Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Knowledge graphs are important resources for many artificial intelligence tasks but often
suffer from incompleteness. In this work, we propose to use pre-trained language models for …

In this work, we move beyond the traditional complex-valued representations, introducing
more expressive hypercomplex representations to model entities and relations for …

Recently, knowledge graph embeddings (KGEs) have received significant attention, and
several software libraries have been developed for training and evaluation. While each of …

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the
same real-world object. Recent advancement in KG embedding impels the advent of …

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To
better comprehend long complicated sentences and obtain accurate aspect-specific …

Knowledge graphs have emerged as a key abstraction for organizing information in diverse
domains and their embeddings are increasingly used to harness their information in various …

Conversational recommender systems (CRS) enable the traditional recommender systems
to explicitly acquire user preferences towards items and attributes through interactive …