# Symbiotic Child Emotional Support with Social Robots and Temporal Knowledge
Graphs

Isabella Saccardi  Duygu Sezen Islakoglu  Anouk Neerincx  Federica Lucia
Vinella  Utrecht University, Department of Information and Computing Sciences,
Utrecht, The Netherlands

###### Abstract

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.

###### keywords:

Social Robots Emotional Support Youth Care Human-Centred Design Human-robot
Interaction Knowledge Graphs

111The authors contribute equally to this paper.

, ††footnotemark: , ,

##  1 Introduction

Social robots are increasingly popular as Emotional Support (ES) actors in
youth-care programs for children (e.g., in hospitals [Jeong et al., 2015,
Moerman and Jansens, 2021, Meghdari et al., 2016], schools [Kirstein and
Risager, 2016, Belpaeme et al., 2018], and youth-care centers [Saldien et al.,
2010, Huijnen et al., 2016]). Yet, it is still unclear which role the social
robot should entail and what a fulfilling child-robot relationship looks like
in this domain. Experts in youth care and families have expressed the need to
increase knowledge regarding the risks and opportunities of employing social
robots in youth healthcare practices. Consequentially, scientific empirical
studies are beginning to advance robot-supported ES programs for youth to
establish regulated and safe practices [Neerincx et al., 2021, Moerman et al.,
2019].

Neerincx et al. [2021] for example, evaluated the robot-child interaction as a
means to break the ice, run standardized tests, and play educational games in
settings such as hospitals and schools. Several other studies have proposed
methods to establish empathy in the child-robot interaction to aid social
presence, engagement, and social support (e.g., [Leite et al., 2014, Ligthart
et al., 2018, Belpaeme et al., 2012]). Jointly, the general public is shifting
toward the notion of assistive social robots as capable of a deeper, more
intimate understanding when communicating with their human counterparts
[Gamborino et al., 2019].

Methodologically, researchers have deployed state-of-the-art machine learning
techniques such as Reinforcement Learning to form mood estimation as a social
profile predictor in social robots [Gamborino et al., 2019]. Withal, these
techniques often lack explainability and interpretability [Puiutta and Veith,
2020] due to implicit policies and hidden processes the robot relies upon to
make decisions. The outcome is a black-box phenomenon where humans do not know
how or why the AI solved a given problem the way it did [Adadi and Berrada,
2018]. We present a way to deal with the specific problem of AI as a black box
within the robot-child ES domain by proposing the development of a semantic
representation of child events. We intend to guide and adapt the robot-child
interaction while informing the expert of the child’s emotional history. Our
framework relies on the domain-specific Knowledge Graph (KG) construct to map
events into triples (Entity-Relation-Entity). These triples would assist the
robot in tailoring the ES statements to the child. They would also inform the
expert by monitoring the temporal evolution of the child’s emotional state.

Knowledge Graphs (KG) – also known as semantic networks – represent networks
of real-world entities (i.e., objects, events, situations, or concepts) and
illustrate the relationship between them [Ji et al., 2022]. This paper focuses
on generic KGs based on triples (subject, relation, object) and Temporal KGs
(TKG) where each triple is associated with a timestamp or time interval and
largely models evolving social networks. KGs are already widely used in
applications such as personal assistants, question answering, and
recommendation systems [Wang et al., 2017]. In the human-robot paradigm, KGs
have been studied for human-robot collaboration [Ding et al., 2019, Zhang et
al., 2021b], human-robot knowledge transfer [Shukla et al., 2015], and
situational human-robot interaction [Zhang et al., 2021a]. However, robot-
aided child ES supported by KGs and TKGs is altogether a novel application.

The contribution of the proposed framework is two-fold. One is to supply
adaptive real-time ES to the child through the social robot; the other is to
give contextualized knowledge to the expert. Firstly, we suggest developing an
abstract KG built on the literature on child ES that we will construct from
triples. Next, we plan to generate an annotated ES corpus from crowdsourcing
annotation ([Smith et al., 2014, Dennis et al., 2013, Liu et al., 2021])
motivated by the scenarios built from the abstract KG. The corpus would guide
the prediction of adaptive ES statements. Our framework is an example of a
symbiotic relationship between robots and humans. Here, the child and the
expert keep the social robot up-to-date while the social robot provides
emotional support to the child and assists the expert. Thanks to their
interpretable nature, KGs are ideal for information visualization and
explanation aiding with the development of human-centered robot-child ES.

The rest of the paper is as follows. Section 2 presents the five phases of the
experimental design, the plan, and the procedure. Section 3 discusses the
limitations and challenges of the proposed framework. Section 4 concludes the
work.

##  2 Experimental Design, Plan and Procedure

In this paper, we propose a research pipeline divided into five phases namely
1. instantiation of KGs, 2. creation of ES corpus, 3. knowledge update, 4.
support, and 5. evaluation. While all five phases are designed to follow each
other linearly, Phase 3 and Phase 4 will occur in a loop and reinforce each
other. Each participant in the current research will be appropriately informed
about the scope of the research and the data usage.

###  2.1 Phase 1: Instantiation of KGs

In this preliminary phase, we propose focusing on the instantiation of two
knowledge graphs. One deals with the theoretical grounding of stressors and
emotional states in children and is an abstract and generalized KG. The second
is a TKG offering a temporal overview of a child’s emotional states and
related situations.

Abstract Knowledge Graph. Firstly, we suggest running an extensive systematic
literature analysis on child ES research to gather theories on child stressors
coupled with qualitative coding, thematic analysis, and ground theory. The
result will be an abstract Knowledge Graph (KG) that will consist of Entity-
Relation-Entity triples on child stressors and emotional states known within
the psychological domain (e.g., child (Entity) refusing to go (negative
emotion/Relation) to school (Entity)). We propose using existing techniques to
build multiple scenarios of child stressors from the abstract KG. The abstract
KG is herein the ground-truth and constant featuring in all child ES programs.
Once assessed and approved by a set of invited experts (including experts in
pediatrics, education, and pedagogy), Phase 2 of the pipeline will utilize
this validated abstract KG to form scenarios for creating a domain-specific ES
corpus.

Child Temporal Knowledge Graph. The second type of graph will be a Temporal KG
(TKG) instantiated at the time of the first intake meeting with the child and
the expert. The expert will ensure that the entities of this graph will have
corresponding entities in the abstract KG. In other words, the abstract KG
will serve as a base for the child TKG. The child TKG instance will update
during the follow-up sessions with the expert and the social robot (Phase 3).
It will comprise the history of social interactions (e.g., family and friends)
and timestamps/time intervals.

###  2.2 Phase 2: Creation of ES corpus

We suggest developing a corpus of ES statements by crowdsourcing human
annotation. This phase is divided into sub-phases namely Scenarios, Corpus
creation, Corpus validation, Corpus classification, and Corpus augmentation.

#### Scenarios.

From the abstract KG formed in Phase 1, we propose focusing on a specific set
of triples characterized by negative or distressing situations. The selected
triples will be used as use cases for a set of hypothetical scenarios where
the child would typically receive ES.

#### Corpus creation.

With the given collection of scenarios, we propose to recruit crowd workers to
generate appropriate ES statements and assign an ES category to each triple.
An example of a question for this crowdsourcing task would be ”Write down
three possible sentences that you would say to a child who is experiencing
this situation.”. The resulting support statements will form an initial
unverified corpus of ES statements.

#### Corpus validation.

Afterward, the corpus will be validated by an independent set of crowd
workers. This sub-phase is needed to ensure that the selected statements are
appropriate for the ES program and the child. Crowd workers will rate on a
five-point Likert scale the appropriateness of the content for children of 8
to 12 years old. Statements with low rating or high standard deviation (e.g.,
mean$<$ given threshold, sd$>$given threshold) will be excluded from the final
corpus. The resulting body of statements will form a new, validated ES corpus.

#### Corpus classification.

In a follow-up crowdsourcing task, each crowd worker will be asked to
categorize a subset of statements (approx. 20) from the validated corpus with
one of the ten chosen classes from the literature ([Smith et al., 2014]222The
category of Deserving was based on reminding the distressed person of what
they deserve (e.g., some time off, some time with their friends). This
category was considered too complex for children and therefore excluded., Liu
et al. [2021]333Liu et al. [2021] categories - named strategies in their
article - partially overlap with the ones of Smith et al. [2014], except for
the Information strategy, which aims to provide information about the
situation. This was excluded due to limitations of the system, as providing
real-time complete information will likely not be possible., Dennis et al.
[2013] 444The category of Reassurance was added, as deemed suitable for
children., see Table 1). The intended result will be an annotated and
validated ES corpus where each triple is associated with one scenario which is
categorized with one class. To achieve this, we plan to calculate intra-rater
agreement, and propose using a free-marginal kappa [Randolph, 2005].
Statements categorized with a marginal kappa $<$ given threshold will be
excluded from the corpus as too ambiguous.

#### Corpus augmentation.

We expect the final annotated corpus to be of limited size (not exceeding 100
statements) and sub-divided into ten ES categories. To ensure a higher
variability of the sentences, we propose to deploy a pre-trained language
model to augment the corpus by paraphrasing. Then, the expert will review the
final corpus: any sentence deemed inappropriate will be excluded. After the
validation of the new instances, the resulting augmented ES corpus will be
used in the next phase of this research and will be continuously updated with
exceptions encountered by the robot during the interaction with the child (see
Phase 4).

###  2.3 Phase 3: Knowledge update

In this phase, the child TKG updates with the child-robot and expert-robot
interactions. The robot-child interaction will create new triples with time
information as the child talks about past events and expresses emotions about
events. If the time information is missing or is inaccurate, the robot will
disambiguate by asking questions during the interaction. Thanks to its
updating property, the child TKG will be robust and adaptable to changes.
Similarly, the expert will moderate the child TKG by applying changes to the
graph whenever needed (e.g., by adding or fixing triples). Both kinds of child
TKG updates (automated and manual) will support the notion of an expert-in-
the-loop approach.

###  2.4 Phase 4: Support

We divide this phase into two types of support. One deals with the ES offered
to the child. The other consists of the support that the expert receives from
the framework in the form of context and knowledge. We explain these two kinds
support in the following paragraphs.

#### Providing adaptive support to the child.

This support deals with the ES sentences dispensed by the robot during the
interaction with the child. The child’s sentences will be converted to triples
and saved in the child’s TKG. Then, a similarity function 555The similarity
function will return a similarity score calculated by comparing two triple
embeddings. will decide whether this triple corresponds to an edge in the
abstract KG.666Both abstract KG and child TKG will be heterogeneous as they
will contain different types of entities and relations. However, we expect to
encounter a limited number of entity types (parents, friends, teachers, etc.)
and a higher number of relation types. The latter will increase the difficulty
of calculating the similarity score between two triples. If the similarity
function returns a sufficiently high similarity score (determined by a
proposed confidence threshold), the model will use the corresponding ES
category to produce a randomly picked ES statement matching that category.
This way, the ES statement can always be justified by the abstract KG and,
ultimately, by the literature. We suggest keeping a log of the ES statements
used to avoid redundancy and dispense a semi-natural dialog between the robot
and the child.

#### Providing support to the expert.

The framework makes the expert and the social robot dynamic partners that
learn from each other. From the child’s TKG, the expert will be able to gain
several benefits.

(a) Access contextual history and handle exceptions. The child TKG enables the
extraction of statistics about the child’s states over time. The expert could
have access to a variety of information, for instance, the duration of a
specific relation (e.g., feeling angry), the frequency of the events, the gaps
between interactions, and the patterns in relational order (e.g., ”Whenever a
homework assigned, Child feels depressed.”). The expert can determine which
outliers to discard according to an arbitrary threshold. Overall, statistical
data will help experts catch and handle exceptions (i.e., outliers, missing
information).

(b) Predict patterns and assess the effectiveness of the ES program. Thanks to
data-driven state-of-the-art TKG methods [Cai et al., 2022], it is possible to
predict the emotional state of the child within a given timeline. Since we
plan to handle emotions as relations (e.g., feeling happy) in the child’s TKG,
a link prediction task could help the expert assess the probability of an
emotional state for any given time. Specifically, the child’s TKG can help
compute the probability of a quadruple (Child, feeling happy, Child, Date) to
manifest at some point in time. Using prediction methods such as Graph Neural
Networks [Wu et al., 2021], the events that are close to a given date and
neighbor entities like parents will contribute to the prediction. The
evolution of the emotional states will grant insights into the effects of the
support program.

(c) Complete the knowledge about the child while preserving privacy. Another
advantage of having the child TKG is the creation of anonymized scenarios when
the expert decides to consult other authorities. Temporal walks from child
entities can create chronological stories carrying rich contextual information
777Information taken from the subgraph that covers relevant interactions with
relevant entities over time. while preserving privacy. Hence, an expert can
utilize temporal walks to obtain better-tailored suggestions.

###  2.5 Phase 5: Evaluation

From the evaluation, we intend to assess the capacity of the proposed
framework to function in real-life scenarios and within the domain herein
presented. We also suggest evaluating ways that the experts and the children
perceive the child-robot interaction with the given framework according to a
set of criteria. For evaluating the framework, we suggest utilizing both
computational evaluative methods used for knowledge graphs (i.e. accuracy for
classification, average precision for future link prediction) and qualitative
methods established in the field of Human-Computer-Interaction (i.e.perceived
anthropomorphism, animacy, likeability, perceived intelligence, and perceived
safety of the robot [Bartneck et al., 2009]).

##  3 Limitations and challenges

#### Data sparsity.

Our proposed version of child TKG strongly relies on the robot-child
interaction to generate triples. However, when the interaction is not common –
which can likely occur in real-life settings – the TKG may not sufficiently
capture the events that are thought relevant for the ES program. For this
reason, we believe that the expert can play a fundamental role in mitigating
the lack of information (e.g., due to the cold start problem, outliers, data
sparsity, etc.) since they can monitor the state of the child’s KG and enrich
it with complementary information from other sources.

#### Accuracy.

False negatives might occur when the ES framework does not identify input and
does not provide an appropriate ES statement. False-positive might occur when
the ES statement tailors a virtually nonexistent problem or the causal
relationship is incorrect. For both types of prediction errors, the role of
the expert will be crucial to correct the prediction and to complete the
missing knowledge in graph.

#### Ambiguity.

Ambiguity can happen when the interaction is novel or when it is not clear how
it can be translated into triple form. This issue can be prevented by
programming the robot to ask questions when ambiguity is present in the
child’s TKG (e.g., triplets with ambiguous sentiment).

#### Modality.

Emotion recognition encompasses other modalities such as gestural interaction,
facial expressions, and body posture. These modalities can capture sentiment
that children may find difficult to verbally express [Neerincx and Luijk,
2020]. However, given the complexity of the domain, we suggest experimenting
with the proposed language-based approach and in the future, introduce non-
verbal modalities to complete the graphs.

##  4 Conclusions

We present an experimental framework based on Knowledge Graphs providing
Emotional Support (ES) to children aged 8 to 12 through child-robot
interaction in schools and youth care centers. The research pipeline is
divided into several phases, spanning from the instantiation of Knowledge
Graphs to the generation and validation of an ES corpus. The end result is an
adaptive framework for child-robot interaction that adapts to the child’s
emotional states over time. It also allows the expert to obtain contextualized
knowledge to improve the ES program. Ours is a proposition for a mutualistic
symbiotic relationship between the robot, the expert, and the child through
semantic knowledge creation where each entity can mutually benefit from the
interaction.

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##  5 Appendix

Category |  Explanation |  Example
---|---|---
Appreciated (APP) |  This statement reminds the child of his / her importance. |  I’m glad you are here for..
Supported (SUP) |  This statement makes the child feel supported. |  I am here for you.
Praise (PRS) |  This statement praises the child, focusing on his / her good qualities. |  You are a good child.
Encouragement (ENC) |  This statement encourages the child to overcome the situation. |  You can do this.
Empathy (EMP) |  This statement legitimize the child’s feeling, making him / her feel understood. |  I understand you are sad.
Practical Advice (PRA) |  This statement provides practical advices to deal with the situation. |  Just do your best.
Emotional Advice (EMO) |  This statement suggests to the child how to deal with his / her emotions. |  It’s okay to cry.
Blameless (BLA) |  This statement reminds the child that the stressful situation is not his / her responsibility. |  It’s not your fault.
Consolation (CON) |  This statement encourages the child to focus on positive aspects of the situation. |  At least that is over with, for the time being.
Reassurance (REA) |  This statement reassurance the child that things will get better. |  It’s going to be alright.
Table 1: ES statement categories, adapted from Smith et al. [2014], Dennis et
al. [2013], Liu et al. [2021].