Knowgl: Knowledge generation and linking from text
G Rossiello, MFM Chowdhury… - Proceedings of the …, 2023 - ojs.aaai.org
We propose KnowGL, a tool that allows converting text into structured relational data
represented as a set of ABox assertions compliant with the TBox of a given Knowledge …
represented as a set of ABox assertions compliant with the TBox of a given Knowledge …
A universal question-answering platform for knowledge graphs
Knowledge from diverse application domains is organized as knowledge graphs (KGs) that
are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well …
are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well …
Harnessing Knowledge and Reasoning for Human-Like Natural Language Generation: A Brief Review
The rapid development and application of natural language generation (NLG) techniques
has revolutionized the field of automatic text production. However, these techniques are still …
has revolutionized the field of automatic text production. However, these techniques are still …
Applying a generic sequence-to-sequence model for simple and effective keyphrase generation
In recent years, a number of keyphrase generation (KPG) approaches were proposed
consisting of complex model architectures, dedicated training paradigms and decoding …
consisting of complex model architectures, dedicated training paradigms and decoding …
Knowledge graph induction enabling recommending and trend analysis: a corporate research community use case
N Mihindukulasooriya, M Sava, G Rossiello… - International Semantic …, 2022 - Springer
A research division plays an important role of driving innovation in an organization. Drawing
insights, following trends, keeping abreast of new research, and formulating strategies are …
insights, following trends, keeping abreast of new research, and formulating strategies are …
SPARKLE: Enhancing SPARQL Generation with Direct KG Integration in Decoding
J Lee, H Shin - arXiv preprint arXiv:2407.01626, 2024 - arxiv.org
Existing KBQA methods have traditionally relied on multi-stage methodologies, involving
tasks such as entity linking, subgraph retrieval and query structure generation. However …
tasks such as entity linking, subgraph retrieval and query structure generation. However …
[PDF][PDF] The combination of BERT and data oversampling for answer type prediction
In this paper, we address the Task 1 (of the SMART Task 2021) of predicting the answer
categories and types based on target ontologies, which could be useful in knowledge-based …
categories and types based on target ontologies, which could be useful in knowledge-based …
Hierarchical pattern-based complex query of temporal knowledge graph
L Zhu, H Zhang, L Bai - Knowledge-Based Systems, 2024 - Elsevier
The complex query is an essential type of knowledge graph query, which aims to deal with
the complex relationships among multiple triples involved in query problems. However, the …
the complex relationships among multiple triples involved in query problems. However, the …
Learning to transpile AMR into SPARQL
We propose a transition-based system to transpile Abstract Meaning Representation (AMR)
into SPARQL for Knowledge Base Question Answering (KBQA). This allows us to delegate …
into SPARQL for Knowledge Base Question Answering (KBQA). This allows us to delegate …
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning
An important open question pertaining to the use of large language models for knowledge-
intensive tasks is how to effectively integrate knowledge from three sources: the model's …
intensive tasks is how to effectively integrate knowledge from three sources: the model's …