This document describes an evolvable semantic platform called EXPERTIZE that was developed to facilitate knowledge exchange between experts at a university. EXPERTIZE analyzes unstructured text from news and matches it to the skills of university experts, as defined in their personal ontologies, in order to recommend relevant experts. It uses a latent Dirichlet allocation algorithm to perform the semantic matching. The system was implemented and evaluated, showing its ability to successfully recommend experts and categories for news items.

1. A I S T 0 8 . 0 4 . 1 6
Y E K A T E R I N B U R G , R U S S I A
N I K O L A Y K A R P O V
E D U A R D B A B K I N
A L E X A N D E R D E M I D O V S K I Y
N A T I O N A L R E S E A R C H U N I V E R S I T Y
H I G H E R S C H O O L O F E C O N O M I C S
Evolvable Semantic Platform for
Facilitating Knowledge Exchange

2. Motivation
 A university undoubtedly should be a catalyst for
exchanging expertise and professional knowledge in
the economic cluster.
 A specifically designed combination of automated
text processing and ontology-based knowledge
engineering may improve quality of information
analysis and reduce university’s response time.
 We propose to facilitate knowledge exchange by
seeking relevant university experts for commenting
actual information events expressed in the texts of
news.

3. Personal ontology in InfoPort system
W3C FOAF
(Friend of Friend)
vocabulary
specification
 Researcher as a
person.
 Researcher as a
skillful agent.
 Researcher as a
team member.
3

4. InfoPort User Interface
a) front page; b) enlarged view of personal time line

5. Platform implementation
 In one hand we have personal ontology which includes
skills of university experts
 In other hand we have unstructured text of news which
are expressed
 We analyze semantic in the news and match it with skills
of experts
 For semantic matching we choose an algorithm
(Momtazi and Naumann, 2013) based on a Latent
Dirichlet allocation.
 It is algorithmically implemented in the newly designed
decision support system titled EXPERTIZE.

6. Matcher using Latent Dirichlet allocation



Zz
zCPdzPCdP )/()/()/( 00
We count a probability for each expert and category c and rank categories
according to value.
Approach by (Momtazi and Naumann, 2013)

7. Interaction of EXPETIZE services with
InfoPort platform
InfoPort
EXPETIZE system
InfoPort platform
Store
Service
Regular
offline
services
Online
services
Native
REST-
Interface
The EXPERTIZE system regularly monitors user profile sources in the
Internet, performs document analysis and provide university employees
with critical information about relevant events according the specific
relevance matching algorithm.

8. Principle design of the EXPERTIZE system
REST-
Interface
Crawler
Service
Data Modeler
Data Store
Matcher
Temporal raw data LDA model
InfoPort
Store Service
RSS
Newsfeed
Online
processing
REST-
Interface
Offline
processing
Web GUI

9. Graphical user interface of the EXPERTIZE system

10. Algorithm quality evaluation
We evaluate algorithm proposed by Momtazi
and Naumann with our datacollection an queries
Score Experts Categories
Precision (10) 0.86 0.72
Precision (5) 0.62 0.44
Precision (1) 0.17 0.37
MAP (10) 0.57 0.49
MAP English TREC 2006 0.471 -
MAP English TREC 2005 0.248 -

11. Conclusion
 Our EXPERTIZE platform applies topic modeling to online expert
recommendation using the university community as the expert
pool.
 We realize and evaluate an algorithm for matching news with a
semantic of two indicators: experts and categories.
 As a source of categories and keywords two taxonomies are
used together as a machine-readable ontology of scientific
areas.
 The first use cases of the EXPERTIZE system show their ability
to solve the task specified.

12. N I K O L A Y K A R P O V
N K A R P O V @ H S E . R U
Thank you for your attention!