Guiding Catalogue Enrichment with User Queries

• URL: http://arxiv.org/abs/2406.07098v1
• Date: Tue, 11 Jun 2024 09:38:46 GMT
• Title: Guiding Catalogue Enrichment with User Queries
• Authors: Yupei Du, Jacek Golebiowski, Philipp Schmidt, Ziawasch Abedjan,
• Abstract summary: We present our intuition for identifying enrichable data points and use general-purpose KGs to show-case the performance benefits. We assess our method on two popular encyclopedia KGs, DBPedia and YAGO 4.
• Score: 8.435621679543464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Techniques for knowledge graph (KGs) enrichment have been increasingly crucial for commercial applications that rely on evolving product catalogues. However, because of the huge search space of potential enrichment, predictions from KG completion (KGC) methods suffer from low precision, making them unreliable for real-world catalogues. Moreover, candidate facts for enrichment have varied relevance to users. While making correct predictions for incomplete triplets in KGs has been the main focus of KGC method, the relevance of when to apply such predictions has been neglected. Motivated by the product search use case, we address the angle of generating relevant completion for a catalogue using user search behaviour and the users property association with a product. In this paper, we present our intuition for identifying enrichable data points and use general-purpose KGs to show-case the performance benefits. In particular, we extract entity-predicate pairs from user queries, which are more likely to be correct and relevant, and use these pairs to guide the prediction of KGC methods. We assess our method on two popular encyclopedia KGs, DBPedia and YAGO 4. Our results from both automatic and human evaluations show that query guidance can significantly improve the correctness and relevance of prediction.

Related papers

• Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion [34.58496513149175]
  We find that the current procedure for constructing inductive KGC datasets inadvertently creates a shortcut that can be exploited. Specifically, we observe that the Personalized PageRank (PPR) score can achieve strong or near SOTA performance on most inductive datasets. We propose an alternative strategy for constructing inductive KGC datasets that helps mitigate the PPR shortcut.
  arXiv  Detail & Related papers  (2024-06-14T21:01:46Z)
• Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs [49.547988001231424]
  We propose the one-shot-subgraph link prediction to achieve efficient and adaptive prediction. Design principle is that, instead of directly acting on the whole KG, the prediction procedure is decoupled into two steps. We achieve promoted efficiency and leading performances on five large-scale benchmarks.
  arXiv  Detail & Related papers  (2024-03-15T12:00:12Z)
• Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning [67.71952251641545]
  GPTRec is an alternative to the Top-K model for item-by-item recommendations. We show that GPTRec offers a better tradeoff between accuracy and secondary metrics than classic greedy re-ranking techniques. Our experiments on two datasets show that GPTRec's Next-K generation approach offers a better tradeoff between accuracy and secondary metrics than classic greedy re-ranking techniques.
  arXiv  Detail & Related papers  (2024-03-07T19:47:48Z)
• Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks [0.552480439325792]
  Cardinality Estimation over Knowledge Graphs (KG) is crucial for query optimization. We propose GNCE, a novel approach that leverages knowledge graph embeddings and Graph Neural Networks (GNN) to accurately predict the cardinality of conjunctive queries.
  arXiv  Detail & Related papers  (2023-03-02T10:39:13Z)
• 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)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• 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)
• Knowledge-Enhanced Top-K Recommendation in Poincar\'e Ball [33.90069123451581]
  We propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. A hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. We show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.
  arXiv  Detail & Related papers  (2021-01-13T03:16:50Z)
• Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions [42.626395991024545]
  We propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Experimental results show that our method outperforms the state-of-the-art methods with higher efficiency on two KGC tasks with OOKG entities.
  arXiv  Detail & Related papers  (2020-09-27T07:12:18Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

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.