Large Language Models (LLMs) have taken Knowledge Representation – and t...
Large-scale pre-trained language models (PLMs) such as BERT have recentl...
Visual Relation Detection (VRD) aims to detect relationships between obj...
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role i...
Representation learning in the form of semantic embeddings has been
succ...
As an important variant of entity alignment (EA), multi-modal entity
ali...
Personal knowledge bases (PKBs) are crucial for a broad range of applica...
Contextual synonym knowledge is crucial for those similarity-oriented ta...
Extrapolation of adverse biological (toxic) effects of chemicals is an
i...
In knowledge graph completion (KGC), predicting triples involving emergi...
Business Knowledge Graph is important to many enterprises today, providi...
Multi-modal aspect-based sentiment classification (MABSC) is an emerging...
Visual question answering (VQA) often requires an understanding of visua...
Zero-shot learning (ZSL) aims to predict unseen classes whose samples ha...
Knowledge Graph (KG) and its variant of ontology have been widely used f...
Computational text phenotyping is the practice of identifying patients w...
Clinical coding is the task of transforming medical information in a
pat...
Knowledge graph (KG) reasoning is becoming increasingly popular in both
...
We have created a knowledge graph based on major data sources used in
ec...
Ontology alignment (a.k.a ontology matching (OM)) plays a critical role ...
Incorporating external knowledge to Visual Question Answering (VQA) has
...
External knowledge (a.k.a side information) plays a critical role in
zer...
Knowledge Graph (KG) alignment aims at finding equivalent entities and
r...
Semantic embedding has been widely investigated for aligning knowledge g...
Knowledge Graph (KG) alignment is to discover the mappings (i.e., equiva...
Zero-shot learning (ZSL) which aims at predicting classes that have neve...
Zero-shot Learning (ZSL), which aims to predict for those classes that h...
Embedding-based entity alignment has been widely investigated in recent
...
Semantic embedding of knowledge graphs has been widely studied and used ...
Zero-shot learning (ZSL) is a popular research problem that aims at
pred...
Zero-shot learning (ZSL) is to handle the prediction of those unseen cla...
Large ontologies still pose serious challenges to state-of-the-art ontol...
In this work we present a novel internal clock based space-time neural
n...
The usefulness and usability of knowledge bases (KBs) is often limited b...
Experimental effort and animal welfare are concerns when exploring the
e...
Relation extraction aims to extract relational facts from sentences. Pre...
Text classification tends to be difficult when the data is deficient or ...
Exploring the effects a chemical compound has on a species takes a
consi...
Ontology-based knowledge bases (KBs) like DBpedia are very valuable
reso...
Transfer learning aims at building robust prediction models by transferr...
The usefulness of tabular data such as web tables critically depends on
...
Reasoning is essential for the development of large knowledge graphs,
es...
Transfer learning which aims at utilizing knowledge learned from one pro...
Automatically annotating column types with knowledge base (KB) concepts ...
Machine learning explanation can significantly boost machine learning's
...
Data stream learning has been largely studied for extracting knowledge
s...