Search Results for author: Jiaoyan Chen

Found 77 papers, 46 papers with code

TrustUQA: A Trustful Framework for Unified Structured Data Question Answering

no code implementations27 Jun 2024 Wen Zhang, Long Jin, Yushan Zhu, Jiaoyan Chen, Zhiwei Huang, Junjie Wang, Yin Hua, Lei Liang, Huajun Chen

In this paper, we propose UnifiedTQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way.

Answer Generation Knowledge Graphs +2

Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion

1 code implementation26 Jun 2024 Wen Zhang, Yajing Xu, Peng Ye, Zhiwei Huang, Zezhong Xu, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen

In this paper, we propose a novel graph-level automatic KG completion task called Triple Set Prediction (TSP) which assumes none of the elements in the missing triples is given.

Knowledge Graph Completion Link Prediction

Ontology Embedding: A Survey of Methods, Applications and Resources

no code implementations16 Jun 2024 Jiaoyan Chen, Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf, Yuan He, Ian Horrocks

Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains.

Logical Reasoning Ontology Embedding

TacoERE: Cluster-aware Compression for Event Relation Extraction

no code implementations11 May 2024 Yong Guan, Xiaozhi Wang, Lei Hou, Juanzi Li, Jeff Pan, Jiaoyan Chen, Freddy Lecue

Existing work mainly focuses on directly modeling the entire document, which cannot effectively handle long-range dependencies and information redundancy.

Event Relation Extraction Relation +1

Untargeted Adversarial Attack on Knowledge Graph Embeddings

no code implementations8 May 2024 Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru, Qika Lin, Yuxia Geng, Jun Liu

In this work, we explore untargeted attacks with the aim of reducing the global performances of KGE methods over a set of unknown test triples and conducting systematic analyses on KGE robustness.

Adversarial Attack Knowledge Graph Embedding +2

HGT: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding

no code implementations28 Mar 2024 Rihui Jin, Yu Li, Guilin Qi, Nan Hu, Yuan-Fang Li, Jiaoyan Chen, Jianan Wang, Yongrui Chen, Dehai Min

Table understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures. To address these challenges, we propose HGT, a framework with a heterogeneous graph (HG)-enhanced large language model (LLM) to tackle few-shot TU tasks. It leverages the LLM by aligning the table semantics with the LLM's parametric knowledge through soft prompts and instruction turning and deals with complex tables by a multi-task pre-training scheme involving three novel multi-granularity self-supervised HG pre-training objectives. We empirically demonstrate the effectiveness of HGT, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks.

Language Modelling Large Language Model

The Power of Noise: Toward a Unified Multi-modal Knowledge Graph Representation Framework

1 code implementation11 Mar 2024 Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Huajun Chen, Wen Zhang

In this work, to evaluate models' ability to accurately embed entities within MMKGs, we focus on two widely researched tasks: Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA).

Knowledge Graph Completion Misconceptions +3

A Language Model based Framework for New Concept Placement in Ontologies

1 code implementation27 Feb 2024 Hang Dong, Jiaoyan Chen, Yuan He, Yongsheng Gao, Ian Horrocks

In all steps, we propose to leverage neural methods, where we apply embedding-based methods and contrastive learning with Pre-trained Language Models (PLMs) such as BERT for edge search, and adapt a BERT fine-tuning-based multi-label Edge-Cross-encoder, and Large Language Models (LLMs) such as GPT series, FLAN-T5, and Llama 2, for edge selection.

Contrastive Learning Entity Linking +1

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

6 code implementations8 Feb 2024 Zhuo Chen, Yichi Zhang, Yin Fang, Yuxia Geng, Lingbing Guo, Xiang Chen, Qian Li, Wen Zhang, Jiaoyan Chen, Yushan Zhu, Jiaqi Li, Xiaoze Liu, Jeff Z. Pan, Ningyu Zhang, Huajun Chen

In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.

Entity Alignment Image Classification +4

Knowledge-Aware Neuron Interpretation for Scene Classification

no code implementations29 Jan 2024 Yong Guan, Freddy Lecue, Jiaoyan Chen, Ru Li, Jeff Z. Pan

Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts.

Classification Scene Classification

Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs

no code implementations26 Jan 2024 Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Sheng Bi, Tongtong Wu, Jeff Z. Pan

The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention.

Benchmarking Knowledge Graphs +1

Language Models as Hierarchy Encoders

1 code implementation21 Jan 2024 Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks

Interpreting hierarchical structures latent in language is a key limitation of current language models (LMs).

Embedding Ontologies via Incorporating Extensional and Intensional Knowledge

1 code implementation20 Jan 2024 Keyu Wang, Guilin Qi, Jiaoyan Chen, Yi Huang, Tianxing Wu

Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts.

Language Modelling Link Prediction +2

Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model

1 code implementation4 Dec 2023 Yuxia Geng, Jiaoyan Chen, Yuhang Zeng, Zhuo Chen, Wen Zhang, Jeff Z. Pan, Yuxiang Wang, Xiaoliang Xu

Accordingly, we propose a new KGC method named PDKGC with two prompts -- a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information.

Knowledge Graph Completion Language Modelling

Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

no code implementations29 Sep 2023 Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, Valentina Tamma

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines.

Knowledge Graphs Management

Exploring Large Language Models for Ontology Alignment

1 code implementation12 Sep 2023 Yuan He, Jiaoyan Chen, Hang Dong, Ian Horrocks

This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies.

Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment

1 code implementation30 Jul 2023 Zhuo Chen, Lingbing Guo, Yin Fang, Yichi Zhang, Jiaoyan Chen, Jeff Z. Pan, Yangning Li, Huajun Chen, Wen Zhang

As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.

 Ranked #1 on Multi-modal Entity Alignment on UMVM-oea-d-w-v2 (using extra training data)

Benchmarking Knowledge Graph Embeddings +2

DeepOnto: A Python Package for Ontology Engineering with Deep Learning

1 code implementation6 Jul 2023 Yuan He, Jiaoyan Chen, Hang Dong, Ian Horrocks, Carlo Allocca, Taehun Kim, Brahmananda Sapkota

Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms.

Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement

1 code implementation26 Jun 2023 Hang Dong, Jiaoyan Chen, Yuan He, Ian Horrocks

Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e. g., ontologies and taxonomies.

Language Modelling Large Language Model

Revisit and Outstrip Entity Alignment: A Perspective of Generative Models

no code implementations24 May 2023 Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yin Fang, Wen Zhang, Huajun Chen

We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).

Entity Alignment Generative Adversarial Network

An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering

no code implementations18 Mar 2023 Nan Hu, Yike Wu, Guilin Qi, Dehai Min, Jiaoyan Chen, Jeff Z. Pan, Zafar Ali

Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP).

Graph Question Answering Knowledge Distillation +1

Knowledge-augmented Few-shot Visual Relation Detection

no code implementations9 Mar 2023 Tianyu Yu, Yangning Li, Jiaoyan Chen, Yinghui Li, Hai-Tao Zheng, Xi Chen, Qingbin Liu, Wenqiang Liu, Dongxiao Huang, Bei Wu, Yexin Wang

Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD.

Diversity Few-Shot Learning +2

Language Model Analysis for Ontology Subsumption Inference

1 code implementation14 Feb 2023 Yuan He, Jiaoyan Chen, Ernesto Jiménez-Ruiz, Hang Dong, Ian Horrocks

Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently.

Language Modelling Natural Language Inference +1

Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking

3 code implementations14 Feb 2023 Hang Dong, Jiaoyan Chen, Yuan He, Yinan Liu, Ian Horrocks

We propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a special NIL entity.

Entity Linking

Dual Box Embeddings for the Description Logic EL++

2 code implementations26 Jan 2023 Mathias Jackermeier, Jiaoyan Chen, Ian Horrocks

OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been widely used for knowledge representation.

Knowledge Graphs Link Prediction +2

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

1 code implementation29 Dec 2022 Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yichi Zhang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen

Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.

 Ranked #1 on Entity Alignment on FBYG15k (using extra training data)

Knowledge Graphs Multi-modal Entity Alignment

Low-resource Personal Attribute Prediction from Conversation

no code implementations28 Nov 2022 Yinan Liu, Hu Chen, Wei Shen, Jiaoyan Chen

Previous studies often rely on a relative number of resources such as labeled utterances and external data, yet the attribute knowledge embedded in unlabeled utterances is underutilized and their performance of predicting some difficult personal attributes is still unsatisfactory.

Attribute text-classification +1

Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge

no code implementations20 Nov 2022 Yangning Li, Jiaoyan Chen, Yinghui Li, Tianyu Yu, Xi Chen, Hai-Tao Zheng

Extensive experiments demonstrate that PICSO can dramatically outperform the original PLMs and the other knowledge and synonym injection models on four different similarity-oriented tasks.

Entity Linking Language Modelling +4

Understanding Adverse Biological Effect Predictions Using Knowledge Graphs

1 code implementation28 Oct 2022 Erik Bryhn Myklebust, Ernesto Jimenez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen

An effect prediction model, with and without background knowledge, was used to predict mean adverse biological effect concentration of chemicals as a prototypical type of stressors.

Knowledge Graphs

Results of SemTab 2022

no code implementations SemTab@ISWC 2022 Nora Abdelmageed, Jiaoyan Chen, Vincenzo Cutrona, Vasilis Efthymiou, Oktie Hassanzadeh, Madelon Hulsebos, Ernesto Jiménez-Ruiz, Juan Sequeda, Kavitha Srinivas

SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Matching (OM) Workshop.

Cell Entity Annotation Column Type Annotation +2

Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph

1 code implementation19 Aug 2022 Yufeng Huang, Zhuo Chen, Jiaoyan Chen, Jeff Z. Pan, Zhen Yao, Wen Zhang

Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.

Decoder Image Captioning +3

LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection

1 code implementation26 Jul 2022 Zhuo Chen, Yufeng Huang, Jiaoyan Chen, Yuxia Geng, Yin Fang, Jeff Pan, Ningyu Zhang, Wen Zhang

Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge.

Decoder Knowledge Graphs +3

DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

2 code implementations4 Jul 2022 Zhuo Chen, Yufeng Huang, Jiaoyan Chen, Yuxia Geng, Wen Zhang, Yin Fang, Jeff Z. Pan, Huajun Chen

Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives.

Attribute Contrastive Learning +4

Disentangled Ontology Embedding for Zero-shot Learning

1 code implementation8 Jun 2022 Yuxia Geng, Jiaoyan Chen, Wen Zhang, Yajing Xu, Zhuo Chen, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen

In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.

Image Classification Ontology Embedding +2

Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

2 code implementations6 May 2022 Yuan He, Jiaoyan Chen, Hang Dong, Ernesto Jiménez-Ruiz, Ali Hadian, Ian Horrocks

Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques.

Ontology Matching

Automated Clinical Coding: What, Why, and Where We Are?

1 code implementation21 Mar 2022 Hang Dong, Matúš Falis, William Whiteley, Beatrice Alex, Joshua Matterson, Shaoxiong Ji, Jiaoyan Chen, Honghan Wu

Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.

Contextual Semantic Embeddings for Ontology Subsumption Prediction

2 code implementations20 Feb 2022 Jiaoyan Chen, Yuan He, Yuxia Geng, Ernesto Jimenez-Ruiz, Hang Dong, Ian Horrocks

Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence.

Knowledge Graph Embeddings Language Modelling +1

Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective

no code implementations15 Feb 2022 Wen Zhang, Jiaoyan Chen, Juan Li, Zezhong Xu, Jeff Z. Pan, Huajun Chen

Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry.

Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey

no code implementations18 Dec 2021 Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Jeff Z. Pan, Yuan He, Wen Zhang, Ian Horrocks, Huajun Chen

Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.

Data Augmentation Few-Shot Learning +10

Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings

3 code implementations8 Dec 2021 Erik B. Myklebust, Ernesto Jiménez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen

Furthermore, we have implemented a fine-tuning architecture that adapts the knowledge graph embeddings to the effect prediction task and leads to better performance.

Knowledge Graph Embedding Knowledge Graph Embeddings

BERTMap: A BERT-based Ontology Alignment System

1 code implementation5 Dec 2021 Yuan He, Jiaoyan Chen, Denvar Antonyrajah, Ian Horrocks

Ontology alignment (a. k. a ontology matching (OM)) plays a critical role in knowledge integration.

Feature Engineering Ontology Matching +1

Results of SemTab 2021

no code implementations ISWC 2021 Vincenzo Cutrona, Jiaoyan Chen, Vasilis Efthymiou, Oktie Hassanzadeh, Ernesto Jimenez-Ruiz, Juan Sequeda, Kavitha Srinivas, Nora Abdelmageed

SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop.

Graph Matching Ontology Matching +1

PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System

1 code implementation16 Jun 2021 Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Yefeng Zheng

Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i. e., mappings) between two KGs.

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

1 code implementation12 May 2021 Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Yuejia Xiang, Ningyu Zhang, Yefeng Zheng

Knowledge Graph (KG) alignment is to discover the mappings (i. e., equivalent entities, relations, and others) between two KGs.

Knowledge-aware Zero-Shot Learning: Survey and Perspective

1 code implementation26 Feb 2021 Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Ian Horrocks, Jeff Z. Pan, Huajun Chen

Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a. k. a.

BIG-bench Machine Learning Zero-Shot Learning

OntoZSL: Ontology-enhanced Zero-shot Learning

1 code implementation15 Feb 2021 Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z. Pan, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen

The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e. g., features) from training classes (i. e., seen classes) to unseen classes.

Image Classification Knowledge Graph Completion +2

An Industry Evaluation of Embedding-based Entity Alignment

1 code implementation COLING 2020 Ziheng Zhang, Jiaoyan Chen, Xi Chen, Hualuo Liu, Yuejia Xiang, Bo Liu, Yefeng Zheng

Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage.

Entity Alignment Knowledge Graphs

Distributed Representations of Entities in Open-World Knowledge Graphs

no code implementations16 Oct 2020 Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yichi Zhang, Zequn Sun, Zhongpo Bo, Yin Fang, Xiaoze Liu, Huajun Chen, Wen Zhang

DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings.

Entity Alignment Graph Neural Network +3

OWL2Vec*: Embedding of OWL Ontologies

1 code implementation30 Sep 2020 Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, Ian Horrocks

Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web.

Knowledge Graphs Language Modelling +1

Ontology-guided Semantic Composition for Zero-Shot Learning

1 code implementation30 Jun 2020 Jiaoyan Chen, Freddy Lecue, Yuxia Geng, Jeff Z. Pan, Huajun Chen

Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information.

Image Classification Ontology Embedding +4

Generative Adversarial Zero-shot Learning via Knowledge Graphs

no code implementations7 Apr 2020 Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen

However, the side information of classes used now is limited to text descriptions and attribute annotations, which are in short of semantics of the classes.

Attribute Image Classification +2

An Internal Clock Based Space-time Neural Network for Motion Speed Recognition

no code implementations28 Jan 2020 Junwen Luo, Jiaoyan Chen

In this work we present a novel internal clock based space-time neural network for motion speed recognition.

Correcting Knowledge Base Assertions

1 code implementation19 Jan 2020 Jiaoyan Chen, Xi Chen, Ian Horrocks, Ernesto Jimenez-Ruiz, Erik B. Myklebus

The usefulness and usability of knowledge bases (KBs) is often limited by quality issues.

TERA: the Toxicological Effect and Risk Assessment Knowledge Graph

4 code implementations27 Aug 2019 Erik Bryhn Myklebust, Ernesto Jimenez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen

Ecological risk assessment requires large amounts of chemical effect data from laboratory experiments.

Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning

no code implementations22 Aug 2019 Ningyu Zhang, Shumin Deng, Zhanlin Sun, Jiaoyan Chen, Wei zhang, Huajun Chen

However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision samples are usually noisy.

Partial Domain Adaptation Relation +2

Knowledge Graph Embedding for Ecotoxicological Effect Prediction

4 code implementations2 Jul 2019 Erik Bryhn Myklebust, Ernesto Jimenez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen

A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity.

Knowledge Graph Embedding

Canonicalizing Knowledge Base Literals

2 code implementations26 Jun 2019 Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues.

BIG-bench Machine Learning

Augmenting Transfer Learning with Semantic Reasoning

no code implementations31 May 2019 Freddy Lecue, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen

We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings.

Transfer Learning

Learning Semantic Annotations for Tabular Data

1 code implementation30 May 2019 Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton

The usefulness of tabular data such as web tables critically depends on understanding their semantics.

Column Type Annotation Type prediction

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

no code implementations21 Mar 2019 Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei zhang, Abraham Bernstein, Huajun Chen

We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.

Entity Embeddings Knowledge Graphs +1

Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]

no code implementations20 Jan 2019 Yuxia Geng, Jiaoyan Chen, Ernesto Jimenez-Ruiz, Huajun Chen

Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide research attentions.

Transfer Learning Zero-Shot Learning

ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

1 code implementation4 Nov 2018 Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton

Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables.

 Ranked #1 on Column Type Annotation on T2Dv2 (F1 (%) metric)

Column Type Annotation Type prediction +1

Knowledge-based Transfer Learning Explanation

1 code implementation22 Jul 2018 Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan, Ian Horrocks, Huajun Chen

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i. e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain.

BIG-bench Machine Learning Decision Making +1

Learning from Ontology Streams with Semantic Concept Drift

no code implementations24 Apr 2017 Freddy Lecue, Jiaoyan Chen, Jeff Pan, Huajun Chen

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records.

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