Search Results for author:
Found 35 papers, 13 papers with code
Fine-tuning and Prompt Engineering with Cognitive Knowledge Graphs for Scholarly Knowledge Organization
Our method involves fine-tuning LLMs with CKG knowledge and additionally injecting knowledge from a CKG with a novel prompting technique significantly increasing the accuracy of scholarly knowledge extraction.
Instruction Finetuning for Leaderboard Generation from Empirical AI Research
This study demonstrates the application of instruction finetuning of pretrained Large Language Models (LLMs) to automate the generation of AI research leaderboards, extracting (Task, Dataset, Metric, Score) quadruples from articles.
Large Language Models as Evaluators for Scientific Synthesis
Our study explores how well the state-of-the-art Large Language Models (LLMs), like GPT-4 and Mistral, can assess the quality of scientific summaries or, more fittingly, scientific syntheses, comparing their evaluations to those of human annotators.
Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach.
Exploring the Latest LLMs for Leaderboard Extraction
The rapid advancements in Large Language Models (LLMs) have opened new avenues for automating complex tasks in AI research.
A FAIR and Free Prompt-based Research Assistant
This demo will present the Research Assistant (RA) tool developed to assist with six main types of research tasks defined as standardized instruction templates, instantiated with user input, applied finally as prompts to well-known--for their sophisticated natural language processing abilities--AI tools, such as ChatGPT (https://chat. openai. com/) and Gemini (https://gemini. google. com/app).
Astro-NER -- Astronomy Named Entity Recognition: Is GPT a Good Domain Expert Annotator?
In this study, we address one of the challenges of developing NER models for scholarly domains, namely the scarcity of suitable labeled data.
Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph
Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers' contributions in a structured manner, but this is labor-intensive and inconsistent between the domain expert human curators.
LLMs4OM: Matching Ontologies with Large Language Models
Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing.
Toward FAIR Semantic Publishing of Research Dataset Metadata in the Open Research Knowledge Graph
We design a specific application of the ORKG-Dataset semantic model based on 40 diverse research datasets on scientific information extraction.
From Keywords to Structured Summaries: Streamlining Scholarly Knowledge Access
This short paper highlights the growing importance of information retrieval (IR) engines in the scientific community, addressing the inefficiency of traditional keyword-based search engines due to the rising volume of publications.
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology
In this paper, we champion the use of structured and semantic content representation of discourse-based scholarly communication, inspired by tools like Wikipedia infoboxes or structured Amazon product descriptions.
Toward Semantic Publishing in Non-Invasive Brain Stimulation: A Comprehensive Analysis of rTMS Studies
Noninvasive brain stimulation (NIBS) encompasses transcranial stimulation techniques that can influence brain excitability.
Procedural Text Mining with Large Language Models
Recent advancements in the field of Natural Language Processing, particularly the development of large-scale language models that are pretrained on vast amounts of knowledge, are creating novel opportunities within the realm of Knowledge Engineering.
LLMs4OL: Large Language Models for Ontology Learning
LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains.
Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph
There have been many recent investigations into prompt-based training of transformer language models for new text genres in low-resource settings.
ORKG-Leaderboards: A Systematic Workflow for Mining Leaderboards as a Knowledge Graph
Furthermore, the system is integrated with the Open Research Knowledge Graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings.
Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections
To address these limitations, we started by creating OCR-noisy texts based on three clean corpora.
Zero-shot Entailment of Leaderboards for Empirical AI Research
We present a large-scale empirical investigation of the zero-shot learning phenomena in a specific recognizing textual entailment (RTE) task category, i. e. the automated mining of leaderboards for Empirical AI Research.
Clustering Semantic Predicates in the Open Research Knowledge Graph
When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i. e. predicates and resources).
Overview of STEM Science as Process, Method, Material, and Data Named Entities
The STEM-NER-60k corpus, created in this work, comprises over 1M extracted entities from 60k STEM articles obtained from a major publishing platform and is publicly released https://github. com/jd-coderepos/stem-ner-60k.
Computer Science Named Entity Recognition in the Open Research Knowledge Graph
Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can beset the task and has been less studied than NER in the general domain.
The Digitalization of Bioassays in the Open Research Knowledge Graph
For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions.
Easy Semantification of Bioassays
Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries.
Ranking Facts for Explaining Answers to Elementary Science Questions
In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice.
Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles
We describe a rule-based approach for the automatic acquisition of salient scientific entities from Computational Linguistics (CL) scholarly article titles.
Automated Mining of Leaderboards for Empirical AI Research
In this regard, the Leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge.
SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph
Being the first-of-its-kind in the SemEval series, the task released structured data from NLP scholarly articles at three levels of information granularity, i. e. at sentence-level, phrase-level, and phrases organized as triples toward Knowledge Graph (KG) building.
Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions -- A Trial Dataset
To this end, specifically, care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.
SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph
As a novel contribution to the problem of semantifying biological assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions.
NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature
We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks.
Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification
With the rapid growth of research publications, there is a vast amount of scholarly knowledge that needs to be organized in digital libraries.
The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources
We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1. 0 (STEM-ECR v1. 0).
Domain-independent Extraction of Scientific Concepts from Research Articles
We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions.
Ranked #1 on
Scientific Concept Extraction
on STM-corpus
Open Research Knowledge Graph: Next Generation Infrastructure for Semantic Scholarly Knowledge
In this paper, we present the first steps towards a knowledge graph based infrastructure that acquires scholarly knowledge in machine actionable form thus enabling new possibilities for scholarly knowledge curation, publication and processing.
