8 research outputs found
Diachronic Embedding for Temporal Knowledge Graph Completion
Knowledge graphs (KGs) typically contain temporal facts indicating
relationships among entities at different times. Due to their incompleteness,
several approaches have been proposed to infer new facts for a KG based on the
existing ones-a problem known as KG completion. KG embedding approaches have
proved effective for KG completion, however, they have been developed mostly
for static KGs. Developing temporal KG embedding models is an increasingly
important problem. In this paper, we build novel models for temporal KG
completion through equipping static models with a diachronic entity embedding
function which provides the characteristics of entities at any point in time.
This is in contrast to the existing temporal KG embedding approaches where only
static entity features are provided. The proposed embedding function is
model-agnostic and can be potentially combined with any static model. We prove
that combining it with SimplE, a recent model for static KG embedding, results
in a fully expressive model for temporal KG completion. Our experiments
indicate the superiority of our proposal compared to existing baselines
Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval
Within the last 15 years, the field of Music Information Retrieval (MIR) has made tremendous progress in the development of algorithms for organizing and analyzing the ever-increasing large and varied amount of music and music-related data available digitally. However, the development of content-based methods to enable or ameliorate multimedia retrieval still remains a central challenge. In this perspective paper, we critically look at the problem of automatic chord estimation from audio recordings as a case study of content-based algorithms, and point out several bottlenecks in current approaches: expressiveness and flexibility are obtained to the expense of robustness and vice versa; available multimodal sources of information are little exploited; modeling multi-faceted and strongly interrelated musical information is limited with current architectures; models are typically restricted to short-term analysis that does not account for the hierarchical temporal structure of musical signals. Dealing with music data requires the ability to tackle both uncertainty and complex relational structure at multiple levels of representation. Traditional approaches have generally treated these two aspects separately, probability and learning being the usual way to represent uncertainty in knowledge, while logical representation being the usual way to represent knowledge and complex relational information. We advocate that the identified hurdles of current approaches could be overcome by recent developments in the area of Statistical Relational Artificial Intelligence (StarAI) that unifies probability, logic and (deep) learning. We show that existing approaches used in MIR find powerful extensions and unifications in StarAI, and we explain why we think it is time to consider the new perspectives offered by this promising research field