The LODeXporter: Flexible Generation of Linked Open Data Triples from NLP Frameworks for Automatic Knowledge Base Construction (L18-1)
Copied to clipboard
| Challenge: | Linked Open Data (LOD) principles are used to export natural language processing (NLP) results to graph-based knowledge base. |
| Approach: | They propose a method for exporting NLP results to a graph-based knowledge base using Linked Open Data principles. |
| Outcome: | The proposed method is available as an open source component for the GATE framework and is available on GitHub. |
Similar Papers
Fintan - Flexible, Integrated Transformation and Annotation eNgineering (2020.lrec-1)
Copied to clipboard
| Challenge: | Fintan is a platform for converting heterogeneous linguistic resources to RDF. |
| Approach: | They introduce Fintan for converting heterogeneous linguistic resources to RDF with its modular architecture, workflow management and visualization features. |
| Outcome: | The Fintan platform is designed to transform linguistic resources to graphs and graphs. |
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data (P18-1)
Copied to clipboard
| Challenge: | Knowledge bases are becoming an enabling resource for many applications including Q&A systems, recommender systems, and summarization tools. |
| Approach: | They propose a system to translate RDF triples into natural sentences using an encoder-decoder framework. |
| Outcome: | The proposed model outperforms the baseline model by 17.6%, 6.0%, and 16.4% in terms of BLEU, METEOR, and TER scores. |
Teanga: A Linked Data based platform for Natural Language Processing (L18-1)
Copied to clipboard
| Challenge: | Using linked data, we can use many NLP services from a single interface . integrating components within a development model is endemic to software development . |
| Approach: | They propose a linked data based platform for natural language processing that uses linked data to define the types of services input and output. |
| Outcome: | The proposed platform is easy to install and run, easy to use and able to run multiple NLP tasks from one interface. |
LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) represent real-world entities and their relations in a structured form. |
| Approach: | They propose a framework that performs triple-level refinement on KGs constructed via GKE. |
| Outcome: | The proposed framework improves KG quality from diverse perspectives. |
G3R: A Graph-Guided Generate-and-Rerank Framework for Complex and Cross-domain Text-to-SQL Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to complex and cross-domain Text-to-SQL generation lack domain knowledge . domain knowledge is not incorporated to enhance their ability to generalise to unseen databases. |
| Approach: | They propose a framework called G3R for complex and cross-domain Text-to-SQL generation . they propose re-ranking SQL queries based on domain knowledge and a graph-guided SQL generator . |
| Outcome: | The proposed framework achieves state-of-the-art results on the Spider and Spider-DK benchmarks. |
Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction (L18-1)
Copied to clipboard
| Challenge: | Currently, the construction and updating of knowledge bases rely on human labor. |
| Approach: | They propose to map relational phrases in triples from natural language to knowledge base predicate format. |
| Outcome: | The proposed mapping results show high quality and promising coverage on relational phrases compared to previous research. |
Tilde MT Platform for Developing Client Specific MT Solutions (L18-1)
Copied to clipboard
| Challenge: | a growing demand for translations and multilingual content is surpassing the supply of professional translation services. |
| Approach: | They present a custom machine translation platform called Tilde MT that provides linguistic data storage, data cleaning and normalisation, statistical and neural machine translation system training and hosting functionality. |
| Outcome: | The proposed platform provides linguistic data storage, data cleaning and normalisation, statistical and neural machine translation system training and hosting functionality, and wide integration capabilities. |
Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for knowledge graph creation (KGC) are limited in their ability to scale up to text common in many real-world applications. |
| Approach: | They propose a framework for knowledge graph creation from input text using a pre-defined schema and a trained component that retrieves schema elements relevant to the input text. |
| Outcome: | The proposed framework extract-define-canonicalize extracts high-quality triplets with a succinct self-generated schema without any parameter tuning and with significantly larger schemas compared to prior works. |
LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models (2024.acl-demos)
Copied to clipboard
| Challenge: | Large language models (LLMs) are used for many computational analyses, but approximate string matching packages are not widely used in social science applications. |
| Approach: | The open-source package LinkTransformer provides an end-to-end software for performing record linkage and other data cleaning tasks with transformer LLMs. |
| Outcome: | The open-source package LinkTransformer outperforms standard methods in a variety of languages and settings. |
R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (2024.findings-emnlp)
Copied to clipboard
Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai
| Challenge: | Adapting existing approaches for converting natural language to SQL encounters hurdles due to distinct nature of GQL compared to SQL. |
| Approach: | They propose a method that integrates both small and large Foundation Models for ranking, rewriting, and refining tasks. |
| Outcome: | The proposed approach integrates both small and large Foundation Models for ranking, rewriting, and refining tasks while capitalizing on the superior generalization and query generation prowess of larger models for the final transformation of natural language queries into GQL formats. |