3 research outputs found
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs
Most knowledge graph completion (KGC) methods learn latent representations of
entities and relations of a given graph by mapping them into a vector space.
Although the majority of these methods focus on static knowledge graphs, a
large number of publicly available KGs contain temporal information stating the
time instant/period over which a certain fact has been true. Such graphs are
often known as temporal knowledge graphs. Furthermore, knowledge graphs may
also contain textual descriptions of entities and relations. Both temporal
information and textual descriptions are not taken into account during
representation learning by static KGC methods, and only structural information
of the graph is leveraged. Recently, some studies have used temporal
information to improve link prediction, yet they do not exploit textual
descriptions and do not support inductive inference (prediction on entities
that have not been seen in training).
We propose a novel framework called TEMT that exploits the power of
pre-trained language models (PLMs) for text-enhanced temporal knowledge graph
completion. The knowledge stored in the parameters of a PLM allows TEMT to
produce rich semantic representations of facts and to generalize on previously
unseen entities. TEMT leverages textual and temporal information available in a
KG, treats them separately, and fuses them to get plausibility scores of facts.
Unlike previous approaches, TEMT effectively captures dependencies across
different time points and enables predictions on unseen entities. To assess the
performance of TEMT, we carried out several experiments including time interval
prediction, both in transductive and inductive settings, and triple
classification. The experimental results show that TEMT is competitive with the
state-of-the-art.Comment: 10 pages, 3 figure
Symbiotic Child Emotional Support with Social Robots and Temporal Knowledge Graphs
In current youth-care programs, children with needs (mental health, family issues, learning disabilities, and autism) receive support from youth and family experts as one-to-one assistance at schools or hospitals. Occasionally, social robots have featured in such settings as support roles in a one-to-one interaction with the child. In this paper, we suggest the development of a symbiotic framework for real-time Emotional Support (ES) with social robots Knowledge Graphs (KG). By augmenting a domain-specific corpus from the literature on ES for children (between the age of 8 and 12) and providing scenario-driven context including the history of events, we suggest developing an experimental knowledge-aware ES framework. The framework both guides the social robot in providing ES statements to the child and assists the expert in tracking and interpreting the child's emotional state and related events over time
Symbiotic Child Emotional Support with Social Robots and Temporal Knowledge Graphs
In current youth-care programs, children with needs (mental health, family issues, learning disabilities, and autism) receive support from youth and family experts as one-to-one assistance at schools or hospitals. Occasionally, social robots have featured in such settings as support roles in a one-to-one interaction with the child. In this paper, we suggest the development of a symbiotic framework for real-time Emotional Support (ES) with social robots Knowledge Graphs (KG). By augmenting a domain-specific corpus from the literature on ES for children (between the age of 8 and 12) and providing scenario-driven context including the history of events, we suggest developing an experimental knowledge-aware ES framework. The framework both guides the social robot in providing ES statements to the child and assists the expert in tracking and interpreting the child's emotional state and related events over time