Implementation and Evaluationof Personal Ontology Building Systemwith Virtual Agent
Shota Nakatani1(B) , Sachio Saiki1 , Masahide Nakamura1,2 ,and Kiyoshi Yasuda3
1

Graduate School of System Informatics, Kobe University,
1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
shota-n@ws.cs.kobe-u.ac.jp, sachio@carp.kobe-u.ac.jp,
masa-n@cs.kobe-u.ac.jp
2
Riken AIP, 1-4-1 Nihon-bashi, Chuo-ku, Tokyo 103-0027, Japan
3
Osaka Institute of Technology, Omiya 2-16, Asahi-ku, Osaka 535-8585, Japan
fwkk5911@mb.infoweb.ne.jp
Abstract. Integrating the virtual agent, IoT, and cloud technologies,
we have been developing a communication care system, where a person with dementia can talk to an agent every day at home. In order toachieve active conversations with a person with dementia, it is essential
to personalize topics to every individual person with dementia. In thispaper, we propose a method that represents and manages “the knowledgeof individual person” (Personal Ontology) with Linked Data, in order togenerate person-centered topics to the individuals. The proposed methodwith Linked Data does not only enable ﬂexible management of personal
ontologies, but also does not only represent complex structure in the ontlogies. Moreover, connecting the data to the external Linked Open Data
makes it eﬃcient to obtain various knowledge relevant to the conversation from the wisdom of crowds in the Internet. Also, in this paper, basedon the proposed method, we develop a prototype system which dynamically builds Personal Ontology through the conversations between theuser and virtual agent. Speciﬁcally, virtual agent inquires of the user (U)the attribute (P) such as hobbies, favorite singers and any others. Thenvirtual agent collects the answers (O) of the user. After that, personal
information represented by tuple U, P, O is added to Personal Ontology.
Furthermore, we conﬁrm collectability of Personal Ontology through theprototype system by experiments with a few subjects.
Keywords: Linked Data · Personal Ontology · Home care ·Virtual agent

1

Introduction

Japan is facing a hyper-aging society. According to the Cabinet Oﬃce, while
the total population of Japan is decreasing, the propotion of the elderly population is rising. The elderly population is predicated to account for 30% of thec Springer Nature Switzerland AG 2019

V. G. Duﬀy (Ed.): HCII 2019, LNCS 11582, pp. 391–403, 2019.
https://doi.org/10.1007/978-3-030-22219-2_30

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total. Also, the number of people with dementia (PWD) will reach 7 million
in 2025, where one-ﬁfth of ﬁve elderly people in Japan will suﬀer from dementia [2]. Hence, while more people need nursing care, there is the problem thatcaregivers are short. According to the Ministry of Health, Labor and Welfare,2.45 million caregivers are estimated to be needed by 2025 [4]. However, theestimated number of long-term care workers in 2025 is about 2.11 million, whichis short of about 0.34 million. Therefore, care and support for elderly people and
PWD who do not depend on human hand is necessary. Techniques to reducethe burden of nursing care by ICT technology are widely demanded, and various
researches are progressing.
Validation therapy [3] and reminiscence [10] are known as a non-drug therapy for symptoms of dementia. For these care methods, continuous conversations between caregiver and patient are important. However, since patients haveindividual diﬀerences in their symptoms, it is a heavy burden for families andcaregivers to respond constantly.
Among them, our research group is developing a dementia care system thatPWD daily can interact at home using IoT and ICT technology [9]. This system
uses a virtual agent (VA) which is a robot program with speech recognition andsynthesis technologies, enabling dialog communication with PWD. This systemaims to reduce the human burden by home care by doing part of the conventionalcare of human caregivers.
In order to implement active conversations with PWD in the dialogue system,
it is important to take topics close to individuals with PWD. In previous
search, we have proposed the two methods. The ﬁrst method is to dynamically generate personalized dialogues with life history and Linked Open Data
(LOD) [8]. The second method is to generate topics of trends and events according to user’s age. In these methods, we use the Center Method [7] in order to
get personal information and knowledge, called Personal Ontology. Speciﬁcally,
before the service is executed, PWD or caregiver is asked to answer the preliminary questionnaires based on the Center Method, and the service provider
registers those with the system. The system generates personalized dialogues
based on registered Personal Ontology.
However, the preliminary questionnaires has many questions, it is diﬃcult
for the user to answer all of them. The questions to be answered by the user
may be diﬀerent. There are also the questions which are unknown at the timeof the questionnaires but can be answered through conversation with the agent.
Furthermore, new knowledge that cannot be gotten by the questionnaires may
be gotten from the conversations. Thus, conceptions that appear in Personal
Ontology is widely diﬀerent from individuals. In other words, collecting Personal
Ontology by preliminary questionnaires has limits on what can be collected. In
addition, collection of Personal Ontology by free description makes it diﬃcult topredict the items to be described, making it diﬃcult to manage in the system.
Moreover, in order for the system to develop topics in dialogue, conceptions
related to Personal Ontology are required.

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In order to solve the above problem, in this study, we propose system torepresent and manage user’s Personal Ontology with Linked Data and dynamically construct Personal Ontology through conversations between agent anduser. Concretely the proposed method consists of the following three steps.
A1: Construction of the Personal Ontology through the conversations
A2: Management of Personal Ontology with Linked Data
A3: Generation of link to utilize external LOD
In A1, the agent discovers information related to the use from the dialogue
with the user and adds it as an ontology. Elements constituting an ontology
can basically be represented by triple of U, P, O. U represents a user, P is aproperty, and O is an object.
Next, in A2, the system converts the Personal Ontology acquired in A1 to
RDF format, and represents and stores it as Linked Data. The system assigns
URI to each of U , P , O acquired in A1 to be in the form of Linked Data suchas below.
<http://example.org/resource/U ><http://example.org/
property/P > <http://example.org/resource/O>.
<http://example.org/resource/U > shows resource representing the user.
<http://example.org/resource/O> shows resource representing the information related the user. <http://example.org/property/P > shows link representing relation between the user and the information.
Finally, in A3, the system links Personal Ontology in the system and external
Linked Open Data (LOD). Thus, the system can acquire related conceptions
necessary for the development of topics from collective knowledge of the Internet.
In this study, speciﬁcally, we use DBpedia [5] which is LOD based on Wikipedia.
In addition, to build Personal Ontology through conversation in this research,
agents use the method that the agent questions from the user and builds fromthe answer. Personal Ontology is triples represented by U, P, O, meaning “U ’s
P is O”. The question asked to the user to construct the ontology is “What(Who,
Where) is U ’s P ?” By this, the system can obtain a user’s response including anoun phrase equivalent to O. A noun phrase is extracted based on the result ofdependency analysis of this response so that the system can be constructed as
Personal Ontology.
We implement the above proposed method as a prototype system. We conﬁrm
that depending on the system, it is possible to appropriately extract the nounphrase corresponding to the answer from the response sentence obtained by theinteractive question between the agent and the user. Then, we conﬁrm that thesystem can express the obtained answer as Linked Data as Personal Ontology.
As a result of the experiment, if the response from the user to the agent’s
question is not grammatically broken, and the syntax analysis is normal, it ispossible to properly extract the answer section and express it as a Personal
Ontology.

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2Preliminary

2.1

Communication Care System for Person with Dementia

Promotion of home care is being carried out in Japan due to an increase in thenumber of person with dementia (PWD) and an increase in the number of people
who wish to provide care at home. As care for people with dementia, personcentered care (PCC) is an ideal care, which monitors and understand individual
circumstances, and plans and executes optimized care. Unlike standardized care,
it is necessary to observe the subject carefully in order to provide care that is
tailored to individuals. There is a limit to doing PCC at home because it places
a big mental and physical burden on caregivers.
Our research group is developing Virtual Care Giver (VCG), a systemthat provides communication daily communication to home demented people.
Figure 1 shows the image of VCG. VCG is implemented dialogue with a PWD
by utilizing a Virtual Agent (VA) with speech dialogue. Moreover, in addition
to voice dialogue, content such as daily calls and reminders, images and videos
can be provided. With VCG, it is possible to realize a conversation partner of
the PWD who does not depend on time, and it can be expected to reduce theburden on human carers.
In order to realize an active conversation between a PWD and an agent, it isnecessary to provide conversation adapted to the individual PWD. In previous
research, we propose a method to dynamically generate topics close to individuals by utilizing life history and Linked Open Data (LOD) [8], and a method
to generate topics of trends/events according to user’s age. These methods are
aimed at realizing active conversation close to individuals by developing conversation by combining information related to the PWD and related knowledge
from open data.
2.2

Personal Ontology

As mentioned in Sect. 2.1, in order for the system to generate a topic close to
an individual, it is necessary to acquire information concerning the individual.
In this research, information and knowledge related to individuals that can be
managed by the system are called Personal Ontology. Representative ones of
Personal Ontology include proﬁles and life history.
Life history is information on what kind of life the person has lived, suchas hometown, family structure, school, work, memories, hobbies. Compared tothe most recent event, life history is likely to remain in memory of PWD, so it
has been actively utilized in previous studies. In previous research, based on thecenter method [7] and Mimamori/Tunagari note [6] actually used at the site ofnursing care, we prepare preliminary questionnaires about the typical data itemsof life history and the PWD or the caregiver answer it. The items that could
not be ﬁlled in preliminary questionnaires are acquired at the time of dialogue
by the agent asking questions.

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Fig. 1. Virtual Care Giver

2.3

Linked Data, Linked Open Data (LOD)

Linked Data [1] is data linked by meaningful links using Web technology. Linked
Data is one of technical components to realize Semantic Web. It is described in
the Resource Description Framework (RDF) which structurally expresses arbitrary information on the web as a resource. Tim Berners-Lee deﬁnes the following
four principles regarding Linked Data [1].
1. Use URIs as names for things.
2. Use HTTP URIs so that people can look up those names.
3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL).
4. Include links to other URIs. So that they can discover more things.
What is commonly shared by opening Linked Data as open data on theInternet is called Linked Open Data (LOD). By linking the opened data witheach other, it is possible to form a huge knowledge database on the web.
In the model of RDF, data is represented by a triple that combines threeelements: a subject, a predicate, and an object. RDF is represented by a directed
graph; a subject and an object are represented by an ellipse (resource), and a
predicate is represented by an arrow (link) connecting two ellipses. However, it
is also possible to make the object a literal rather than a URI, in which case theobject is represented by a rectangle.
Figure 2 shows the example of RDF graph. The URI can be described by omitting it by using the namespace preﬁx. In this ﬁgure, “dbpedia:Tokyo” refers to“http://dbpedia.org/resource/Tokyo.” In this example, “URI of dbpedia:Tokyo

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Fig. 2. Example of RDF graphis indicative of “Tokyo”, and “the country where Tokyo is located is Japan” aretwo representations.
In addition, the syntax of RDF is expressed as follows by enclosing the URIof the subject, predicate, and object with <>, followed by a period (.).
<http://dbpedia.org/resource/Tokyo> <https://www.w3.org/2000/
01/rdf-schema#label> “Tokyo”.
<http://dbpedia.org/resource/Tokyo><http://dbpedia.org/ontology/country><http://dbpedia.org/resource/Japan>.
You can also write as follows by using the namespace preﬁx as in the graph.
PREFIX dbpedia: <http://dbpedia.org/resource/>
PREFIX dbpedia-owl: <http://dbpedia.org/ontology/>
PREFIX rdfs: <https://www.w3.org/2000/01/rdf-schema\#>
dbpedia:Tokyo rdfs:label ”Tokyo” .
dbpedia:Tokyo dbpedia-owl:country dbpedia:Japan .

In this paper, we use “http://example.org/resource/” and “http://
example.org/property/” as a namespace URI, but using each preﬁx “ex:” and“ex:prop”.

3

The Proposed Method

3.1

Goal and Approach

In this study, we propose a method to dynamically acquire, accumulate and use
Personal Ontology which is needed for generating personal adaptation dialogue
in dialogue system for PWD through conversation.

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Continuation of daily conversation is important in care for dementia. Furthermore, since the conversation that is close to individual PWD is important,the more individual ontologies that trigger the topic, the more patterns of topics
that can be generated. However, since the number of items in the preliminary
questionnaire adopted in the current system is large, it is diﬃcult to answer
strictly to all and items to be answered by people are diﬀerent. Also, it may bepossible to discover new Personal Ontology that do not exist in the questionnaire
item from the daily conversation. Concepts appearing in such a newly obtained
Personal Ontology are greatly diﬀerent for each user. When considering a system that manages and accumulates Ontology having diﬀerent structures amongindividuals, it is diﬃcult to apply a relational database (RDB) that requires
schema to be ﬁxed in advance. Also, in order to develop a topic based on theaccumulated Personal Ontology, related concepts are necessary. However, since
they are diverse, it is not realistic to manage all of them within the system.
For the above reasons, in this study, we propose a mechanism to dynamicallyconstruct Personal Ontology by expressing and managing with Linked Datathrough dialogue between agent and user. Speciﬁcally, the proposed method
consists of the following three steps.
A1: Construction of the Personal Ontology through the conversations
A2: Management of Personal Ontology with Linked Data
A3: Generation of link to utilize external LOD
3.2

A1: Construction of the Personal Ontology Through theConversations

The agent dynamically extracts and creates ontology of the user’s individual
through conversation with the user. The personal ontology is deﬁned by a setof triple of U, P, O. U is a user (subject), P is a property (predicate), O is anobject. When U = “Nakatani”, P = “favorite sport” and O = “Tennis”, it means“Nakatani’s favorite sport is tennis”.
We consider about two approaches as a method of constructing Personal Ontology through conversation. The ﬁrst method is a method inwhich an agent asks user questions and builds from the answer. In thismethod, the system generates a question asking for O based on P , inquiresof user, obtains the user’s answer, and then builds a triple of Personal
Ontology. For example, the agent makes a question such as “Where is
Nakatani’s hometown?” and “What is your favorite sport?”, and builds Personal Ontology from the answer of “Hyogo prefecture” and “Tennis”, such as
Nakatani, Hometown, Hyogo, Nakatani, Favorite sport, Tennis.
The second method is a method of building by understanding the utterance of
the user by the agent. In this method, natural language processing technology isused to analyze user’s free utterance and extract what can become an Ontology.
A method of extracting sentences that can become Personal Ontology from freeutterances is currently being studied.

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Fig. 3. System architecture of proposed system

3.3

A2: Management of Personal Ontology with Linked Data

The system dynamically converts the Personal Ontology acquired in A1 to RDF
format, expresses it as Linked Data and accumulates it. Speciﬁcally, for eachU, P, O acquired in A1, let U be a resource representing the user, O be aresource representing information related to the user, and P be a link representing the relationship between the user and the information.
Next, URIs are assigned to P and O. For the assignment method, let P
for “ex-prop:P ”, O for “ex:O”. Also, U refers to the name of the user, but
assigning a URI in the same way as P and O will make it impossible to distinguishinformation of those users if they have the same name. In order to prevent this,ID is given for each user beforehand, URI is assigned to user ID (uid), and it is“ex:uid”. Then, in order to express what the URI means to human to understandeasily, describing with the character string label (“rdfs:label”). In this way, it ispossible to express “ex:uid represents U ”.
ex:uid rdfs:label “U ”.
Similarly for “ex-prop:P ”, “ex:O”, the following description is added so thatyou can directly obtain what the URI means as a character string.
ex-prop:P rdfs:label “P ” .
ex:O rdfs:label “O” .
In this way, by describing U, P, O acquired in A1 in RDF format as follows,
it expresses the same meaning as “U ’s P is O”.

Implementation and Evaluation of Personal Ontology Building System399

ex:uid rdfs:label “U ” .
ex-prop:P rdfs:label “P ” .
ex:O rdfs:label “O” .
ex:uid ex-prop:P ex:O .
The previous example, Nakatani, Favorite sport, Tennis, is converted to
RDF format as follows.
ex:uid0 rdfs:label “Nakatani” .
ex-prop:FavoriteSport rdfs:label “favorite sport” .
ex:Tennis rdfs:label “Tennis” .
ex:uid0 ex-prop:FavoriteSport ex:Tennis .
Also, if O consists of multiple words and phrases, use “rdf:Bag” to group
resources. According to the principle of Linked Data (see Sect. 2.3), resources
should be referred to by URIs based on word. Therefore, when O is composed ofa multiple words and phrases, it is not preferable to express it with the URI of theaforementioned method. Besides, it makes it diﬃcult to create a link to a resourcein external LOD which is described later. Therefore, when O contains pluralwords o1, o2, it is converted using “rdf:Bag” as follows. As a result, resources
can be generated only with words contained in O, and can be expressed without
information loss.
ex:uid ex-prop:P :a1 .
:a1 rdfs:label “O” .
:a1 rdf:type rdf:Bag .
:a1 rdf: 1 ex:o1 .
:a1 rdf: 2 ex:o2 .
As an example, converting Nakatani, Memories of childhood, reading comics
and playing tennis to RDF format is as follows.
ex:uid0 ex-prop:MemoriesOfChildhood :a1 .
:a1 rdfs:label “reading comics and playing tennis” .
:a1 rdf:type rdf:Bag .
:a1 rdf: 1 ex:comics .
:a1 rdf: 2 ex:tennis .
This allows to divide O into word level resources and assign URIs to each. It
is easier to create a link to a resource in external LOD. The RDF data obtainedby the above conversion is stored in a dedicated database called an RDF store.

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A3 : Generation of Link to Utilize External LOD

By dynamically linking the Linked Data in the system accumulated in A2 withthe external LOD, it becomes possible to procure related knowledge necessary
for the development of the topic from the LOD. In this research, we use DBpedia[5] which is LOD extracted from Wikipedia. DBPedia is used to link between
various publicly available LODs, and functions as a hub of the data set.
In this case, for each resource “ex:O” constructed in A2, if there is a resource,“http://ja.dbpedia.org/resource/O(dbpedia:O)” pointing to the same concept in DBpedia, link “ex:O” to “dbpedia:O”. For the link, use a property,“http://www.w3.org/2002/07/owl#sameAs(owl:sameAs)” indicating that thetwo resources are the same. Speciﬁcally, when “dbpedia:O” exists, the following
RDF data is additionally registered in the system.
ex:o owl:sameAs dbpedia:o .
By doing this, “ex:O” and “dbpedia:O” are referenced diﬀerently but are
regarded as the same object, and can be connected to external LOD. When theagent develops a topic based on “ex:O”, it is possible to retrieve information
related to “dbpedia:O” for an external RDF store with “ex:O”. This allows
to eﬃciently procure from external LODs without having to manage the hugeknowledge related to “ex:O” on own system.
3.5

System Architecture

The overall architecture of the proposed system is shown in the Fig. 3. First, fromthe response of the user U obtained through the conversation with the agent, theproperty P and the object O are extracted by A1 and the system constructs theset of triplet Personal Ontology U, P, O. Next, each U, P, O generated in A1
is converted into RDF format data by A2 and stored in the RDF store dedicatedto Personal Ontology. Finally, in order to procure the knowledge related to O
obtained in A1 from the external LOD, the system creates a link by DBpedia
URIs by A3 and store it in the RDF store.
The Personal Ontology stored in the RDF store is used when the systemgenerates topics for individuals. It is also possible to acquire related knowledge
from external LOD and make it a trigger to develop topics.

4

Implementation of Prototype

In this section, prototype implementation of the system having the functionsof A1, A2, A3 mentioned in Sect. 3 is performed. In implementing the system,
the function A1 for extracting the answer part from the user’s response for theagent’s question is important. The triple U, P, O which is a representation of a
Personal Ontology means “U ’s P is O”. The question to construct an Ontology

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401

is “What(Who, Where) is U ’s P ?”. This allows to obtain a response including
a noun phrase equivalent to O. In implementing this processing for extracting
noun phrase from the response sentence of the user, we used the “Communication
R API”” in this research. This API can analyze the structure
Engine “COTOHA
of Japanese text and analyze dependency between morphemes. Based on the
result obtained by applying this API to the response of the user, by extracting
the noun phrase including the modiﬁer part for the noun, it is set as the user’s
answer O to the agent’s question. From the above, we generate triple U, P, O.
In the function A2, when the noun phrase O extracted from the user’s response
is not a single noun or a proper noun, morphological analysis and named entity
R API”, and the system registers
recognition are performed with “COTOHA
multiple words in the RDF store.
We use Apache Jena Fuseki for the RDF store which manages and accumulates these constructed Personal Ontology. Apache Jena is a Java library forprocessing data in RDF format. Apache Jena Fuseki has a server function thatcan manage and search RDF data by SPARQL.
The technology used for implementing the system for constructing the Personal Ontology is as follows.
– Development Language: Java– Java Library: Apache Jena
– Web Server: Apache Tomcat– Web Service FrameWorks: Apache Axis2– RDF Store: Apache Jena Fuseki
R API”– Web API: Communication Engine “COTOHA

5

Evaluation Experiment

In this section, experiments are conducted to determine by the prototype system whether the noun phrase corresponding to the answer can be appropriately
extracted from the user’s response sentence, and whether the obtained answer
can be expressed as Personal Ontology with Linked Data format.
Below is a partial excerpt of the question contents of the agent and the user’s
response to it. The actual dialogue is performed in Japanese. The following is
the English translation by the authors. A represents an utterance from an agent,and U represents a utterance from a user.
A : What was your “memory of school life”?
U : Memory of school life was fun having a variety of mahjong with friends,
reading comics, playing soccer and playing video games
A : Where is your “favorite place”?
U : I love Okinawa.
Next, the Personal Ontology constructed by the above question-answering isshown below. Theactual Personal Ontology is also constructed in Japanese. The
following is English translation by the authors.

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cs27 : uid001
cs27 −prop : MemoryOfSchoolLife[r d f : type r d f : Bag ;rdf : 1 cs27 : Friends ;r d f : 2 c s 2 7 : mahjong ;r d f : 3 c s 2 7 : Comics ;rdf : 4 cs27 : Soccer ;r d f : 5 c s 2 7 : Video Games ;r d f s : l a b e l ” Having a v a r i e t y o f mahjong withf r i e n d s , r e a d i n g comics , p l a y i n g s o c c e r
and p l a y i n g v i d e o games ”] ;cs27 −prop : F a v o r i t e P l a c e
c s 2 7 : Okinawa ;In the question-answering above, the system was able to extract noun phrases
and express them as RDF form as Personal Ontology. Particularly as a nounphrase corresponding to “memory of school life”, the part of “Having a variety of mahjong with friends, reading comics, playing soccer and playing video
games” existed in the user response sentence, so it was able to extract the answer
appropriately. However, in the dialogue experiments not described above, there
were errors in extraction of answers due to Japanese grammar errors and disappointing in response to users and misunderstanding of API.
Therefore, in the case where there is a correct response as a Japanese grammar from a user to an agent’s question, the system can appropriately extract
the noun phrase corresponding to the answer of the user.

6

Conclusion

In this paper, we propose a method to accumulate and manage knowledge (Personal Ontology) about individual users in a dialog system that dynamicallygenerates personalized topics. In the proposed method, Personal Ontology is
acquired dynamically through conversation between user and agent, and theacquired information is expressed as Linked Data and accumulates in RDF
format. We also implemented a Personal Ontology construction system using
agents based on the proposed method. Using the system, we conﬁrmed whether
the system appropriately extracts the part corresponding to the answer fromthe response sentence of the user to the agent’s question in an interactive manner. Then we conﬁrmed whether the system can express the obtained answer as
Personal Ontology in the form of Linked Data. When response sentence fromthe user to the agent’s question can be obtained without grammar breakdown,
appropriate processing could be done.
In our future works, we improve error extraction of answers from user
response sentences and study methods to generate new dialogue from the constructed Personal Ontology.

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Acknowledgements. This research was partially supported by the Japan Ministry of Education, Science, Sports, and Culture [Grant-in-Aid for Scientiﬁc Research(B) (16H02908, 18H03242, 18H03342), Grant-in-Aid for Scientiﬁc Research (A)(17H00731)], and Tateishi Science and Technology Foundation (C) (No. 2177004).

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