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Found 24 papers, 16 papers with code
PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking
Inspired by human fact checkers, who use different types of evidence (e. g. tables, images, audio) in addition to text, several datasets with tabular evidence data have been released in recent years.
Methods to Assess the UK Government's Current Role as a Data Provider for AI
Governments typically collect and steward a vast amount of high-quality data on their citizens and institutions, and the UK government is exploring how it can better publish and provision this data to the benefit of the AI landscape.
Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting
Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, especially in large projects.
Croissant: A Metadata Format for ML-Ready Datasets
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point.
A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation
In this study, we conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples.
Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs
Conversational question answering systems often rely on semantic parsing to enable interactive information retrieval, which involves the generation of structured database queries from a natural language input.
ChartCheck: Explainable Fact-Checking over Real-World Chart Images
Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked.
Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
Thus, understanding and reasoning with numbers are essential skills for language models to solve different tasks.
Using Large Language Models for Knowledge Engineering (LLMKE): A Case Study on Wikidata
In this work, we explore the use of Large Language Models (LLMs) for knowledge engineering tasks in the context of the ISWC 2023 LM-KBC Challenge.
Multimodal Automated Fact-Checking: A Survey
In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation.
Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text, tables, images, audio, or video.
Exploring and Eliciting Needs and Preferences from Editors for Wikidata Recommendations
Our aim with this paper is to elicit the user requirements for a Wikidata recommendations system.
ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources
Knowledge Graphs are repositories of information that gather data from a multitude of domains and sources in the form of semantic triples, serving as a source of structured data for various crucial applications in the modern web landscape, from Wikipedia infoboxes to search engines.
A Decade of Knowledge Graphs in Natural Language Processing: A Survey
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry.
Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs
However, such labels are not guaranteed to match across languages from an information consistency standpoint, greatly compromising their usefulness for fields such as machine translation.
WDV: A Broad Data Verbalisation Dataset Built from Wikidata
Data verbalisation is a task of great importance in the current field of natural language processing, as there is great benefit in the transformation of our abundant structured and semi-structured data into human-readable formats.
Assessing the quality of sources in Wikidata across languages: a hybrid approach
Wikidata is one of the most important sources of structured data on the web, built by a worldwide community of volunteers.
Learning to Recommend Items to Wikidata Editors
The system uses a hybrid of content-based and collaborative filtering techniques to rank items for editors relying on both item features and item-editor previous interaction.
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata
While Wikipedia exists in 287 languages, its content is unevenly distributed among them.
Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples
We explore the problem of generating natural language summaries for Semantic Web data.
Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion Lexicon
For these methods to work, they require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the range of emotions it captures diversity.
A Neural Network Approach for Knowledge-Driven Response Generation
Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space.
