Modeling Global Semantics for Question Answering over Knowledge Bases

• URL: http://arxiv.org/abs/2101.01510v1
• Date: Tue, 5 Jan 2021 13:51:14 GMT
• Title: Modeling Global Semantics for Question Answering over Knowledge Bases
• Authors: Peiyun Wu and Yunjie Wu and Linjuan Wu and Xiaowang Zhang and Zhiyong Feng
• Abstract summary: We present a relational graph convolutional network (RGCN)-based model gRGCN for semantic parsing in KBQA. Experiments evaluated on benchmarks show that our model outperforms off-the-shelf models.
• Score: 16.341353183347664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic parsing, as an important approach to question answering over knowledge bases (KBQA), transforms a question into the complete query graph for further generating the correct logical query. Existing semantic parsing approaches mainly focus on relations matching with paying less attention to the underlying internal structure of questions (e.g., the dependencies and relations between all entities in a question) to select the query graph. In this paper, we present a relational graph convolutional network (RGCN)-based model gRGCN for semantic parsing in KBQA. gRGCN extracts the global semantics of questions and their corresponding query graphs, including structure semantics via RGCN and relational semantics (label representation of relations between entities) via a hierarchical relation attention mechanism. Experiments evaluated on benchmarks show that our model outperforms off-the-shelf models.

Related papers

• Relation-Aware Question Answering for Heterogeneous Knowledge Graphs [37.38138785470231]
  Existing retrieval-based approaches solve this task by concentrating on the specific relation at different hops. We claim they fail to utilize information from head-tail entities and the semantic connection between relations to enhance the current relation representation. Our approach achieves a significant performance gain over the prior state-of-the-art.
  arXiv  Detail & Related papers  (2023-12-19T08:01:48Z)
• 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)
• Logical Message Passing Networks with One-hop Inference on Atomic Formulas [57.47174363091452]
  We propose a framework for complex query answering that decomposes the Knowledge Graph embeddings from neural set operators. On top of the query graph, we propose the Logical Message Passing Neural Network (LMPNN) that connects the local one-hop inferences on atomic formulas to the global logical reasoning. Our approach yields the new state-of-the-art neural CQA model.
  arXiv  Detail & Related papers  (2023-01-21T02:34:06Z)
• Relation-Aware Language-Graph Transformer for Question Answering [21.244992938222246]
  We propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations. Specifically, QAT constructs Meta-Path tokens, which learn relation-centric embeddings based on diverse structural and semantic relations. We validate the effectiveness of QAT on commonsense question answering datasets like CommonsenseQA and OpenBookQA, and on a medical question answering dataset, MedQA-USMLE.
  arXiv  Detail & Related papers  (2022-12-02T05:10:10Z)
• 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)
• Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding [84.04706075621013]
  We present a general-purpose, modular neural semantic parsing framework based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural entity linker (NSP)
  arXiv  Detail & Related papers  (2022-09-28T21:00:30Z)
• Knowledge Base Question Answering by Case-based Reasoning over Subgraphs [81.22050011503933]
  We show that our model answers queries requiring complex reasoning patterns more effectively than existing KG completion algorithms. The proposed model outperforms or performs competitively with state-of-the-art models on several KBQA benchmarks.
  arXiv  Detail & Related papers  (2022-02-22T01:34:35Z)
• Attention Guided Semantic Relationship Parsing for Visual Question Answering [36.84737596725629]
  Humans explain inter-object relationships with semantic labels that demonstrate a high-level understanding required to perform Vision-Language tasks such as Visual Question Answering (VQA) Existing VQA models represent relationships as a combination of object-level visual features which constrain a model to express interactions between objects in a single domain, while the model is trying to solve a multi-modal task. In this paper, we propose a general purpose semantic relationship which generates a semantic feature vector for each subject-predicate-object triplet in an image, and a Mutual and Self Attention mechanism that learns to identify relationship triplets that are important to
  arXiv  Detail & Related papers  (2020-10-05T00:23:49Z)
• Query Focused Multi-Document Summarization with Distant Supervision [88.39032981994535]
  Existing work relies heavily on retrieval-style methods for estimating the relevance between queries and text segments. We propose a coarse-to-fine modeling framework which introduces separate modules for estimating whether segments are relevant to the query. We demonstrate that our framework outperforms strong comparison systems on standard QFS benchmarks.
  arXiv  Detail & Related papers  (2020-04-06T22:35:19Z)
• Iterative Context-Aware Graph Inference for Visual Dialog [126.016187323249]
  We propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations.
  arXiv  Detail & Related papers  (2020-04-05T13:09:37Z)

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.