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Found 72 papers, 45 papers with code
UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic Relations
To tackle this task, this work proposes to fine-tune different BERT-based models pre-trained on different languages.
HGT: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding
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.
The Power of Noise: Toward a Unified Multi-modal Knowledge Graph Representation Framework
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).
A Language Model based Framework for New Concept Placement in Ontologies
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.
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data
Table-to-Text Generation is a promising solution by facilitating the transformation of hybrid data into a uniformly text-formatted corpus.
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
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.
Knowledge-Aware Neuron Interpretation for Scene Classification
Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts.
Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs
The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention.
Language Models as Hierarchy Encoders
Interpreting hierarchical structures latent in language is a key limitation of current language models (LMs).
Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
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.
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
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 Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
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.
Exploring Large Language Models for Ontology Alignment
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.
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm.
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
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)
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
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
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.
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).
An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering
Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP).
Knowledge-augmented Few-shot Visual Relation Detection
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.
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering.
Language Model Analysis for Ontology Subsumption Inference
Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently.
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking
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.
Dual Box Embeddings for the Description Logic EL++
OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been widely used for knowledge representation.
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
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)
Low-resource Personal Attribute Prediction from Conversation
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.
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge
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.
Understanding Adverse Biological Effect Predictions Using Knowledge Graphs
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.
Results of SemTab 2022
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.
Ranked #2 on
Cell Entity Annotation
on ToughTables-WD
Relational Message Passing for Fully Inductive Knowledge Graph Completion
Subgraph reasoning with message passing is a promising and popular solution.
Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks.
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge.
Ranked #21 on
Visual Question Answering (VQA)
on OK-VQA
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
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.
Ranked #1 on
Zero-Shot Learning
on CUB-200-2011
Disentangled Ontology Embedding for Zero-shot Learning
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.
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes.
Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
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.
Automated Clinical Coding: What, Why, and Where We Are?
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
Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence.
Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective
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
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.
Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
Furthermore, we have implemented a fine-tuning architecture that adapts the knowledge graph embeddings to the effect prediction task and leads to better performance.
BERTMap: A BERT-based Ontology Alignment System
Ontology alignment (a. k. a ontology matching (OM)) plays a critical role in knowledge integration.
Results of SemTab 2021
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.
Zero-shot Visual Question Answering using Knowledge Graph
Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need.
Ranked #1 on
Visual Question Answering (VQA)
on F-VQA
PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System
Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i. e., mappings) between two KGs.
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities.
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
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
Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a. k. a.
OntoZSL: Ontology-enhanced Zero-shot Learning
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.
An Industry Evaluation of Embedding-based Entity Alignment
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.
Distributed Representations of Entities in Open-World Knowledge Graphs
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.
OWL2Vec*: Embedding of OWL Ontologies
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.
Zero-shot Text Classification via Reinforced Self-training
In this situation, transferring from seen classes to unseen classes is extremely hard.
Ontology-guided Semantic Composition for Zero-Shot Learning
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.
Generative Adversarial Zero-shot Learning via Knowledge Graphs
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.
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems.
An Internal Clock Based Space-time Neural Network for Motion Speed Recognition
In this work we present a novel internal clock based space-time neural network for motion speed recognition.
Correcting Knowledge Base Assertions
The usefulness and usability of knowledge bases (KBs) is often limited by quality issues.
TERA: the Toxicological Effect and Risk Assessment Knowledge Graph
Ecological risk assessment requires large amounts of chemical effect data from laboratory experiments.
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification
Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes.
Ranked #1 on
Multi-Domain Sentiment Classification
on ARSC
Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning
However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision samples are usually noisy.
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity.
Canonicalizing Knowledge Base Literals
Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues.
Augmenting Transfer Learning with Semantic Reasoning
We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings.
Learning Semantic Annotations for Tabular Data
The usefulness of tabular data such as web tables critically depends on understanding their semantics.
Ranked #1 on
Column Type Annotation
on T2Dv2
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
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.
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]
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.
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
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)
Knowledge-based Transfer Learning Explanation
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.
Learning from Ontology Streams with Semantic Concept Drift
Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records.
