Search Results for author: Daisy Zhe Wang

Found 17 papers, 5 papers with code

Learned Accelerator Framework for Angular-Distance-Based High-Dimensional DBSCAN

1 code implementation6 Feb 2023 Yifan Wang, Daisy Zhe Wang

In this paper, we propose LAF, a generic learned accelerator framework to speed up the original DBSCAN and the sampling-based variants of DBSCAN on high-dimensional data with angular distance metric.

A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge

no code implementations3 Jan 2023 Haodi Ma, Daisy Zhe Wang

In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications.

Few-Shot Learning Graph Representation Learning

Knowledge Base Completion using Web-Based Question Answering and Multimodal Fusion

no code implementations14 Nov 2022 Yang Peng, Daisy Zhe Wang

Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge.

Knowledge Base Completion Question Answering

DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

1 code implementation LREC 2022 Jayetri Bardhan, Anthony Colas, Kirk Roberts, Daisy Zhe Wang

Our goal is to provide a benchmark dataset for multi-modal QA systems, and to open up new avenues of research in improving question answering over EHR structured data by using context from unstructured clinical data.

Question Answering Text-To-SQL

LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval

no code implementations2 May 2022 Yifan Wang, Haodi Ma, Daisy Zhe Wang

As the basic unit of LIDER to index and search data, a core model includes an adapted recursive model index (RMI) and a dimension reduction component which consists of an extended SortingKeys-LSH (SK-LSH) and a key re-scaling module.

Dimensionality Reduction Passage Retrieval +1

GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation

no code implementations COLING 2022 Anthony Colas, Mehrdad Alvandipour, Daisy Zhe Wang

Recent improvements in KG-to-text generation are due to additional auxiliary pre-training tasks designed to give the fine-tune task a boost in performance.

Graph Attention KG-to-Text Generation +2

EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation

no code implementations30 Oct 2021 Anthony Colas, Ali Sadeghian, Yue Wang, Daisy Zhe Wang

We also evaluate two types of baseline on EventNarrative: a graph-to-text specific model and two state-of-the-art language models, which previous work has shown to be adaptable to the knowledge graph-to-text domain.

Knowledge Graphs Text Generation

More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering

no code implementations25 Sep 2021 Yang Bai, Daisy Zhe Wang

Textual Question Answering (QA) aims to provide precise answers to user's questions in natural language using unstructured data.

Machine Reading Comprehension Question Answering

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

1 code implementation NeurIPS 2019 Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang

Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities.

Inductive knowledge graph completion Inductive Link Prediction +1

Measuring Impact of Climate Change on Tree Species: analysis of JSDM on FIA data

1 code implementation11 Oct 2019 Hyun Choi, Ali Sadeghian, Sergio Marconi, Ethan White, Daisy Zhe Wang

In this study tree species interaction and the response to climate in different ecological environments is observed by applying a joint species distribution model to different ecological domains in the United States.

Populations and Evolution

Mining Rules Incrementally over Large Knowledge Bases

no code implementations20 Apr 2019 Xiaofeng Zhou, Ali Sadeghian, Daisy Zhe Wang

To the best of our knowledge, our incremental rule mining system is the first that handles updates to web-scale knowledge bases.

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