=Paper=
{{Paper
|id=Vol-2000/jist2017pd_paper2
|storemode=property
|title=Action Planning based on Open Knowledge Graphs and LOD (Poster)
|pdfUrl=https://ceur-ws.org/Vol-2000/jist2017pd_paper2.pdf
|volume=Vol-2000
|authors=Seiji Koide,Fumihiro Kato,Hideaki Takeda,Yuta Ochiai,Kenki Ueda
|dblpUrl=https://dblp.org/rec/conf/jist/KoideK0OU17a
}}
==Action Planning based on Open Knowledge Graphs and LOD (Poster)==
https://ceur-ws.org/Vol-2000/jist2017pd_paper2.pdf
Action Planning based on Open Knowledge
Graphs and LOD
Seiji Koide1 , Fumihiro Kato1 , Hideaki Takeda12 , Yuta Ochiai3 , and
Kenki Ueda3
1
National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430,
Japan,
takeda@nii.ac.jp,
WWW home page: http://www-kasm.nii.ac.jp/~takeda/index.html
2
SOKENDAI (The Graduate University for Advanced Studies)
3
Toyota Motor Corporation
Abstract. In this preliminary report, we show how we can realize ac-
tion planning by using open knowledge-bases and LOD like Linked Geo
Data, DBpedia, and WordNet, etc. To make a recommendation for car
drivers and passengers, we combine these open datasets by newly con-
structed ontologies of facilities and services. Then we develop the infer-
ence procedure to translate user requests into SPARQL queries to obtain
a recommendation on appropriate facilities and areas for users. Common
sense knowledge is also required in the reason process.
Keywords: DBpedia, LinkedGeoData, Knowledge-based system
1 Introduction
While Linked Data is now gradually growing to be the infrastructure of coming
Knowledge Society, we are still struggling to show the potential of Linked Data
to most people in basic industries. To cope with this situation and propel the de-
ployment of Semantic Web technology in the society, it is needed to demonstrate
the performance of linking distinct datasets and show the potential and useful-
ness of outbound and inbound linking data beyond enterprise data in higher
levels of diverse applications. However, although each collection of large linked
data such as DBpedia, Freebase, and OpenCyc are a kind of isolated showcase
of LOD with internally linked data within their own territory and objective, yet
there is no linking data among them from the viewpoint of LOD applications.
In this preliminary work, we utilized linked open datasets, DBpedia, Linked
Geo Data, and WordNet for the purpose of making a recommendation system
for car drivers and passengers. We have found that it is required more goal-
oriented linked datasets and common sense knowledge as bridge between isolated
LOD datasets. We have also found that Semantic Web technology or specifically
LOD and SPARQL engines are enough as enabling technology to create and
demonstrate new applications based on heterogeneous and diverse datasets.
In our use-case, the system accepts ambiguous requests from car drivers and
passengers, plans driver actions to achieve goals that satisfies the requests, in-
cluding alternatives, and makes a recommendation for the drivers and passen-
gers.
To obtain the destination as goal, we utilized Linked Geo Data and DBpedia,
and arranged them with newly constructed facility ontology and service ontol-
ogy for linking among such open datasets. WordNet is also utilized as general
knowledge, because it was necessary to make the inference with common sense
to discover driving destinations from user requests. Then, we developed the in-
ference procedure to translate user requests into SPARQL queries to obtain a
recommendation on appropriate facilities and areas for users.
The purpose of this preliminary report is to make a clear direction for devel-
opment of LOD applications in order to deploy linked data as the infrastructure
of society in future.
2 Problem Setting for the Use Case
In setting of the use-case, we firstly made more than ten scenarios of conversation
between users and this system. In each case, a user in a car speaks a single or
a number of requests to do something with driving a car. Then, the system
analyzes the requests under the consideration of current contexts such as time,
location, driving time, etc. At last, the system makes concrete action proposals
to visit specific points (shop, facility, etc.) or areas (sightseeing area, good place
for time-consuming, etc.) with a reasonable visiting order. Basically, the request
may be vague and complex, but the recommendation is specific and concrete.
However, every recommendation is a sequence of actions, and proposed actions
are quite limited within these scenarios, for example, drive somewhere, buy or
eat something, do some sport, and so on. One of the simplest scenarios is as
follows.
Child passenger(hereafter C): I want to see a lion.
System(hereafter S): How about Ueno Zoo. A baby lion was born re-
cently.
C: It sounds good, but I was there last month.
S: Well, how about Kinoshita Circus. You can see a lion show there.
C: OK. That’s fine.
In this scenario, the system must discover the knowledge that a lion is a
kind of animal and a zoo is an public entertainment facility for seeing animals.
The system must find out a nearest zoo, that is Ueno Zoo in this case, from
the current location, and must reason that users have enough time to drive to
the destination and walking around the zoo. Furthermore, due to the negative
response of the user, the system must discover a neighboring circus that presents
a lion show as an alternative.
3 Ontologies for Facility, Action Target, and Service
Instead of directly searching individual facilities like Ueno Zoo or individual
shops like Yodobashi Akiba store (a home electric appliance mass retailer in
Japan), we considered classes of facilities like zoo or home electric appliance
mass retailer to make the system scalable, then made a facility ontology that
contains typical facilities and we defined typical users’ behavior at such facilities
like “a user sees animals in a zoo” or “a user buys a household appliance at a
home electric appliance mass retailer”. Even if we accidentally fail to guide an
actual facility that satisfies user’s special requests, such a problem will be solved
with the development of more rich and specific datasets that includes individual
facilities.
The facility ontology is constructed mainly by extracting facility classes re-
lated to leisure and meals in Lined Geo Data (LGD). LGD constructs a shallow
class hierarchy from tags attached to the nodes and ways of OpenStreetMap
(OSM). Therefore, LGD classes makes it easy to incorporate new facilities and
new facility types.
On the other hand, as a result of adopting LGD / OSM, duplicates of classes
due to notation fluctuation of tags and the low coverage rate of actual facilities
at the instance level could be a big problem. However, we think this approach
is the best for our purpose in our best knowledge, because the LGD / OSM is
the largest facility data that can be freely used at the present. Also note that
actually it is impossible to measure how much the existing facilities are covered
in reality. Regarding duplicates of classes in LGD, we select an entity as primary
class that has both the most information-rich descriptions on the OSM and a
large number of instances, then the rest are associated with owl:equivalentClass
to the primary class.
The following shows an example of zoo class in the facility ontology. The
meanings of Japanese words are added here in English as turtle comments for
readers. Both a service of “see animal” and “pay admission fee for cultural facil-
ity” are actually described in the service ontology as subclasses of “see” service
and “admission-viewing-gaming” service. Note that each service is described as a
pair of an action and an action target, which users can perform. In this paper, we
manually acquired and created service knowledge of facilities within the scenarios
as necessary. See the statistic numbers in Table 1. As shown below, the lgdo:Zoo
class is linked to the dbo:Zoo class in DBpedia Ontology to make possible to
search related facility instances in DBpedia Japanese. The dbo:Zoo already has
a link to Wikidata’s wikidata:Q43501. Thus, it can be easily expanded when
Wikidata is added.
lgdo:Zoo a owl:Class;
servicevoc:dbpediaClass dbo:Zoo ;
servicevoc:provideService [ servicevoc:hasService [
servicevoc:action action:払う; # pay
servicevoc:target target:文化施設入場料 ], [ # admission fee
# for cultural facility
servicevoc:action action:見る; # see
servicevoc:target target:動物 ]] ; # animal
rdfs:subClassOf servicevoc:Facility .
For the sake of systematical description of actions and action targets, we
used the Household Income Balance Item Classification List (January, 2015) of
the Statistics Bureau of the Ministry of Internal Affairs and Communications,
of which items of statistics data are used to describe purchasing behavior at
facilities. User’s behavior at facilities can be divided into purchasing behavior
(such as buying something or paying for some benefits as service) and the other
actions (see, eat, drink, etc.). This classification is based on a hierarchical struc-
ture of action targets as users’ behavior as consumer, so it is possible to consider
cooperation with statistical data in future, starting with purchase actions. For
actions and action targets other than purchasing behavior, we used Japanese
WordNet, because we want to use WordNet’s knowledge on the relationship be-
tween each verb as action and each noun as an action target. For instance, we
made Action Target Ontology as follows.
target:動物 rdfs:label "動物"; # animal
servicevoc:wordnet wnja11instances:word-動物 .
target:食料 a owl:Class; rdfs:label "食料"; # food
servicevoc:wordnet wnja11instances:word-食料 ;
rdfs:subClassOf target:購買対象 . # purchase object
The service ontology at the bottom of the table is the ontology we constructed
this time, as explained in the above.
In the facility ontology, a number of services corresponding to distinct facil-
ities come up with common abstract services. For example, both museums and
art museums have the same service of “paying entrance fee for cultural facilities”.
In addition, there are hierarchical relationships among users’ action targets, then
we have a similar relationship between services. For example, “seeing animals”
can be regarded as the top of “looking at a lion”. We constructed an ontology of
services apart from facility classes, so that services are independently recogniz-
able, and it enabled us to expand the performance of inference by applying the
hierarchy of services. In this paper, the part of service ontology is constructed
by using the Classification in the Household Survey of the Ministry of Internal
Affairs and Communications. The top of service ontology is the ‘facility service’
and it is related to aspects of two types of behaviors, namely, ‘purchase service’
focused on purchasing behavior, and an ‘activity service’ focused on the other
behaviors at facilities. The following shows an example of ‘purchase service’ on-
tology entries.
service:食料_サービス a owl:Class; # food service
rdfs:label "食料_サービス";
servicevoc:action action:買う; # buy
servicevoc:target target:食料; # food
Table 1. Outline of Prepared Datasets and Used Datasets
Dataset Version Num. triples Num, classes used
Fact Dataset
DBpedia core+en 2016-04-01 1,131,657,931 - △
DBpedia Japanese 2017-02-20 113,299,748 - ○
LinkedGeoData 2015-11-02 1,216,560,762 - ○
General Ontology
DBpedia Ontology 2016-11-01 30,793 758 ○
LGD Ontology 2014-09-09 24,530 1,200 ○
Japanese WordNet 2013-06-26 4,003,288 57,238 ○
Japanese Wikipedia Ontology 2013-11-07 21,863,327 166,397 ×
YAGO 3.0.2 1,001,461,792 5,130,031 ×
OpenCyc 2012-05-10 5,783,451 233,644 ×
UMBEL 1.5 392,728 33,686 ×
Service Ontology
Facility Ontology 2017-02-20 3,257 418 ○
Service Ontology 2017-02-20 3,933 750 ○
Action Target Ontology 2017-02-20 2,030 622 ○
Action Ontology 2017-02-20 153 55 ○
subtotal of Service Ontologies 9,373 1,845
Total 3,495,087,723 5,624,799
rdfs:subClassOf service:購買_サービス . # purchase service
service:肉類_サービス a owl:Class; # meat service
rdfs:label "肉類_サービス";
servicevoc:action action:買う; # buy
servicevoc:target target:肉類; # meat
rdfs:subClassOf service:食料_サービス . # food service
4 Building Knowledge Graphs
We have collected a number of open knowledge resources as shown at the upper
part of Table 1, and all of them are stored in one RDF store. However, at the time
of this writing, we have actually used only DBpedia Japanese, LinkedGeoData,
Japanese WordNet, and DBpedia Ontology as open datasets. Wikidata is not
stored because of the capacity.
The system used one endpoint built with one dedicated RDF store.
5 Reasoning and Q&A Process
In this preliminary research, we process natural sentences only within the range
expected at use-cases. Furthermore, in this paper it is assumed that the input
is transcribed as text instead of speech.
5.1 Process Flow and Reasoning
Work flow of this system is as follows.
1. Input a text of user’s requests.
2. Perform the morphological analysis for the input text.
3. Perform the case analysis starting with surface cases to deep cases.
4. Translate the requests into SPARQL queries.
5. Obtain the reply of SPARQL queries.
6. Generate the answering text from the obtained reply.
Japanese is a kind of agglutinative languages and a Japanese sentence is
written without a space left among phrases and words. A noun phrase is com-
posed of a noun and a particle, a verb phrase is composed of a stem of verb and a
grammatical conjugation. So, morphological analysis is requisite in Japanese text
processing in order to separate a sentence into phrases and words. Furthermore,
particles attached to nouns decide the grammar case. For example, in response
to an user’s input “ライオンが見たいな (I want to see a lion)”, the morphological
analysis and shift-reduce method changes the Japanese sentence into the form of
((な (pos info) 8) ((たい (pos info) 6) (見 (pos info) 5)) ((が (pos info) 4) (ラ
イオン (pos info) 0))), here (pos info) stands for a Part-of-Speech information
of each, then case analysis produces the result such as Subject:NIL, Verb:(見
る (pos info) 5), Object:(ライオン (pos info) 0), toPlace:NIL, fromPlace:NIL,
Tool:NIL. Part-of-speech information obtained from morphological analysis is
effectively used in various ways. For example, if there is an auxiliary verb ‘たい
(want)’ next to a form of a behavioral verb such as ‘見る (see)’ or ‘食べる (eat)’,
the whole sentence is interpreted as request. Thus, a request of seeing a lion is
captured and transformed into a SPARQL query to the endpoints.
From the interpretation of request (see lion), the system searches facilities
that can see a lion, using action target ontology and facility ontology. However,
we have no common sense as LOD that a lion is in a zoo. When searching fails
here, WordNet is used to generalize the target to more abstract ones by searching
hypernym relations in WordNet until animal is found.
The SPARQL search picks up a number of facilities that are located near the
current location, and the closest one to the current location is chosen outside of
SPARQL search.
5.2 Inference with SPARQL
Initially, we attempted to make a plan by introducing IS-A logic function into
planning based on classical state space reasoning and backward reasoning [1].
However, more than it, searching combined ontologies using one SPARQL query
easily enabled us to retrieve acceptable instances of appropriate facility from
the action target ontology and the facility ontology without any problems in
execution speed. The LGD class according to the user’s request from the facility
ontology can be found, and once the LGD class is known, SPARQL allows direct
retrieval of the facility instance within the LGD. If there is a DBpedia class linked
from LGD, DBpedia Japanese is also automatically searched in SPARQL queries.
The current system consists of RDF Store search and inference for interpretation
of user’s requests. This configuration is beneficial at usability and re-usability.
Based on SPARQL search and open resources, it is possible to expand and
refine ontology without touching the inference engine of the planning system
in applications. It is meaningful for practical application of reasoning by large
amount of data.
6 Example of Execution
The following shows an example of execution by this prototype system, see the
added comments translated into English for readers.
SYSTEM(4): (eliza)
system> スポーツがしたいな。そのあと、温泉に行きたい。
;; I want to enjoy some sport, after that, I want to go to hot spring.
現在地はトヨタ東富士研究所です。
;; the current location is Toyota Higashfuji Institute.
スポーツをする場所を探します。; searching a location for sports
......
一番近くの場所を案内します。 ; guiding the nearest place
距離は 13.37621km です。 ; the distance is 13.37621km
場所：沼津市営球場 ; place: Numazu City Ball Park
緯度：35.1125 ; longitude
経度：138.863 ; latitude
URL："http://linkedgeodata.org/triplify/node2877270449"
現在地は (35.1125 . 138.863) です。; the current location is (35.1125 . 138.863)
温泉に入る場所を探します。 ; searching a location for hot spring
......
一番近くの場所を案内します。 ; guiding the nearest place
距離は 10.426165km です。 ; the distance is 10.426165km
場所：伊豆長岡温泉 ; place: Izu-Nagaoka Hot Spring
緯度：35.0353 ; longitude
経度：138.929 ; latitude
URL："http://ja.dbpedia.org/resource/伊豆長岡温泉"
Searching for a facility in the vicinity of the current location, the Toyota
Higashifuji Institute, the system made a recommendation to go to Numazu City
Ball Park, then go to Izu-Nagaoka Hot Spring, in response to a request to go to
a hot spring after enjoying some sport.
While this prototype of action planning by using open knowledge sources and
SPARQL queries is widely applicable to various kind of applications, yet there is
not enough as intelligent agent. Making more intelligent agent remains in future
work.
7 Discussion
In this preliminary research, the following issues are suggested.
1. It is necessary to understand data characteristics of coverage and granularity
of each dataset, but it is generally hard for large datasets. At this time, we
firstly made a utilization plan on the whole data set, after we examined the
availability of actual data on the premise of these use-case scenarios.
2. Generally, it is tough work to find out correct relations between datasets.
While simple string matching allows us an automatic matching process, the
ontology mapping cannot be avoid human power at the present. While the
accuracy of this mapping greatly affects the result, mechanical matching pro-
cessing is difficult. In addition, we built intermediate ontologies and mapped
them to LOD datasets, but building ontology is generally not easy for a
novice.
3. Since DBpedia and LGD are datasets made by crowd sourcing, we cannot
expect the completeness and validity of them. Missing or biased data is still
problematic at reasoning. Actually, we found a closed food shop as results.
At this time we attempted to eliminate errors as soon as it was found, but
we need to think about some tools for (semi) automated error checking.
4. The inference procedure was designed according to these use-case scenarios.
For other problems, different datasets and different work flows may be used.
For example, it depends on features of a target problem about how the
balance should be taken between general knowledge and fact data to solve
the problem.
8 Conclusion
In this preliminary research, we made a prototype of action planning system for
events of everyday life and world, based on open knowledge of LOD as fact data
and taxonomy as common knowledge. We utilized a number of large-scale open
databases and knowledge-bases. We found that we had already abundant knowl-
edge about the everyday life and world as diverse open knowledge resources. This
condition is very different at the era of Good-Old-Fashioned-AI (GOGAI) before
the Web age and LOD. However, we also found that we needed the additional
general and common knowledge that connects such different open resources in
reasoning action plans with SPARQL endpoints. It is obvious that it will be
necessary to make open knowledge more available not only in the verification
and validation for each, but also in the combinations of them for applications.
References
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Elsevier (2004) .