Session 2.1 ontological representation of the telecom domain for advanced ai applications
1. Ontological representation of the telecom domain for advanced
AI applications
Felix Burkhardt, Joachim Stegmann: Deutsche Telekom AG
Till Plumbaum, Christian Sauer: DAI (Distributed Artificial Intelligence) Labor
Tilman Becker, Michael Feld: DFKI (German Research Center for AI)
2. 2
Creating an ontology in the telecom domain
Overview
• Research and development project together with scientific partners
• DAI-Labor, Distributed Artificial Intelligence Laboratory, Berlin
• DFKI (German research center for AI), Saarbrücken
3. 3
Creating an ontology in the telecom domain
Contents
• Motivation
• Ontology Creation:
• Datasources
• Manual creation of upper ontology
• Retrieving suggestions for new concepts, synonyms, relations
• Comparing traditional machine learning with DNNs
• Ontology storage and maintenance
• Natural lanuguage interface
• Translating Natural queries to SPARQL, Answer generation
• Application examples
• Audio based semantic search, Chatbot integration
• Summary and Outlook
4. 4
Creating an ontology in the telecom domain
Motivation
• Statistical data mining is successful
but has its limits
• Rule based systems can be used
when training data is sparse
• Data-based / rulse based can be
combined when rules are learned
from data
• Modeling semantic knowledge
explicitly can help knowledge retrieval
applications
• One example would be
disambiguation for question
answering in „chatbots“, i.e.
automatic dialog systems
5. 5
Creating an ontology in the telecom domain
Motivation
Why are we developing an own ontology?
• An ontology is deeply connected to the company‘s knowledge / internal
data and can exist in a vendor independent format
• Separating the ontology from the supplier lessens the dependence to
one supplier
• One common ontology about a company‘s domain can be updated by
common data-sources and reused by different applications
6. 6
Creating an ontology in the telecom domain
Datasources
• Several
datasources get
harvested:
• Forum posts
• Official
website
• XML files
(product
specs)
• Chatlogs
7. 7
Creating an ontology in the telecom domain
Manual creation of upper Ontology
• A general ontology for a domain big as the telecom domain is a challenge
• Needs to cover different areas, such as sales, infrastructure,
customer support and many more
• Two design decisions were made:
1. Concentrate on one area after another – not all at once
• Starting with customer support use case
2. Creating an ontology/taxonomy for each area - relations
between different areas are added when needed
• Each area is created by area experts and ontology experts
Early version of the ontology showing the broad concepts included
8. 8
Creating an ontology in the telecom domain
Retrieving suggestions for new concepts
• After the manual creation of the upper structure, we used
crawler techniques to gather more concepts automatically
• Different sources were used
• Telekom Hilft Forum
• Telekom Product Website
• XML Data
• For each source we created a specialized crawler
• New sub-concepts, e.g. from Telekom Product Website
new VR hardware as part of the general Home concept
• New attributes from product data, e.g. XML such as 5G as
new transmission speed
9. Creating an ontology in the telecom domain
Retrieving suggestions for new concepts
9
• New Concepts retrieved for the Home (Zuhause) concept
include for example sub-concepts like EntertainTV or Geräte
(Devices)
• These sub-concepts are then populated with devices and
device information (also automated by crawling the relevant
sources)
Concept Zuhause with automatically added sub-concepts
Sub-concept WLANundRouter
with automatically entities
10. 10
Creating an ontology in the telecom domain
Retrieving suggestions for new Synonyms
• Customers tend to use different words for the same thing – Synonyms
• Our ontology should cover all those different words
• Important in e.g. a search use case
• To retrieve the synonyms (and also misspellings)
we used shallow neuronal nets (word2vec)
and fasttext to learn from a big corpus of
user conversations
• Corpus is Telekom Hilft Forum
with over 2 Million text snippets
W2V and fasttext deliver
different views on a concept
11. 11
Creating an ontology in the telecom domain
Retrieving suggestions for new Synonyms
ML Approach allows us to
get Information for yet
unknown
devices.
Word2Vec based on Deep
Learning for J, skip gram
version.
Word2Vec operates word-
based, fasttext character-
based, so it finds similar
terms even for unknown
words
12. 12
Creating an ontology in the telecom domain
Comparing traditional machine learning with DNNs
• We investigated topic
classification of the forum-posts
• Compared „classical machine
learning“ with Deep neural
nets.
• Both resulted in 55% accuracy
rsp. 83% „one in three“
• Also investigated subclustering
with DNN (4 subclusters per
category)
Apache Lucene preprocessing
NER / disambuiguation
Multinomial Naive Bayes
Deep Temporal
Convolutional Neural
Network *
Comparison
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Creating an ontology in the telecom domain
Adding arbitrary relations
• Not done automatically
yet.
• Of course term
candidates from natrual
language harvesting
might become alt-labels
for relations
• For now, relations are
added manually,
derived from appliaction
use-cases
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Creating an ontology in the telecom domain
Ontology storage and maintenance
• Started with
Protege and
switched to
Poolparty now
• Scalabilty
• Interfaces
• NLP integration
• Maintenance
15. 15
Creating an ontology in the telecom domain
Translating Natural queries to SPARQL
• Design Time:
NL model Generator
builds example
sentences from question
templates and ontology
entities
• Runtime:
Nuance Mix model
processes input
sentence. The extracted
paramters are converted
into a SPARQL query
and executed on the
ontology. Results are
converted back to text. 13
NL Model
Generator
Ontology
Q&A Templates
(Classification)
NL Model
(Nuance .trsx file)
DialogRuntime
e.g. „Which
smartphones havea
changeable
battery?“
e.g. „The
iPhone7 and
the Samsung
Galaxy S7“
SPARQL Query
forresponse
EntitiesList
CloudUpload /
Download
User Input
Chatbot
Output
Static
Generation
NLU via
Mix API
Query
Generation
JSON
Text/
Speech
Facts
Answer
Generation
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Creating an ontology in the telecom domain
NL MODEL EXAMPLE
• The Nuance Mix
web interface
allows the
definition of intents
and parameters.
• For each intent,
several example
sentences should
be provided.
• Here, the intents
are the different
Q&A templates
(question types).
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Creating an ontology in the telecom domain
Answer generation
• Upper part:
The Q&A template
database defines how
SPARQL queries look
for different linguistic
question structures.
• Lower part:
An example question
is executed and the
presented
intermediate and final
results are generated. Running Query
on Ontology
(via Jena)
Reading question classification:
Intent ID
+ SPARQL Template
+ Parameter
Ask question
[via Text or Speech input]
List:
SCOWWS_SELECT ?s WHERE {?s rdf:type/rdfs:subClassOf* dtag:%s.?s dtag:%s?v. FILTER(?v = "%s").}_subject1_predicate1_object1
SCOWWI_SELECT ?s WHERE {?s rdf:type/rdfs:subClassOf* dtag:%s. ?s dtag:%s?v. FILTER(REGEX(str(?v), "%s")).}_subject1_predicate1_object1
…
JSON
"interpretations": [{
"action": {"intent":
"value": "SCOWWS"}},
"concepts": {
"object1":[{"literal": “changeable", "value": "changeable"}],
"predicate1": [{"literal": “battery", "value": “battery" }],
"subject1":[{"literal": "Smartphones", "value": "Smartphone"}]},
"literal":„Which smartphoneshaveachangeablebattery"}]
Evaluation using Mix.nlu Model
via WebSocket
(NLU Service)
via WebSocket
(NLU Service)
1) Extract intent + concepts
2) Based on IntentID, create final SPARQL query
via SPARQL Template
Answer
dtag:Alcatel2051silver
dtag:SamsungGalaxyJ52016black
dtag:SamsungGalaxyXcover3SMG389Fsilver
SCOWWS: Question by Subjectwhere Subject = Class; WITHOUTComputation; WITH Predicate, WITH Object(String)
SPARQL Query
Ontologie
SELECT?s WHERE {?s rdf:type/rdfs:subClassOf*dtag:Smartphone.?s dtag:battery ?v.FILTER(?v = “changeable").}
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Creating an ontology in the telecom domain
Application examples: Audio based semantic search
• Human agents get supported by intelligent content suggestions
• Project Highlights
• Intelligent Q&A is highly appreciated by DT agents
• Full export of product XML data provides good precondition for AI based answer generation
• Expansion of AI approach to support DT Social Media agents with recommended answers for
several customer requests (Facebook & Twitter)
• Learnings:
• System performance depends on data material quality (structured vs. unstructured)
• Social media data are highly unstructured – time is needed for manual preprocessing -
currently full automated clustering and preprocessing is still under development
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Creating an ontology in the telecom domain
Application examples: Audio based semantic search
21. 21
Creating an ontology in the telecom domain
Summary and Outlook
• We created an ontology based on RDF in the telecom domain to support our AI activities.
• From several in domain data sources such as Product descriptions, chat logs and help forum posts
• On the one hand the ontology will be queried directly by SPARQL queries that are derived from natural language searches.
• On the other hand, it is the semantic basis for a variety of other applications such as
• semantic search,
• agent content assistance,
• virtual digital assistant,
• social media mining and
• intelligent chat bot.
• The advantages of maintaining an own centralized ontology are manifold: by storing the knowledge in an open standard
format
• we strengthen the independence of proprietary technology,
• can keep parts of the data private and on-site,
• and re-use the data more easily.
