Search Results for author: Elena Simperl

Found 16 papers, 9 papers with code

PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking

1 code implementation Findings (NAACL) 2022 Mubashara Akhtar, Oana Cocarascu, Elena Simperl

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.

Fact Checking

Using Large Language Models for Knowledge Engineering (LLMKE): A Case Study on Wikidata

1 code implementation15 Sep 2023 Bohui Zhang, Ioannis Reklos, Nitisha Jain, Albert Meroño Peñuela, Elena Simperl

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.

Knowledge Probing

Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking

no code implementations27 Jan 2023 Mubashara Akhtar, Oana Cocarascu, Elena Simperl

Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text, tables, images, audio, or video.

Fact Checking

ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources

no code implementations26 Oct 2022 Gabriel Amaral, Odinaldo Rodrigues, Elena Simperl

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.

Binary Classification Claim Verification +1

A Decade of Knowledge Graphs in Natural Language Processing: A Survey

1 code implementation30 Sep 2022 Phillip Schneider, Tim Schopf, Juraj Vladika, Mikhail Galkin, Elena Simperl, Florian Matthes

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.

Knowledge Graphs

Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs

no code implementations17 Jun 2022 Gabriel Amaral, Mārcis Pinnis, Inguna Skadiņa, Odinaldo Rodrigues, Elena Simperl

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.

Knowledge Graphs Machine Translation +2

WDV: A Broad Data Verbalisation Dataset Built from Wikidata

1 code implementation5 May 2022 Gabriel Amaral, Odinaldo Rodrigues, Elena Simperl

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

no code implementations20 Sep 2021 Gabriel Amaral, Alessandro Piscopo, Lucie-Aimée Kaffee, Odinaldo Rodrigues, Elena Simperl

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

no code implementations13 Jul 2021 Kholoud Alghamdi, Miaojing Shi, Elena Simperl

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.

Collaborative Filtering Recommendation Systems

Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion Lexicon

1 code implementation4 Oct 2017 Giannis Haralabopoulos, Elena Simperl

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.

Sentiment Analysis

A Neural Network Approach for Knowledge-Driven Response Generation

1 code implementation COLING 2016 Pavlos Vougiouklis, Jonathon Hare, Elena Simperl

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.

Response Generation

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