This document discusses a study that analyzed 14 popular Internet of Things (IoT) ontologies to identify commonly used concepts. It extracted terms from the ontologies and calculated term frequencies. It then used word embedding and k-means clustering to group related terms and identify popular concept clusters. The clusters were manually analyzed to assign concept names. The study found only low reuse of concepts across ontologies and aims to help standardize IoT vocabulary.

1. Technische Universität Chemnitz  Prof. Dr.-Ing. Martin Gaedke & Team 23.10.2018
Concept Extraction from theWeb
ofThings Knowledge Bases
Mahda Noura*,Amelie Gyrard**,
Sebastian Heil*, Martin Gaedke*
*Technische Universität Chemnitz,Chemnitz,Germany
** Knoesis,Wright State University, Ohio, USA

2. Introduction

3. One of the six technologies that will most influence the world by 2025
IoT market will grow to $7.1 trillion
>300 IoT platforms in the market
IDC
UNIFY
IoT
NIC
Internet ofThings
3

4. Challenge: missing interoperability
#40% of the potential benefits of
IoT cannot be obtained without
interoperability
McKinsey
4

5. “Enablingdifferentagents,services, andapplicationsto
exchange information,dataandknowledge in a
meaningfulway, on andoffthe Web“ (W3C)
5
Semantic Interoperability

6. Automatically
built
my machines
Automatically built
by machines
Google’s Knowledge
Graph
You are using the SemanticWeb Every Day!

7. Ongoing
Extension to
schema.org
iot.schema.org

8. How to reuse the domain knowledge already encoded in
IoT ontologies?
Research Challenge

9. 10
cc
RelatedWork

10. Ontology Analysis
semantic interoperabilitypractices
(EuropeanResearchCluster,2015)
health,transportation and logistic
(Ganzhaetal.2017)
ontology validationtools
(Gyrard etal.2018)
11

11. Approach Input Search terms
Tool
Availability
IoT ontology
Support
Output
OntoKhoj,
Sindice
keyword class, subclass, domain/range ✗ ✗
ranked
ontologies
Swoogle,
Watson
keyword
class, property, label,
comment, literal
✓ ✗
ranked
ontologies
AKTiveRank keyword terms
✗
✗
ranked
ontologies
Falcons keyword class, property, labels ✗ ✗
ranked
ontologies
OntoSelect,
OntoSearch2
keyword
class, label, property,
ontology-title
✗ ✗
ranked
ontologies
Popular Concepts in IoT ontologies?
not considered

12. 13
Approach

13. Identifying Most Popular Concepts

14. STEP 1: Ontology Selection
• Selection of 14 IoT ontologies
̶ Standardized
̶ The most cited when investigating the literature survey
̶ Availability of the ontology, code online
̶ Ability to work with a set of validation tools (e.g., PerfectO)

15. STEP 2: Preprocessing
Convert all
ontologies to
ttl format
ASCII
Conversion
Case
Conversion
Store
ontologies in
Virtuoso

16. STEP 3: Vocabulary Extraction

17. STEP 4: Term Extraction
Split vocabulary
on camel-case
or snake-case
Remove
punctuation
marks
Remove
numbers
Stop Word
Removal

18. STEP 5: Frequency Calculation
Given a set of terms 𝑡 ∈ 𝑇, the frequency of the term in
the set of ontologies 𝑂 is calculated 𝑓𝑂(𝑡) as it
represents the total number of occurrences of each
unique term in all ontologies

19. STEP 6: Word2Vec

20. STEP 7: k-Means Clustering

21. STEP 8: Results Aggregation
Using the term frequencies and term clusters as input this
step calculates the total number of occurrences of the
terms in different ontologies as well as the ontologies
using this term per cluster

22. STEP 9: Concept Assignment
Given the term clusters the ontology experts
manually assigned names to each cluster based on
the semantic meaning of the cluster.

23. 24
Results

24. Identification of Popular Concepts

25. Extent of re-use among ontologies

26. 27
Conclusion

27. Concept Extraction from IoT Ontologies
Existing knowledge is constantly redesigned in different
communities
Extent of reuse among ontologies is low
Future Work
Create training dataset for IoT
Apply the presented methodology to other IoT application
domains

28. VSR
Technische Universität Chemnitz  Prof. Dr.-Ing. Martin Gaedke & Team 23.10.2018
ThankYou!
mahda.noura@informatik.tu-chemnitz.de
VSR.Informatik.TU-Chemnitz.de
@mahdanoura /mahda noura /mahdanoura