PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System

• URL: http://arxiv.org/abs/2106.08801v1
• Date: Wed, 16 Jun 2021 14:06:09 GMT
• Title: PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System
• Authors: Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Yefeng Zheng
• Abstract summary: PRASEMap is an unsupervised KG alignment system that iteratively computes the Mappings with both Probabilistic Reasoning (PR) And Semantic Embedding (SE) techniques. PRASEMap can support various embedding-based KG alignment approaches as the SE module, and enables easy human computer interaction. The demonstration showcases these features via a stand-alone Web application with user friendly interfaces.
• Score: 22.6762874669173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i.e., mappings) between two KGs. The existing approaches utilize either reasoning-based or semantic embedding-based techniques, but few studies explore their combination. In this demonstration, we present PRASEMap, an unsupervised KG alignment system that iteratively computes the Mappings with both Probabilistic Reasoning (PR) And Semantic Embedding (SE) techniques. PRASEMap can support various embedding-based KG alignment approaches as the SE module, and enables easy human computer interaction that additionally provides an option for users to feed the mapping annotations back to the system for better results. The demonstration showcases these features via a stand-alone Web application with user friendly interfaces.

Related papers

• GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction [20.1127163541618]
  We have designed a universal map generation framework, GenMapping. The framework is established with a triadic synergy architecture, including principal and dual auxiliary branches. A thorough array of experimental results shows that the proposed model surpasses current state-of-the-art methods in both semantic mapping and vectorized mapping, while also maintaining a rapid inference speed.
  arXiv  Detail & Related papers  (2024-09-13T10:15:28Z)
• Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation [79.05949524349005]
  We propose AuxSegNet+, a weakly supervised auxiliary learning framework to explore the rich information from saliency maps. We also propose a cross-task affinity learning mechanism to learn pixel-level affinities from the saliency and segmentation feature maps.
  arXiv  Detail & Related papers  (2024-03-02T10:03:21Z)
• Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning [38.415692986360995]
  We propose a novel method named HOME-GCL for learning representations of multiple categories of map entities. Our approach utilizes a heterogeneous map entity graph (HOME graph) that integrates both road segments and land parcels into a unified framework. To the best of our knowledge, HOME-GCL is the first attempt to jointly learn representations for road segments and land parcels using a unified model.
  arXiv  Detail & Related papers  (2024-02-09T01:47:18Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection [17.92210977820113]
  We propose Guided-Embedding Network(GEN) to attain a two-branch pipeline without post-matching. For the association, previous two-branch methods suffer from complex and costly post-matching. For the interaction understanding, previous methods suffer from long-tailed distribution and zero-shot discovery.
  arXiv  Detail & Related papers  (2022-03-26T01:04:13Z)
• BERTMap: A BERT-based Ontology Alignment System [24.684912604644865]
  BERTMap can support both unsupervised and semi-supervised settings. BERTMap can often perform better than the leading systems LogMap and AML.
  arXiv  Detail & Related papers  (2021-12-05T21:08:44Z)
• Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation [61.114580368455236]
  User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. We propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors.
  arXiv  Detail & Related papers  (2021-09-07T04:28:09Z)
• Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding [22.123001954919893]
  We propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-12T11:27:46Z)
• 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)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)
• Articulation-aware Canonical Surface Mapping [54.0990446915042]
  We tackle the tasks of predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape, and inferring the articulation and pose of the template corresponding to the input image. Our key insight is that these tasks are geometrically related, and we can obtain supervisory signal via enforcing consistency among the predictions. We empirically show that allowing articulation helps learn more accurate CSM prediction, and that enforcing the consistency with predicted CSM is similarly critical for learning meaningful articulation.
  arXiv  Detail & Related papers  (2020-04-01T17:56:45Z)

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.