Entity Type Prediction in Knowledge Graphs using Embeddings

• URL: http://arxiv.org/abs/2004.13702v2
• Date: Wed, 6 May 2020 14:16:54 GMT
• Title: Entity Type Prediction in Knowledge Graphs using Embeddings
• Authors: Russa Biswas, Radina Sofronova, Mehwish Alam, Harald Sack
• Abstract summary: 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.
• Score: 2.7528170226206443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Hence, the completeness and correctness of the Knowledge Graphs (KGs) are vital. Most of these KGs are mostly created either via an automated information extraction from Wikipedia snapshots or information accumulation provided by the users or using heuristics. However, it has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings. We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs.

Related papers

• Learning Rules from KGs Guided by Language Models [48.858741745144044]
  Rule learning methods can be applied to predict potentially missing facts. Ranking of rules is especially challenging over highly incomplete or biased KGs. With the recent rise of Language Models (LMs) several works have claimed that LMs can be used as alternative means for KG completion.
  arXiv  Detail & Related papers  (2024-09-12T09:27:36Z)
• Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals [2.211868306499727]
  Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG) We propose a set of universal preprocessing operators which can be used to transform KGs with literals for numerical, temporal, textual, and image information. The results on the kgbench dataset with three different embedding methods show promising results.
  arXiv  Detail & Related papers  (2023-09-06T14:08:46Z)
• Text-To-KG Alignment: Comparing Current Methods on Classification Tasks [2.191505742658975]
  knowledge graphs (KG) provide dense and structured representations of factual information. Recent work has focused on creating pipeline models that retrieve information from KGs as additional context. It is not known how current methods compare to a scenario where the aligned subgraph is completely relevant to the query.
  arXiv  Detail & Related papers  (2023-06-05T13:45:45Z)
• A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal [57.8455911689554]
  Knowledge graph reasoning (KGR) aims to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs) It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc.
  arXiv  Detail & Related papers  (2022-12-12T08:40:04Z)
• KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models [76.01814380927507]
  KGxBoard is an interactive framework for performing fine-grained evaluation on meaningful subsets of the data. In our experiments, we highlight the findings with the use of KGxBoard, which would have been impossible to detect with standard averaged single-score metrics.
  arXiv  Detail & Related papers  (2022-08-23T15:11:45Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)
• Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out [11.534085606272242]
  We propose an active typing error detection algorithm that maximizes the utilization of both gold and noisy labels. The outcomes of this study provide guidelines for researchers to use noisy factual KGs.
  arXiv  Detail & Related papers  (2021-02-03T21:47:37Z)
• Connecting Embeddings for Knowledge Graph Entity Typing [22.617375045752084]
  Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG. We propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge from KGs.
  arXiv  Detail & Related papers  (2020-07-21T15:00:01Z)
• ENT-DESC: Entity Description Generation by Exploring Knowledge Graph [53.03778194567752]
  In practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. We introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. We propose a multi-graph structure that is able to represent the original graph information more comprehensively.
  arXiv  Detail & Related papers  (2020-04-30T14:16:19Z)
• 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.