Neural Relation Prediction for Simple Question Answering over Knowledge Graph

• URL: http://arxiv.org/abs/2002.07715v3
• Date: Sun, 5 Jul 2020 14:34:10 GMT
• Title: Neural Relation Prediction for Simple Question Answering over Knowledge Graph
• Authors: Amin Abolghasemi, Saeedeh Momtazi
• Abstract summary: We propose an instance-based method to capture the underlying relation of question and to this aim, we detect matching paraphrases of a new question. Our experiments on the SimpleQuestions dataset show that the proposed model achieves better accuracy compared to the state-of-the-art relation extraction models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs are widely used as a typical resource to provide answers to factoid questions. In simple question answering over knowledge graphs, relation extraction aims to predict the relation of a factoid question from a set of predefined relation types. Most recent methods take advantage of neural networks to match a question with all predefined relations. In this paper, we propose an instance-based method to capture the underlying relation of question and to this aim, we detect matching paraphrases of a new question which share the same relation, and their corresponding relation is selected as our prediction. The idea of our model roots in the fact that a relation can be expressed with various forms of questions while these forms share lexically or semantically similar terms and concepts. Our experiments on the SimpleQuestions dataset show that the proposed model achieves better accuracy compared to the state-of-the-art relation extraction models.

Related papers

• Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction [19.019881161010474]
  Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM) Existing approaches therefore solve the problem in an indirect way: they fine-tune an LM to learn embeddings of the head and tail entities, and then predict the relationship from these entity embeddings. Our hypothesis in this paper is that relation extraction models can be improved by capturing relationships in a more direct way.
  arXiv  Detail & Related papers  (2023-12-18T09:58:19Z)
• Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors [58.340159346749964]
  We propose a new neural-symbolic method to support end-to-end learning using complex queries with provable reasoning capability. We develop a new dataset containing ten new types of queries with features that have never been considered. Our method outperforms previous methods significantly in the new dataset and also surpasses previous methods in the existing dataset at the same time.
  arXiv  Detail & Related papers  (2023-04-14T11:35:35Z)
• A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction [22.60428265210431]
  Few-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation. Some recent works have introduced relation information to assist model learning based on Prototype Network. We argue that relation information can be introduced more explicitly and effectively into the model.
  arXiv  Detail & Related papers  (2022-05-19T13:03:01Z)
• Link Prediction on N-ary Relational Data Based on Relatedness Evaluation [61.61555159755858]
  We propose a method called NaLP to conduct link prediction on n-ary relational data. We represent each n-ary relational fact as a set of its role and role-value pairs. Experimental results validate the effectiveness and merits of the proposed methods.
  arXiv  Detail & Related papers  (2021-04-21T09:06:54Z)
• Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering [26.254684189099496]
  Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. We propose a novel neural model, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information.
  arXiv  Detail & Related papers  (2021-04-01T03:04:36Z)
• Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs [49.6661602019124]
  We study a spectrum of models derived by generalizing the current state of the art for few-shot link prediction. We find that a simple zero-shot baseline - which ignores any relation-specific information - achieves surprisingly strong performance. Experiments on carefully crafted synthetic datasets show that having only a few examples of a relation fundamentally limits models from using fine-grained structural information.
  arXiv  Detail & Related papers  (2021-02-05T21:04:31Z)
• Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction [84.64435075778988]
  We propose a general approach to learn relation prototypes from unlabeled texts. We learn relation prototypes as an implicit factor between entities. We conduct experiments on two publicly available datasets: New York Times and Google Distant Supervision.
  arXiv  Detail & Related papers  (2020-11-27T06:21:12Z)
• Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification [31.887770824130957]
  We propose a novel logic-guided semantic representation learning model for zero-shot relation classification. Our approach builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules.
  arXiv  Detail & Related papers  (2020-10-30T04:30:09Z)
• Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training [49.9995628166064]
  We propose CTEG, a model equipped with two mechanisms to learn to decouple easily-confused relations. On the one hand, an EGA mechanism is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations.
  arXiv  Detail & Related papers  (2020-10-21T11:07:53Z)
• Visual Question Answering with Prior Class Semantics [50.845003775809836]
  We show how to exploit additional information pertaining to the semantics of candidate answers. We extend the answer prediction process with a regression objective in a semantic space. Our method brings improvements in consistency and accuracy over a range of question types.
  arXiv  Detail & Related papers  (2020-05-04T02:46:31Z)

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.