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8 results for “Knowledge Graph Generation”
MUHAI Benchmark : Task 1 (Short story generation with Knowledge Graphs)
<p><strong>Meaning and Understanding in Human-Centric AI (MUHAI) Benchmark<br> Task 1 (Short story generation with Knowledge Graphs and Language Models)</strong> </p> <p>The dataset can be used to test understandability of text generated through the combination of knowledge graphs and language models without using knowledge graph embeddings.<br> <br> The task here is to generate 5-sentence stories from a set of <em>subject-predicate-object</em> triples that are extracted from a knowledge graph. Two steps need to be performed:</p> <p>1. Language model fine-tuning (SVO triple extraction + model fine-tuning)<br> 2. Story generation (knowledge enrichment + text generation) <br> <br> The submission includes the following data:</p> <ol> <li>Original ROC stories corpus (100 stories)</li> <li>ROC stories encoded with relevant triples (extracted through SpaCy, 2 versions, with and without coreference resolution)</li> <li>Stories generated by the pre-trained model (GPT2-simple)</li> <li>Stories generated by the fine-tuned model (DICE + ConceptNet + DBpedia )</li> <li>Stories generated by the fine-tuned model (DICE + ConceptNet + DBpedia + WordNet )</li> <li>Stories generated by the GPT-2-keyword-generation (an open-source software that uses GPT-2 to generate text pertaining to the specified keywords)</li> <li>Model results</li> <li>Evaluation metrics description</li> <li>User-evaluation questionnaire </li> </ol> <p>Code : https://github.com/kmitd/muhai-dice_story</p>
PrimeKGQA, the dataset from paper: Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset
<p>Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total,<br>we create 83999 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA system.</p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 3. Representation of the grammar G1 in the labelled graph G0 1
<p>If we take the labeled graph G0 1 given in Figure 3 and construct the stratified graph structure over (99) such that (100) we obtain (101), (102). </p> <p>In this paper, we proposed a new system for formal language generation by means of stratified graphs structures. This mechanism can generate languages of the first type and of the second type. More precisely, we propose a new system for formal language generation by means of a system of knowledge based on stratified graphs. We exemplified that, using an interpretation system specially defined for stratified graphs representations, a particular formal language can be obtained by means of the resulted accepted structured paths.</p>
A Benchmark Dataset with Knowledge Graph Generation for Industry 4.0 Production Lines
<p>A benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how I4.0 industry can benefit from KGs and semantic datasets. This work is a result of collaborations with the production line managers, supervisors, and engineers of a football industry to acquire realistic production line data. Knowledge Graphs (KGs) or a Knowledge Graph (KG) emerged as a significant technology to store the semantics of the domain entities. The data is mapped and populated with RGOM classes and relations using an automated solution based on JenaAPI, producing an I4.0 KG. <br> <br> Usage:<br> <br> Recently, we use this dataset to analyze the performance of the five state-of-the-art KG embedding models, namely ComplEx, DistMult,TransE, ConvKB, and ConvE. We evaluated the models using two key metrics: Mean Reciprocal Rank (MRR), and Hits@N (Hits@10, Hits@3, and Hits@1). We observed that the TransE model outperforms other models, followed by ComplEx and DistMult, with ConvE demonstrating the lowest performance. Similarly, the dataset can be used alternatively in other potential scenarios.</p>
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
<p>This is the repository for ISWC 2023 Resource Track submission for <code>Text2KGBench: Benchmark for Ontology-Driven Knowledge Graph Generation from Text</code>. Text2KGBench is a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences.</p> <p>It contains two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences.</p> <p><strong>An example</strong></p> <p>An example test sentence:</p> <pre><code>Test Sentence: {"id": "ont_music_test_n", "sent": "\"The Loco-Motion\" is a 1962 pop song written by American songwriters Gerry Goffin and Carole King."} </code></pre> <p>An example of ontology:</p> <p>Ontology: <a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ontologies/owl/ont_2_music.ttl">Music Ontology</a></p> <p>Expected Output:</p> <pre><code>{ "id": "ont_k_music_test_n", "sent": "\"The Loco-Motion\" is a 1962 pop song written by American songwriters Gerry Goffin and Carole King.", "triples": [ { "sub": "The Loco-Motion", "rel": "publication date", "obj": "01 January 1962" },{ "sub": "The Loco-Motion", "rel": "lyrics by", "obj": "Gerry Goffin" },{ "sub": "The Loco-Motion", "rel": "lyrics by", "obj": "Carole King" },] } </code></pre> <p>The data is released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY 4.0) License.</p> <p>The structure of the repo is as the following.</p> <ul> <li>Text2KGBench <ul> <li>src: the source code used for generation and evaluation, and baseline <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/benchmark"><code>benchmark</code></a> the code used to generate the benchmark</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/evaluation"><code>evaluation</code></a> evaluation scripts for calculating the results</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/evaluation">baseline</a> code for generating the baselines including prompts, sentence similarities, and LLM client.</li> </ul> </li> <li>data: the benchmark datasets and baseline data. There are two datasets: wikidata_tekgen and dbpedia_webnlg. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen">wikidata_tekgen</a> Wikidata-TekGen Dataset <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ontologies">ontologies</a> 10 ontologies used by this dataset</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/train">train</a> training data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/test">test</a> test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/manually_verified_sentences">manually_verified_sentences</a> ids of a subset of test cases manually validated</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences">unseen_sentences</a> new sentences that are added by the authors which are not part of Wikipedia <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences/test">test unseen</a> test unseen test sentences</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences/ground_truth">ground_truth</a> ground truth for unseen test sentences.</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ground_truth">ground_truth</a> ground truth for the test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines">baselines</a> data related to running the baselines. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/test_train_sent_similarity">test_train_sent_similarity</a> for each test case, 5 most similar train sentences generated using SBERT T5-XXL model.</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/prompts">prompts</a> prompts corresponding to each test file <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/prompts/unseen">unseen prompts</a> unseen prompts for the unseen test cases</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B">Alpaca-LoRA-13B</a> data related to the Alpaca-LoRA model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen">unseen results</a> results for the unseen test cases <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B">Vicuna-13B</a> data related to the Vicuna-13B model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg">dbpedia_webnlg</a> DBpedia Dataset <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/ontologies">ontologies</a> 19 ontologies used by this dataset</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/train">train</a> training data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/test">test</a> test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/ground_truth">ground_truth</a> ground truth for the test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines">baselines</a> data related to running the baselines. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/test_train_sent_similarity">test_train_sent_similarity</a> for each test case, 5 most similar train sentences generated using SBERT T5-XXL model.</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/prompts">prompts</a> prompts corresponding to each test file</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B">Alpaca-LoRA-13B</a> data related to the Alpaca-LoRA model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B">Vicuna-13B</a> data related to the Vicuna-13B model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>This benchmark contains data derived from the TekGen corpus (part of the KELM corpus) [1] released under CC BY-SA 2.0 license and WebNLG 3.0 corpus [2] released under CC BY-NC-SA 4.0 license.</p> <p>[1] Oshin Agarwal, Heming Ge, Siamak Shakeri, and Rami Al-Rfou. 2021. Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3554–3565, Online. Association for Computational Linguistics.</p> <p>[2] Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017. Creating Training Corpora for NLG Micro-Planners. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 179–188, Vancouver, Canada. Association for Computational Linguistics.</p>
The knowledge graphs generated from CRE and The Session datasets
<p>This dataset contains the knowledge graphs generated from CRE and the Session datasets. All the knowledge graphs are in TTL format.</p>
Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders
<p>Dataset for the Knowledge graph used in the paper "<a href="https://arxiv.org/abs/2405.17044">Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders</a>" by Xueme Gu and Mario Krenn. </p> <p>Nodes represent scientific concepts extracted from 2.44 million paper titles and abstracts and edges are formed when two concepts co-occur in titles or abstracts of over 58 million papers from OpenAlex, augmented with citation information. </p>
Mobile Feature-oriented Knowledge Base Generation Using Knowledge Graphs
<p>Sample study data set and derived metadata and evaluation files for the research titled 'Mobile Feature-oriented Knowledge Base Generation Using Knowledge Graphs' (MApp-KG resource)</p>
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