=Paper=
{{Paper
|id=Vol-2293/jist2018pd_paper11
|storemode=property
|title=Knowledge Graph on University Campus Issues
|pdfUrl=https://ceur-ws.org/Vol-2293/jist2018pd_paper11.pdf
|volume=Vol-2293
|authors=Yuto Tsukagoshi,Takahiro Kawamura,Akihiko Ohsuga
|dblpUrl=https://dblp.org/rec/conf/jist/TsukagoshiKO18
}}
==Knowledge Graph on University Campus Issues==
https://ceur-ws.org/Vol-2293/jist2018pd_paper11.pdf
Knowledge Graph on University Campus Issues
Yuto Tsukagoshi1 , Takahiro Kawamura1,2 , and Akihiko Ohsuga1
1
University of Electro-Communications, Tokyo, Japan
2
Japan Science and Technology Agency, Tokyo, Japan
Abstract. This paper aims to build a knowledge graph as Linked Open
Data (LOD) on a university campus and to support solving campus
issues by completing the missing data in this knowledge graph. We first
designed LOD schema for the campus issue. Then, we extracted the
data including daily parking status and the related data on campus and
built the knowledge graph. We then complemented it by TransE and its
derivatives, and finally propose the improvement of them for the campus
issue.
Keywords: Knowledge graph completion · Campus issues · TransE.
1 Introduction
Nowadays there are many urban issues, including littering, scribbling, and il-
legally parked bicycles. Toward the solution of these issues, it is requested not
only for the government but also for corporations and individuals to disclose and
share their related statistical and sensory data. The Ministry of Internal Affairs
and Communications in Japan promotes a plan to use such “open data”, and
the local governments are also promoting to solve social issues by using the data.
Then, Linked Open Data (LOD) is recommended to increase the usefulness of
the open data.
We thus constructed a knowledge graph as LOD regarding illegally parked
bicycles in Tokyo and made it publicly available in cooperation with the Tokyo
Metropolitan Bureau in 2017 [1]. However, as more immediate concern a univer-
sity campus is an epitome of a society and has many social issues. Concerning
the bicycle parking, for example, a specific area is overflowing on a specific date
and time; thus, there can be a risk in an emergency. In our university, there is
a student organization called Student Assistant, which observes daily parking
status and records the number of bicycles parked in each parking area. However,
there are areas that are still not observed because of the shortage of time and
student volunteers.
In this paper, we build a knowledge graph containing the number of bicycles
in each parking area with other related data on our campus. We then aim to
complement the knowledge graph to include the missing number of bicycles.
We first disclose the number of bicycles observed as the open data to make it
available for everyone to develop the related services on a campus. We then linked
other related data on the campus to promote the innovative services in Section
3. Secondly, we try to complement the knowledge graph with knowledge graph
completion techniques in related literature in Section 4. Finally, we conclude this
paper with the future work in Section 5.
2 Y. Tsukagoshi et al.
2 Related Work
Bordes et al.[2] proposed the method called TransE, which embeds a knowledge
graph into a vector space and estimates the similarity of entities and relations
based on an energy-based model with the set of triples where the head or tail
replaced by a possible entity(but not both at the same time). In this paper, we
complement the knowledge graph using the TransE and its derivatives, such as
TransH[3], TransR[4] and TransD[5]. We then propose the improvement of them
based on the domain knowledge on the campus issue.
3 Building of knowledge graph on campus issues
In the knowledge graph, we integrated the data about the number of bicycles, the
information obtained from a university website, such as buildings and class sched-
ules, and weather data obtained from the Japan Meteorological Agency after ex-
tracting values and descriptions on time and places. Prior to this integration, we
retrieved requirements on the possible usage of the data from several students
and defined the schema, in which individual issues have interactive relations.
However, we used existing ontology alone to make it interoperable with LOD
cloud as much as possible. We then stored all the data in an RDF database, whose
SPARQL endpoint can be found at http://www.ohsuga.lab.uec.ac.jp/sparql, and
the Graph IRI is http://www.ohsuga.lab.uec.ac.jp/campus 2017.
3.1 Dataset
We retrieved the following information for the 2017 fiscal year from the data
collected by the Student Assistant, the university website, Google Maps, and
the Japan Meteorological Agency.
– Time, e.g., fiscal year, semester, month, day, date, time zone, etc.
– Parking areas and the number of bicycles
– Course titles, and classrooms
– Name of rooms in every building
– Event titles and venues
– Temperature and precipitation
– Latitude and longitude of every place
3.2 Schema design
We then define the following RDF schema as shown in Fig. 1 to make unique
entities mutually connecting via specific relations. Specifically, we used 13 on-
tologies, 25 properties, and 10 classes, including aiiso3 , event4 , geo5 , gn6 , ical7 ,
3
http://purl.org/vocab/aiiso/schema#
4
http://purl.org/NET/c4dm/event.owl#
5
http://www.w3.org/2003/01/geo/wgs84 pos#
6
http://www.geonames.org/ontology#
7
http://www.w3.org/2002/12/cal/icaltzd#
Knowledge Graph on University Campus Issues 3
owl8 , rdf9 , rdfs10 , time11 , teach12 , wo13 , and the IPBLOD in our previous work
[1].
Fig. 1. The schema for campus issues.
We converted the collected dataset to RDF data with the defined schema
giving unique entities to URIs. Currently, we have 20,820 triples in the graph.
As an RDF database, we used Open Link Virtuoso 7.
4 Completion of knowledge graph
Secondly, we complemented the knowledge graph by embedding it into a vec-
tor space using several completion methods, since the knowledge graph includes
missing triples such as . We extracted all the
triples from the graph and estimated the missing triples related to the number
of bicycles by using TransE[2], TransH[3], TransR[4] and TransD[5]. In the ex-
periment, we trained on 20,448 triples except for 372 missing triples with 1,000
epochs. The hyperparameters were: learning rate = 0.001, hidden layers = 100,
minibatch = 100, and margin = 1.0. Table 1 shows the average results of the
experiments in five times, where a half of the dataset is randomly selected for
the training, and the other half of the dataset is used for the testing. In this
8
http://www.w3.org/2002/07/owl#
9
http://www.w3.org/1999/02/22-rdf-syntax-ns#
10
http://www.w3.org/2000/01/rdf-schema#
11
http://www.w3.org/2006/time#
12
http://linkedscience.org/teach/ns#
13
http://www.auto.tuwien.ac.at/downloads/thinkhome/ontology/WeatherOntology.owl
4 Y. Tsukagoshi et al.
experiment, a lower MeanRank is better while a higher Hits@n is better. Com-
paring with the random estimation, we can find that TransE and TransH were
superior at least in Hits@3 and Hits@1.
Table 1. Results of each method.
MeanRank Hits@10 Hits@3 Hits@1
TransE 89.747 0.351 0.284 0.208
TransH 70.479 0.390 0.274 0.219
TransR 289.414 0.205 0.086 0.023
TransD 148.131 0.288 0.208 0.051
Random 1298 0.385 0.116 0.039
However, we are now trying to get more significant results by adjusting the
model to this problem domain, since this knowledge graph is not a highly-
simplified test data, such as FB15k, but is specialized for a real campus issue.
For example, we can add as the domain knowledge the parameter corresponding
the numbers of classes and events around bicycle parking areas, because the bi-
cycles parked in the morning will not be moved when the next class is in a short
distance.
5 Conclusion and Future Work
This paper described a knowledge graph about the numbers of bicycles and other
related issues on university campus and complemented by estimating missing
triples. As of now, we used existing methods to estimate only the number of
bicycles; however, the original model will be required, since the knowledge graph
is specialized for campus issues. Besides, we will get more data other than the
2017 fiscal year to improve accuracy of the estimation. Furthermore, through
the visualization of parking status and other campus issues obtained from the
complemented knowledge graph, we will improve student awareness on their
campus through recommendation and visualization of parking status.
Acknowledgements. This work was supported by JSPS KAKENHI Grant
Numbers JP16K00419, JP16K12411, JP17H04705, JP18H03229, JP18H03340,
JP18K19835.
References
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for Modeling Multi-relational Data，Proc. of NIPS, pp.2787-2795(2013).
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Proc. of AAAI, pp.1112-1119(2014).
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Knowledge Graph Completion, Proc. of AAAI, pp.2181-2187(2015).
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