Criminal Investigation with Augmented Ontology
and Link Prediction
Takanori Ugai
Fujitsu Limited
Kawasaki, Japanugai@fujitsu.com

Abstract—This paper shows that our basic approach to theKnowledge Graph Reasoning Challenge is to create an ontology
of motives and means. We also use link prediction based on
knowledge graph embedding techniques to solve the case. The
ontologies on motives and means are based on hand-craftedin our previous work and are supplemented by an automatedontology created by linking Wikidata. As a result of preliminary
experiments, we present an overview of the ontology created at
this stage and show how to predict characters with motives forincidents from the knowledge graph of the novel The Adventureof the Speckled Band.
Index Terms—Knowledge Graph, Ontology, Wikidata, LinkPrediction

I. I NTRODUCTION
The Knowledge Graph Reasoning Challenge [1] has been
held in Japan since 2018. The Challenge aims to promote thetechnology of explainable AI using knowledge graphs. Thechallenge is to use a dataset of knowledge graphs representing
eight Sherlock Holmes mystery novels to estimate the culpritwith a reasonable explanation. 1st International Knowledge
Graph Reasoning Challenge(IKGRC)1 is an international version of the challenge, in which participants are tasked withsolving the same task using a revised dataset.
The study aimed to find the murderer by inferring who had
the motive, opportunity and method. The ontology extended
using Wikidata was combined with the original knowledge
graph to infer the murderer, motive, and weapon by link
prediction using knowledge graph embedding. The following
sections of this paper will be devoted to Section II. In Section
II of this paper, we describe the structure of the knowledge
graph provided by the knowledge graph inference challenge,
Section III presents the basic policy we have adopted tosolve the case. In addition, Section IV describes the Section
IV outlines the ontology we have created as a preliminaryexperiment, based on links to wikidata2 , and shows the resultsof our predictions for the knowledge graph of the novel “TheAdventure of the Speckled Band”(Speckled Band).
II. S TRUCTURE OF THE KNOWLEDGE GRAPH PROVIDED
The schema of the knowledge graph provided by the organisers is based on the division of the set of content into
scenes and the graphical representation of the content of each
1 https://ikgrc.org/2023/application.htm
2 https://wikidata.org/

scene and the relationships between the scenes. Each scene
is described by the following basic properties. The properties
differ from the common <subject, predicate, object> format,
where the scene ID is used as the subject and the information
related to that scene is summarised [2].
subject: The person or thing that is the subject of thedescription of the scene• hasPredicate: A predicate describing the content of thescene• hasProperty: The nature of the person or thing that is thesubject• Objects with scene details: whom, where, when, what,
how etc.
• Relationships between scenes: then, if, because, etc.
• time: The absolute time the scene took place(xsd:DateTime)• source: The original text of the scene (Literals in Englishand Japanese)•In the provided knowledge graphs, the types Person, Place,Situation and Object are given, but Object is not given a more
specific type.
III. O UR A PPROACH
To deduce the culprit, various kinds of evidence (physicalevidence and circumstances), such as the circumstances ofthe damage, the appearance of the victim and the suspect,
and their relationships, are accumulated and compared withvarious knowledge related to the crime. Multiple analyses are
conducted to narrow down the suspects and deduce the culprit.
The clues for the identification of the perpetrator include
1) the analysis of motive2) the analysis of opportunity
3) the analysis of method4) the analysis of behaviour and knowledge
In this study, we try to find the murderer by inferring theperson with the motive and method. We create ontologies ofmotives and methods of crime from dictionaries, add themto the knowledge graphs and apply link prediction basedon knowledge graph embedding to determine motives andmethods. We manually made the final overall judgment basedon motive, opportunity, and method.

First, the triples obtained using the following SPARQL
query are added to the original knowledge graph.
CONSTRUCT {?who :hasMotivation ?motivation .
}WHERE {
?scene :subject ?who .
?scene :hasPredicate ?motivation .
?motivation :instanceOf :Motivation .
}Fig. 1. Example of an object connected to Wikidatarepresenting

IV. P RELIMINARY E XPERIMENT
A. Ontology Augmentation
In novels, Sherlock Holmes uses his knowledge and deductive powers, in addition to what he has seen and heard, to solve
the case. The novel showcases these in the scene where thecase is solved. Therefore, the knowledge graphs that represent
the novel do not describe these knowledge and reasoning
rules. Therefore, to explore the truth of the case formallyand mechanically, it is necessary to add the knowledge thatSherlock Holmes would have about the crime’s motives, meansand alibis. In our previous work [3], we have created twoontologies that we use to investigate crimes. The first one isa structured ontology of criminal motives. Possible criminal
motives were listed in the White Paper on Crime to create
this ontology.
The means of killing were listed from the backward matchof “kill” in the Japanese dictionary, and the details weredescribed as structured data with attributes and values.
The previously created version was insufficient to cover theeight cases, so it was decided to extend it using Wikidata.
The extension of the knowledge graph with Wikidata wascarried out in the following steps.
1) Extracting instances of object classes from the Novel
Knowledge Graph.
2) Searching Wikidata for object strings.
3) Adding words close to each word as synonyms using
Wikipedia word embedding data3 .
4) Assign relations and what he has seen and heard Wikidata’s class hierarchy.
1069 classes were added, and 780 objects in the Novel
Knowledge Graph were typed.
Figure1 is part of the ontology created4 . From the knowledge graph of Speckled Band, extended by Wikidata, it canbe deduced that Roma has negative feelings towards Roylott.
B. Case resolution using link prediction with knowledge graphembedding
In this section, we show the results of predicting who has
motives for the knowledge graph of Speckled Band.
3 https://fasttext.cc/docs/en/pretrained-vectors.html
4 https://github.com/takanori-ugai/KGRCOntology

Next, knowledge graph embedding is applied to the augmented
knowledge graph. The simplest TransE [4] was used for theknowledge graph embedding. Finally, the motivation of eachcharacter is predicted and scored. For example, for Roylott, wepredicted <Roylot hasMotivation ?motivation>, resulting in ascore of 0.9 for property purpose and 0.8 for envy. Comparing
each character’s motivations, Roylott’s property motive wasplausible, followed by Helen’s love motive.
V. S UMMARY AND F UTURE W ORK
In this paper, we showed that our basic approach to theKnowledge Graph Reasoning Challenge is to create ontologiesof motives and means. We also used link prediction basedon knowledge graph embedding techniques to solve the case.
The ontology on motives and methods is added to the previously hand-created ontology, automatically created by linking
Wikidata. As a result of preliminary experiments, an overview
of the ontology created at this stage is presented, showing
how the knowledge graph of the novel “The Adventure of theSpeckled Band” can be used to predict which characters havemotives for the case and predict that Roylott is most likely to
be a property seeker. This was shown to be the most likely
prediction for Roylott. The same method will be applied to allnovel knowledge graphs in the future, using the same way topredict how each character causes the incident.
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G. Xiao, and E. Botoeva, Eds. Cham: Springer International Publishing,
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[3] T. Ugai, Y. Koyanagi, and F. Nishino, “A logical approach to criminalcase investigation,” The 1st International Workshop on Knowledge Graph
Reasoning for Explainable Artificial Intelligence, 12 2021.
[4] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko,“Translating embeddings for modeling multi-relational data,” in Advancesin Neural Information Processing Systems, C. Burges, L. Bottou,
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cc/paper/2013/file/1cecc7a77928ca8133fa24680a88d2f9-Paper.pdf