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Found 42 papers, 15 papers with code
MC-NEST -- Enhancing Mathematical Reasoning in Large Language Models with a Monte Carlo Nash Equilibrium Self-Refine Tree
Mathematical reasoning has proven to be a critical yet challenging task for large language models (LLMs), as they often struggle with complex multi-step problems.
NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering
The growing volume of biomedical scholarly document abstracts presents an increasing challenge in efficiently retrieving accurate and relevant information.
LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesis
In response to the growing complexity and volume of scientific literature, this paper introduces the LLMs4Synthesis framework, designed to enhance the capabilities of Large Language Models (LLMs) in generating high-quality scientific syntheses.
LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge
This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge.
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.
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.
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.
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.
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.
Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community - Semantic Web Machine Learning (SWeML for short).
MORTY: Structured Summarization for Targeted Information Extraction from Scholarly Articles
Information extraction from scholarly articles is a challenging task due to the sizable document length and implicit information hidden in text, figures, and citations.
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).
Plumber: A Modular Framework to Create Information Extraction Pipelines
Information Extraction (IE) tasks are commonly studied topics in various domains of research.
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.
Triple Classification for Scholarly Knowledge Graph Completion
Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications.
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.
Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines
In the last decade, a large number of Knowledge Graph (KG) information extraction approaches were proposed.
Analysing the Requirements for an Open Research Knowledge Graph: Use Cases, Quality Requirements and Construction Strategies
Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work.
Metadata Analysis of Open Educational Resources
Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning.
Computers and Society
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.
Question Answering on Scholarly Knowledge Graphs
Our system can retrieve direct answers to a variety of different questions asked on tabular data in articles.
Requirements Analysis for an Open Research Knowledge Graph
Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get an overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work.
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
The Query Translation Landscape: a Survey
In particular, we study which query language is a most suitable candidate for that 'universal' query language.
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.
Towards a Knowledge Graph based Speech Interface
Applications which use human speech as an input require a speech interface with high recognition accuracy.
Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition
In this paper, we have tried to see the semantic relatedness between the words in a sentence to rescore the N-best list.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Git4Voc: Git-based Versioning for Collaborative Vocabulary Development
Collaborative vocabulary development in the context of data integration is the process of finding consensus between the experts of the different systems and domains.
