Clustering Semantic Predicates in the Open Research Knowledge Graph

• URL: http://arxiv.org/abs/2210.02034v1
• Date: Wed, 5 Oct 2022 05:48:39 GMT
• Title: Clustering Semantic Predicates in the Open Research Knowledge Graph
• Authors: Omar Arab Oghli, Jennifer D'Souza, S\"oren Auer
• Abstract summary: We describe our approach tailoring two AI-based clustering algorithms for recommending predicates about resources in the Open Research Knowledge Graph (ORKG) Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. This work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.

Related papers

• Exploiting Large Language Models Capabilities for Question Answer-Driven Knowledge Graph Completion Across Static and Temporal Domains [8.472388165833292]
  This paper introduces a new generative completion framework called Generative Subgraph-based KGC (GS-KGC) GS-KGC employs a question-answering format to directly generate target entities, addressing the challenge of questions having multiple possible answers. Our method generates negative samples using known facts to facilitate the discovery of new information.
  arXiv  Detail & Related papers  (2024-08-20T13:13:41Z)
• Knowledge Graph Completion using Structural and Textual Embeddings [0.0]
  We propose a relations prediction model that harnesses both textual and structural information within Knowledge Graphs. Our approach integrates walks-based embeddings with language model embeddings to effectively represent nodes. We demonstrate that our model achieves competitive results in the relation prediction task when evaluated on a widely used dataset.
  arXiv  Detail & Related papers  (2024-04-24T21:04:14Z)
• Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models [95.31941227776711]
  We propose MPIKGC to compensate for the deficiency of contextualized knowledge and improve KGC by querying large language models (LLMs) We conducted extensive evaluation of our framework based on four description-based KGC models and four datasets, for both link prediction and triplet classification tasks.
  arXiv  Detail & Related papers  (2024-03-04T12:16:15Z)
• EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion [54.12709176438264]
  Commonsense knowledge graphs (CSKGs) utilize free-form text to represent named entities, short phrases, and events as their nodes. Current methods leverage semantic similarities to increase the graph density, but the semantic plausibility of the nodes and their relations are under-explored. We propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class.
  arXiv  Detail & Related papers  (2024-02-15T02:27:23Z)
• Contextual Dictionary Lookup for Knowledge Graph Completion [32.493168863565465]
  Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples. Most existing embedding models map each relation into a unique vector, overlooking the specific fine-grained semantics of them under different entities. We present a novel method utilizing contextual dictionary lookup, enabling conventional embedding models to learn fine-grained semantics of relations in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-06-13T12:13:41Z)
• Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [57.854498238624366]
  We propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP) for data-efficient knowledge graph construction. RAP can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample.
  arXiv  Detail & Related papers  (2022-10-19T16:40:28Z)
• Joint Language Semantic and Structure Embedding for Knowledge Graph Completion [66.15933600765835]
  We propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models. Our experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method.
  arXiv  Detail & Related papers  (2022-09-19T02:41:02Z)
• Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency [10.7334441041015]
  Knowledge graph-to-text (KG-to-text) generation aims to generate easy-to-understand sentences from the knowledge graph. In this paper, we optimize the knowledge description order prediction under the order supervision extracted from the caption. We incorporate the Part-of-Speech (POS) syntactic tags to constrain the positions to copy words from the KG.
  arXiv  Detail & Related papers  (2022-07-02T02:42:14Z)
• Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification [2.064612766965483]
  We propose a novel GNN-based sparse structure learning model for inductive document classification. Our model collects a set of trainable edges connecting disjoint words between sentences and employs structure learning to sparsely select edges with dynamic contextual dependencies. Experiments on several real-world datasets demonstrate that the proposed model outperforms most state-of-the-art results.
  arXiv  Detail & Related papers  (2021-12-13T02:36:04Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs [96.73259297063619]
  We consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions. We leverage Generative Adrial Networks (GANs) to establish the connection between text and knowledge graph domain.
  arXiv  Detail & Related papers  (2020-01-08T01:19:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.