Learning Query Expansion over the Nearest Neighbor Graph

• URL: http://arxiv.org/abs/2112.02666v1
• Date: Sun, 5 Dec 2021 19:48:42 GMT
• Title: Learning Query Expansion over the Nearest Neighbor Graph
• Authors: Benjamin Klein and Lior Wolf
• Abstract summary: Graph Query Expansion (GQE) is presented, which is learned in a supervised manner and performs aggregation over an extended neighborhood of the query. The technique achieves state-of-the-art results over known benchmarks.
• Score: 94.80212602202518
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Query Expansion (QE) is a well established method for improving retrieval metrics in image search applications. When using QE, the search is conducted on a new query vector, constructed using an aggregation function over the query and images from the database. Recent works gave rise to QE techniques in which the aggregation function is learned, whereas previous techniques were based on hand-crafted aggregation functions, e.g., taking the mean of the query's nearest neighbors. However, most QE methods have focused on aggregation functions that work directly over the query and its immediate nearest neighbors. In this work, a hierarchical model, Graph Query Expansion (GQE), is presented, which is learned in a supervised manner and performs aggregation over an extended neighborhood of the query, thus increasing the information used from the database when computing the query expansion, and using the structure of the nearest neighbors graph. The technique achieves state-of-the-art results over known benchmarks.

Related papers

• Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs [51.33342412699939]
  Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. Recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. We propose an effective Query Instruction Parsing (QIPP) that captures latent query patterns from code-like query instructions.
  arXiv  Detail & Related papers  (2024-10-27T03:18:52Z)
• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• Database-Augmented Query Representation for Information Retrieval [59.57065228857247]
  We present a novel retrieval framework called Database-Augmented Query representation (DAQu) DAQu augments the original query with various (query-related) metadata across multiple tables. We validate DAQu in diverse retrieval scenarios that can incorporate metadata from the relational database.
  arXiv  Detail & Related papers  (2024-06-23T05:02:21Z)
• Progressive Query Expansion for Retrieval Over Cost-constrained Data Sources [6.109188517569139]
  ProQE is a progressive query expansion algorithm that iteratively expands the query as it retrieves more documents. Our results show that ProQE outperforms state-of-the-art baselines by 37% and is the most cost-effective.
  arXiv  Detail & Related papers  (2024-06-11T10:30:19Z)
• User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU [0.0]
  This work analyzed the informational, navigational, and transactional-based intents in queries for enhanced QP. For efficient QP, the data is structured using Epanechnikov Kernel-Ordering Points To Identify the Clustering Structure (EK-OPTICS) The extracted features, detected intents and structured data are inputted to the Multi-head Gated Recurrent Learnable Attention Unit (MGR-LAU)
  arXiv  Detail & Related papers  (2024-06-06T20:28:05Z)
• 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)
• Better Query Graph Selection for Knowledge Base Question Answering [2.367061689316429]
  This paper presents a novel approach based on semantic parsing to improve the performance of Knowledge Base Question Answering (KBQA) Specifically, we focus on how to select an optimal query graph from a candidate set so as to retrieve the answer from knowledge base (KB)
  arXiv  Detail & Related papers  (2022-04-27T01:53:06Z)
• Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching [58.39970828272366]
  Subgraph matching algorithms enumerate all is embeddings of a query graph in a data graph G. matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms.
  arXiv  Detail & Related papers  (2022-01-25T00:10:03Z)
• Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization [3.146785346730256]
  We present a query expansion method, which is combined in the manifold ranking to resolve this problem. Our method not only utilizes the information of the query term itself and the knowledge base WordNet to expand it by synonyms, but also uses the information of the document set itself to expand the query in various ways. In addition, we use the degree of word overlap and the proximity between words to calculate the similarity between sentences.
  arXiv  Detail & Related papers  (2021-07-31T02:20:44Z)

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.