Search Results for author:
Found 30 papers, 7 papers with code
AutoBlock: A Hands-off Blocking Framework for Entity Matching
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity.
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
We study this problem in the context of product catalogs that often have missing values for many attributes of interest.
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
Most prior works on this matter mine new values for a set of known attributes but cannot handle new attributes that arose from constantly changing data.
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories
Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce.
TCN: Table Convolutional Network for Web Table Interpretation
Existing work linearize table cells and heavily rely on modifying deep language models such as BERT which only captures related cells information in the same table.
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference
In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB).
CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web
In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision.
LinkNBed: Multi-Graph Representation Learning with Entity Linkage
Knowledge graphs have emerged as an important model for studying complex multi-relational data.
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
How can we estimate the importance of nodes in a knowledge graph (KG)?
OpenCeres: When Open Information Extraction Meets the Semi-Structured Web
In this paper, we define the problem of OpenIE from semi-structured websites to extract such facts, and present an approach for solving it.
Efficient Knowledge Graph Accuracy Evaluation
To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts.
Databases
ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages
In this work, we propose a solution for "zero-shot" open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals.
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data
(1) We formalize the problem of validating the textual attribute values of products from a variety of categories as a natural language inference task in the few-shot learning setting, and propose a meta-learning latent variable model to jointly process the signals obtained from product profiles and textual attribute values.
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
We propose to distantly train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure as well as the query-item-taxonomy interactions for term attachment.
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web
In this tutorial we take a holistic view toward information extraction, exploring the commonalities in the challenges and solutions developed to address these different forms of text.
MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage
We evaluate CorDEL with extensive experiments conducted on both public benchmark datasets and a real-world dataset.
Ranked #7 on
Entity Resolution
on Amazon-Google
J-Recs: Principled and Scalable Recommendation Justification
Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users.
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction
We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions.
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding
However, this approach constrains knowledge sharing across different attributes.
PAM: Understanding Product Images in Cross Product Category Attribute Extraction
Understanding product attributes plays an important role in improving online shopping experience for customers and serves as an integral part for constructing a product knowledge graph.
End-to-End Conversational Search for Online Shopping with Utterance Transfer
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation
AdaMEL models the attribute importance that is used to match entities through an attribute-level self-attention mechanism, and leverages the massive unlabeled data from new data sources through domain adaptation to make it generic and data-source agnostic.
Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage
However, this task is challenging as the variational attributes are often present as a part of unstructured text and are domain dependent.
Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?
Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the world knowledge in a symbolic form, will be replaced with LLMs.
Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact
Knowledge Graphs (KGs) have been used to support a wide range of applications, from web search to personal assistant.
Lumos : Empowering Multimodal LLMs with Scene Text Recognition
We introduce Lumos, the first end-to-end multimodal question-answering system with text understanding capabilities.
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling
We also show that by fine-tuning on a small collection of diverse task-oriented dialogues, we can equip modestly sized models, specifically a 13B parameter LLaMA2-Chat model, with function-calling capabilities and DST performance comparable to ChatGPT while maintaining their chat capabilities.
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM
We have developed the \textbf{SnapNTell Dataset}, distinct from traditional VQA datasets: (1) It encompasses a wide range of categorized entities, each represented by images and explicitly named in the answers; (2) It features QA pairs that require extensive knowledge for accurate responses.
