1. Kittiya Poonsilp, Rujijan Vichivanives, Attakorn Poonsilp
Woven Fabric Data Retrieval Across Museums in Thailand using Knowledge Graph
Kittiya Poonsilp, Rujijan Vichivanives, Attakorn Poonsilp
Good afternoon everybody. My name is Kittiya Poonsilp. Today I will present “Woven fabric data retrieval across museums in Thailand using knowledge graph”. We have 3 researchers: Me, Rujijan Vichivanives and Attakorn Poonsilp.

2. Agenda Introduction High Level Architecture Knowledge Graph
Query Process
Result
Conclusion
Here is the agenda.
Firstly, I will give you an introduction to our research
follow by high level architecture so you can see the overall concept of the research.
Then I will show the knowledge graph that
we used as well as the query process into the knowledge graph.
Then show the result and finally the conclusion.

3. Introduction Thai woven fabrics Store, retrieve woven fabric data
Data collected from museums in Thailand
Use Knowledge Graph
Query using natural language e.g. “What is the oldest woven fabric in Thailand ? ”
Let me give you some introduction. In Thailand, woven fabrics is very popular, there are several villages that produce a high-quality and beautiful woven fabrics.
Weaving a fabric is the part of their life and their culture for a long time. One of the popular village is the Na Muen Sri village, they produce a woven fabrics since 400 years ago.
Their old and antique fabrics are kept in several museums in Thailand nowadays.
Anyway, most museums use physical paper to keep a record of a fabric which lead to several problems, for example, hard to find the record for a desire fabric and difficult to maintain in a long term.
In this research, we propose the new way to keep and organize these data.
All woven fabric record as well as its related information will be kept in knowledge graph.
The advantage of using knowledge graph is
we can connect the woven fabric with other related information like a weaver, village, pattern, purpose of each type of fabric, et cetera.
We also support natural language input like “What is the oldest woven fabric in Thailand”. So it will be much more easier for user to use our system.

4. High Level Architecture
Data are stored in Centralized Database
Knowledge Graph is built from data in the database
Natural Language Processor resolves user intention from query text
Query Engine do the query from user intention
Query result will be shown to user
This is a high level architecture of our research.
All data that we’ve gathered from museums are kept in our centralized database.
Knowledge Graph Builder build the knowledge graph from data in the database.
User input their query text and interact to our system via the Frontend
Query text from user will be processed by Natural Language Processor to resolve user intention
Query Engine do the query in the knowledge graph
And query result will be built by the result builder and shown to a user

5. Sample Records in Database
This is the sample records in our database.
The record consist of several fields like a name, type of a fabric, warp color, weft color, pattern name, weaver name, et cetera.

6. Knowledge Graph Built from database
Here is the knowledge graph that is built from data in database.
You can see that we have a node for woven fabric, weaver, pattern, type, warp color, weft color as well as its relationship between nodes.
All data in database will be loaded into this knowledge graph.
Once knowledge graph is ready, then user can start to input a query.

7. Query Process - Word Breaking
The sentence “What is the oldest woven fabric in Thailand?” will be broken to array of words
Part-of-speech tagging
I will show you on how the query works.
Assumes that user input the query “What is the oldest woven fabric in Thailand?”.
The query string will be broken into words and do part-of-speech tagging on each word.
The result from this step is the linked list containing all words with part-of-speech tag.

8. Query Process - Knowledge Node Matching
Match word or words to node in knowledge graph using longest string matching
In this step, we will match word or group of words into a node in the knowledge graph.
The “Woven Fabric” will be matched to Woven Fabric node and “Thailand” will be matched to Thailand node in the knowledge graph.
Now we have a pattern NODE-IN-NODE in our linked list.
If this pattern found, we have a rules to enumerate all Woven Fabric nodes in Thailand node.

9. Query Process - Data Consolidation
Enumerates all “Woven Fabric” nodes under “Thailand” node
Calculate shortest path from “Woven Fabric” to “Thailand” (Country node)
Back-propagate along the shortest path and enumerates descendants.
In order to enumerate all woven fabric nodes under Thailand node, we firstly calculate the shortest path from “Woven Fabric” to “Thailand” node.
The shortest path is shown in blue.
Once we have a shortest path,
we will start from Thailand (Country node), enumerates all Region, for each region, enumerates all Province and so on until we get all woven fabrics.

10. Query Process - Data Consolidation
Replace with new composite node, storing all woven fabrics
Query engine continue to match more pattern in the linked list
DT-JJS-COMPOSITE pattern is found, the list will be ordered by date and get the oldest one
All woven fabrics will be kept in Woven Fabric List.
And we will replace the source nodes in the linked list with this new node.
The new node is Composite node shown in the picture.
Query Engine will continue to match remaining pattern in the linked list. DT-JJS-COMPOSITE pattern is found and JJS has the keyword “oldest”, in this case,
we have a rule to sort the list by date and get the oldest one.
Once we have the oldest item.
The remaining pattern is “What is”, so the oldest item will be used as the result.

11. Result “Woven fabric weaved by Plek”
Here is some more result from our experiment.
This is the result from the query “Woven fabric weaved by Plek”.
Plek is the weaver name.
The query engine will enumerate all woven fabric nodes under specified weaver node.
And this is the result that we got.

12. Result “The oldest woven fabric weaved by Plek”
This is another query: “The oldest woven fabric weaved by Plek”.
It’s similar to the previous sample, just add one more process to sort the result by date and pick the oldest one as a final result.

13. Result “Look Kaew Pra Som pattern”
This query is “Look Kaew Pra Som pattern”.
Asking for the pattern name “Look Kaew Pra Som”.
And the result is the list of all woven fabric that has the pattern name “Look Kaew Pra Som”.

14. Conclusion
More efficient way to store and search a woven fabric compared to a traditional way
Easy to use especially for non-IT people
Conclusion:
The proposed method is more efficient way to store and search a woven fabric compared to a traditional way.
And it’s easy to use especially for non-IT people.