--> Wikidata KG aligned corpus used to train the data-to-text generation model. The Train & validation splits are created using distant supervision methods and Test data is generated through human annotations. ## Data Format Dataset is publicly available here. Each directory contains language specific dataset (refered through language ISO code) and contains of three files: - train.jsonl - test.jsonl - val.jsonl Data stored in the above files are of JSON Line (jsonl) format. ### Record structure (JSON structure) Each record consist of the following entries: - sentence (string) : Native language wikipedia sentence. (non-native language strings were removed.) - facts (List[Dict]) : List of facts associated with the sentence where each fact is stored as dictionary. - language (string) : Language identifier. The facts key contains list of facts where each facts is stored as dictionary. A single record within fact list contains following entries: - subject (string) : central entity. - object (string) : entity or a piece of information about the subject. - predicate (string) : relationship that connects the subject and the object. - qualifiers (List[Dict]) : It provide additional information about the fact, is stored as list of qualifier where each record is a dictionary. The dictionary contains two keys: qualifier_predicate to represent property of qualifer and qualifier_object to store value for the qualifier's predicate. ### Examples Example from English dataset { "sentence": "Mark Paul Briers (born 21 April 1968) is a former English cricketer.", "facts": [ { "subject": "Mark Briers", "predicate": "date of birth", "object": "21 April 1968", "qualifiers": [] }, { "subject": "Mark Briers", "predicate": "occupation", "object": "cricketer", "qualifiers": [] }, { "subject": "Mark Briers", "predicate": "country of citizenship", "object": "United Kingdom", "qualifiers": [] } ], "language": "en" } Example from one of the low-resource languages (i.e. Hindi) { "sentence": "बोरिस पास्तेरनाक १९५८ में साहित्य के क्षेत्र में नोबेल पुरस्कार विजेता रहे हैं।", "facts": [ { "subject": "Boris Pasternak", "predicate": "nominated for", "object": "Nobel Prize in Literature", "qualifiers": [ { "qualifier_predicate": "point in time", "qualifier_subject": "1958" } ] } ], "language": "hi" }

ENT-DESC involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions. Source: ENT-DESC: Entity Description Generation by Exploring Knowledge Graph