Knowledge and Data Integration (ICSI500)
Welcome to the homepage of the fall 2022 edition of Knowledge and Data Integration, course of the Information Technology degree at the National University of Mongolia.

 

 

News


Course registration is now open in the SISi system (Knowledge and Data Integration (ICSI500) - Amarsanaa G.).

August 18th, 2021

This class will start on Tuesday September 6th. More details in the Calendar and Material section.

August 18th, 2022

 

 

Last modification: August 18th, 2022

Instructions


This KDI course is the same course taught at the University of Trento in the academic year of 2021/2022. This course is taught in the presence in class of both lecturers and students, primarily as a hands-on lab course. Passing the exam amounts to developing a project, which ultimately will lead to generating a Knowledge Graph (and support documentation) starting from data that will have to be found on the Web. This goal will be reached under the continuous supervision of the lectures providing advice and support, and in collaboration, doing joint work with a colleague taking this course. There are no easy or cost-effective ways to achieve this goal without a continuous presence in class. Given the current Covid situation, if someone taking the course cannot be in class for a particular lecture, that lecture will be registered and made available to him/ her. The registration request should be done as soon as possible before the beginning of the lecture (ideally 2-3 days before) and supported by a valid justification. Notice, however, that these registered lectures will unlikely have the same quality as the physical lecture, particularly for classes that will consist of one-to-one interactions between the students and the lecturers. In most cases, additional interaction with the lecturers during the Q&A lectures will achieve a better goal (see below).

The lectures will follow the scheduling indicated in the Calendar and Material section. The course material includes slides, demo videos, support resources, and links, and all are provided on the website under the Calendar and Material section. For those interested, it is possible to consult the registrations of the KDI Trento lectures of the A.Y. 2020/2021 (here). This might be occasionally useful but with the following two points of attention: (1) while being very similar in spirit to the last year, the course this year presents some substantial differences, all exploiting that the lectures are in presence, and (2) in most cases we suggest you ask the lecturers for feedback or suggestions. In particular, to help students, after each phase of the methodology taught in the KDI course, there will be a Q&A lecture in which the students can ask questions about all their open problems and doubts.

At the end of the course, students will be asked to complete an online questionnaire about the overall process and methodology they will have learned. This feedback is essential to us as it is the basis for continuous evolution and improvement of the methodology being taught. To this extent, students are strongly encouraged to raise doubts, ask questions, and discuss their doubts about the methodology during the Q&A lectures.

Syllabus


Course Objectives and Outcomes

The Knowledge and Data Integration course aims to providing motivations, definitions, theorems and techniques for a concrete and effective understanding of what (in the context of computer science) is meant for knowledge and data integration. Providing also, techniques for analyzing and modelling knowledge and data as well as techniques for data and knowledge integration. Stimulating the students to continue their career with higher interest into data and knowledge representation in their own field of expertise, and to produce computer-processable solutions of relevant problems.

 

General Description

This course will cover the following topics:
  • the main issues which can be addressed when data and knowledge resources have to be integrated.
  • a general methodology (iTelos) for knowledge and data analysis, modeling and integration.
  • an analysis of the state of the art tools and methodologies for data analysis, modeling and integration.
  • an introduction to ontologies, Extended ER models and linguistic resources.
This is a hands-on, lab and experiment based course. Students will be given a data analysis/modelling/integration problem that they will have to solve, possibly, while taking the class. During the experiment, students will have to apply to the problem the notions introduced in class. The students splitted in teams, where each team will solve an integration problem adopting the methodology taught during the lectures.

Staff


Amarsanaa Ganbold
Naranchimeg Bold
Tsolmon Zundui
a portrait photo of Amarsanaa Ganbold
a portrait photo of Naranchimeg Bold
a portrait photo of Tsolmon Zundui
amarsanaag@num.edu.mn
naranchimeg@num.edu.mn
tsolmonz@num.edu.mn

Calendar and Material


The course runs from Sep, 6, 2022 till Dec 20, 2022 with the following schedule

     

  • Tuesday, 16:00-16:45, Room 3A-103

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  • Tuesday, 16:45-18:25, Room 3A-103

 

You might want to read the Instructions to understand how to take the course.

 

Notice also the titles and structure of the lessons yet to be delivered might change slightly. The rule of the thumb is: if there are links with materials, things won’t change; if there are no links to the materials, titles and content are just suggestions.

 

Lesson Number Date                                  Time Material                              Content of Material Lecturer(s)                 External resources                         Phase documentation deadline                        
0 Tue 6 Sep, 2022 16:00 Slides Introduction & Representation Diversity. G. Amarsanaa
1 Tue 6 Sep, 2022 16:45 Slides Solving Representation Diversity. G. Amarsanaa
2 Tue 13 Sep, 2022 16:00 Solving Representation Diversity G. Amarsanaa
3 Tue 13 Sep, 2022 16:45 Slides iTelos - methodology principles Z. Tsolmon
4 Tue 20 Sep, 2022 16:00 Slides iTelos - methodology structure Z. Tsolmon
5 Tue 20 Sep, 2022 16:45 Project Organization
Project Proposals
KDI Projects - organization & developments G. Amarsanaa Project Example
6 Tue 27 Sep, 2022 16:00 Slides Metadata G. Amarsanaa LiveSchema catalog
OpenDatTrentino catalog
SHAPEness
7 Tue 27 Sep, 2022 16:45 Slides Inception phase - theory G. Amarsanaa Resources
8 Tue 4 Oct, 2022 16:00 Slides Inception phase - practice G. Amarsanaa InceptionSheet
ProjectReportTemplate
9 Tue 4 Oct, 2022 16:45 Video
Slides
KOS
Data management Libs.
Project support tool (KOS)
Q&A
Z. Tsolmon
10 Tue 11 Oct, 2022 16:00 Inception phase - Q&A G. Amarsanaa
11 Tue 11 Oct, 2022 16:45 Slide Informal Modeling phase - Teleologies Z. Tsolmon
12 Tue 18 Oct, 2022 16:00 Slides-1
Slides-2
Informal Modeling phase - ETG model building Z. Tsolmon ModelingSheet
13 Tue 18 Oct, 2022 16:45 Slides-1
Slides-2
Inception & Informal Modeling phases - Evaluation G. Amarsanaa, Z. Tsolmon Project report - Inception
14 Tue 25 Oct, 2022 16:00 Informal Modeling phase - Q&A Z. Tsolmon
15 Tue 25 Oct, 2022 16:45 Slides-1
Slides-2
Slides-3
Formal Modeling phases - theory G. Amarsanaa
16 Tue 3 Nov, 2022 16:00 Slides Formal Modeling phases - data management G. Amarsanaa
17 Tue 3 Nov, 2022 16:45 Slides
Protege
Formal Modeling & Data Integration phases - evaluation G. Amarsanaa, B. Naranchimeg Protege-guidelines
Base-schema-structure
18 Tue 10 Nov, 2022 16:00 Formal Modeling phases - practice + Q&A G. Amarsanaa Project report - Informal modeling
19 Tue 10 Nov, 2022 16:45 Formal Modeling phase - Q&A G. Amarsanaa
20 Tue 17 Nov, 2022 16:00 Slides Data Integration phases - theory B. Naranchimeg
21 Tue 17 Nov, 2022 16:45 Karmalinker Data Integration phases - practice B. Naranchimeg
22 Tue 24 Nov, 2022 16:00 Data Integration phases - Q&A B. Naranchimeg Project report - Formal modeling
23 Tue 24 Nov, 2022 16:45 KG exploitation KG Exploitation (demo preparation) B. Naranchimeg GraphDB
SPARQL-book
SPARQL-W3C
24 Tue 1 Dec, 2022 16:00 General Q&A G. Amarsanaa, B. Naranchimeg, Z. Tsolmon

Exam


The exam will consist of two parts. The first, and most important, will be a presentation, in front of the lecturers and the colleagues, of the work developed. This will consist of a slide presentation, plus a demo. Details will be provided in class about how this will have to be done. The second and last part will consist of a written exam where students will be asked to describe and discuss the work done by the colleague in their group. In fact, the project will be done, modulo exceptions, by groups of two, where each person will be in charge of a different task (one in charge of developing the schema of the Knowledge Graph, the other developing the data populating it). The goal of the written exam will be to make sure that each student has a full understanding of the work done by the partner. But this should come for free for groups working together and continuously discussing the main issues which will arise, still each of them beingin charge of his/her.part.

Collaboration Opportunities


Knowledge and Data Engineering group of the Machine Intelligence Laboratory is seeking students who wants to work on research projects and thesis with KDE group. Anyone interested in these opportunities can send an email to amarsanaag@num.edu.mn, providing information about preferences in terms of topics or activities (if known).