Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …

Audiovisual data is everywhere in this digital age, which raises higher requirements for the
deep learning models developed on them. To well handle the information of the multi-modal …

Given a document in a source language, cross-lingual summarization (CLS) aims at
generating a concise summary in a different target language. Unlike monolingual …

Entity alignment (EA) aims to automatically match entities in different knowledge graphs,
which is beneficial to the development of knowledge-driven applications. Representation …

This paper addresses the task of temporal sentence grounding (TSG). Although many
respectable works have made decent achievements in this important topic, they severely …

Multi-modal opinion summarization aims at automatically generating summaries of products
or businesses from multi-modal reviews containing text, image and table to present clear …

Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity
pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges …

The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple
Knowledge Graphs (KGs) and create a more comprehensive and unified KG. The majority of …

Entity alignment aims to identify entities referring to the same real world object among
multiple knowledge graphs. Current embedding based approaches suffer from the lack of …