KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning

• URL: http://arxiv.org/abs/2408.14658v1
• Date: Mon, 26 Aug 2024 21:47:49 GMT
• Title: KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning
• Authors: Pierre Monnin, Cherif-Hassan Nousradine, Lucas Jarnac, Laurel Zuckerman, Miguel Couceiro,
• Abstract summary: We introduce KGPrune, a Web Application that extracts subgraphs of interest from Wikidata. KGPrune relies on a frugal pruning algorithm based on analogical reasoning to only keep relevant neighbors while pruning irrelevant ones. The interest of KGPrune is illustrated by two concrete applications, namely, bootstrapping an enterprise KG and extracting knowledge related to looted artworks.
• Score: 3.250579305400297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources, and are nowadays covering an ever increasing array of domains. However, not all knowledge represented is useful or pertaining when considering a new application or specific task. Also, due to their increasing size, handling large KGs in their entirety entails scalability issues. These two aspects asks for efficient methods to extract subgraphs of interest from existing KGs. To this aim, we introduce KGPrune, a Web Application that, given seed entities of interest and properties to traverse, extracts their neighboring subgraphs from Wikidata. To avoid topical drift, KGPrune relies on a frugal pruning algorithm based on analogical reasoning to only keep relevant neighbors while pruning irrelevant ones. The interest of KGPrune is illustrated by two concrete applications, namely, bootstrapping an enterprise KG and extracting knowledge related to looted artworks.

Related papers

• A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning [17.676185326247946]
  We propose a prompt-based KG foundation model via in-context learning, namely KG-ICL, to achieve a universal reasoning ability. To encode prompt graphs with the generalization ability to unseen entities and relations in queries, we first propose a unified tokenizer. Then, we propose two message passing neural networks to perform prompt encoding and KG reasoning, respectively.
  arXiv  Detail & Related papers  (2024-10-16T06:47:18Z)
• Knowledge Graph-Enhanced Large Language Models via Path Selection [58.228392005755026]
  Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. LLMs are known to generate factually inaccurate outputs, a.k.a. the hallucination problem. We propose a principled framework KELP with three stages to handle the above problems.
  arXiv  Detail & Related papers  (2024-06-19T21:45:20Z)
• Multi-hop Question Answering over Knowledge Graphs using Large Language Models [1.8130068086063336]
  We evaluate the capability of (LLMs) to answer questions over Knowledge graphs that involve multiple hops. We show that depending upon the size and nature of the KG we need different approaches to extract and feed the relevant information to an LLM.
  arXiv  Detail & Related papers  (2024-04-30T03:31:03Z)
• Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
  We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs) GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
  arXiv  Detail & Related papers  (2024-04-23T04:47:22Z)
• PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs [56.5262495514563]
  We propose a grounding-pruning-reasoning pipeline to prune noisy computation nodes. We also propose a graph attention network (GAT) based module to reason with the subgraph data.
  arXiv  Detail & Related papers  (2024-01-31T01:37:33Z)
• Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning [4.281723404774889]
  We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities.
  arXiv  Detail & Related papers  (2023-06-28T15:17:59Z)
• BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models [65.51390418485207]
  We propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition, the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge. We deploy the approach to harvest KGs of over 400 new relations from different LMs.
  arXiv  Detail & Related papers  (2022-06-28T19:46:29Z)
• Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus [59.20235923987045]
  It is challenging to enrich a Knowledge Graph with newly harvested triples while maintaining the quality of the knowledge representation. This paper proposes a system to refine a KG using information harvested from an additional corpus.
  arXiv  Detail & Related papers  (2022-06-15T12:16:10Z)
• Relational Learning Analysis of Social Politics using Knowledge Graph Embedding [11.978556412301975]
  This paper presents a novel credibility domain-based KG Embedding framework. It involves capturing a fusion of data obtained from heterogeneous resources into a formal KG representation depicted by a domain. The framework also embodies a credibility module to ensure data quality and trustworthiness.
  arXiv  Detail & Related papers  (2020-06-02T14:10:28Z)
• Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering [50.72473345911147]
  This paper augments a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop relational path.
  arXiv  Detail & Related papers  (2020-05-02T03:53:21Z)
• Entity Type Prediction in Knowledge Graphs using Embeddings [2.7528170226206443]
  Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Most of these KGs are mostly created either via an automated information extraction from snapshots or information accumulation provided by the users or using Wikipedias. It has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. A multi-label classification approach is proposed in this work for entity typing using KG embeddings.
  arXiv  Detail & Related papers  (2020-04-28T17:57:08Z)

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.