Abstract
Knowledge Graphs (KG) are widely used abstractions to represent entity-centric knowledge. Approaches to embed entities, entity types and relations represented in the graph into vector spaces - often referred to as KG embeddings - have become increasingly popular for their ability to capture the similarity between entities and support other reasoning tasks. However, representation of time has received little attention in these approaches. In this work, we make a first step to encode time into vector-based entity representations using a text-based KG embedding model named Typed Entity Embeddings (TEEs). In TEEs, each entity is represented by a vector that represents the entity and its type, which is learned from entity mentions found in a text corpus. Inspired by evidence from cognitive sciences and application-oriented concerns, we propose an approach to encode representations of years into TEEs by aggregating the representations of the entities that occur in event-based descriptions of the years. These representations are used to define two time-aware similarity measures to control the implicit effect of time on entity similarity. Experimental results show that the linear order of years obtained using our model is highly correlated with natural time flow and the effectiveness of the time-aware similarity measure proposed to flatten the time effect on entity similarity
Federico Bianchi
Postdoctoral Researcher at Stanford University
My research interests include developing and understanding large language (and vision) models and recommender systems.