1. Research on Knowledge Element Relation and Knowledge Service for Agricultural Literature Resource
Xie nengfu; Sun wei and Zhang xuefu
3rd April 2017

2. Background Knowledge Service
Knowledge has become the key economic resource and the dominant - and perhaps even the only - source of competitive advantage----Peter Drucker.
Now because of the information explosion, it will take a long time for users to summarize and analyze the search results.
knowledge service under the background of literature service is an innovation and development of traditional information service
Knowledge service’s characteristics for literature service :
Knowledge service is centered by the consumer with the aim of consumer satisfaction.
It is around the knowledge innovation target,
It is personalized and customized service.
Agricultural User in china:
27,000 agricultural researchers.
1 million agricultural extension workers.
0.4 billion agricultural production and operation users.

3. Our Work
Knowledge
discovery
Knowledge
representation
Content discovery
and recommendation KB
Knowledge element+
Linking relation 
Literature resource
Knowledge service
Knowledge base
Knowledge Network
Our work:
How to discover knowledge for literature resource? That is how to discover knowledge based on knowledge element from agricultural literature resources.
How to represent the knowledge element?
How to implement a knowledge element-based knowledge service system?

4. Knowledge service 知识服务的基础：信息的知识化
The basis of knowledge service is how to information translating  realize knowledge.
We can find that knowledge element is an important part in knowledge service system.
Literature
service
Knowledge
Service
Element
知识服务的基础：信息的知识化

5. Knowledge element In our work, knowledge element’s characters are:
It is a knowledge unit with complete knowledge representation.
It is an controllable unit of explicit knowledge
It is logically complete, and can represent an integrate fact, principle, method and technique, and etc..
Knowledge elements connect each other using linking semantic word, which can lead to the increase of the value of knowledge, and even produce the new knowledge.

6. Knowledge element structure
The knowledge element structural description includes the two elements:
the attribute of the knowledge element
Association relations

7. Knowledge element instant
Knowledge element = { Subject category, Knowledge source, Knowledge type}
Subject category is decided by knowledge system.
Knowledge source is an identifier of knowledge element.
Knowledge type is digital content description and type of Knowledge element.
Knowledge elements:

8. Association relation model
Association relation includes 12 types:
Broader
narrower
Include
Part of
Consistof
Consistedof
Equal
Sameas
Kindof
Instanceof
Attributeof
related

9. KE-based Knowledge service system
Knowledge service system mainly contains three parts:
Knowledge system includes subject classification and categorization schemes, and also includes highly structured vocabularies (glossary).
Knowledge element extraction and indexing is the core part of the system, which produces the knowledge elements indexed.
Service product is built on the knowledge element, maybe:
Knowledge search
Knowledge discovery
Knowledge mining

10. Knowledge System Knowledge System is consisted of：
Subject classification
Subject glossary
Subject word
Free word
Knowledge System is used for knowledge element indexing.
CP001 is subject classification label
“资源” and “营养” are subject words

11. Knowledge element extraction and indexing
Knowledge element extraction and indexing ‘s steps includes:
Firstly, extract keywords. A keyword is a word that appears in the title (title, chapter name) of the digital content, and a summary, which will be extracted.
Secondly, knowledge element content extraction. It will analyze the sentences and decide the content unit and classification label for knowledge element.
Lastly, knowledge element generation. It will decide the indexing words and classification code by conception extraction, and generate a new knowledge element by indexing according to knowledge element description structure.

12. Knowledge element-based search
Browse by classification 
User can get the related knowledge network of a concept in knowledge subject classification. The result show the knowledge element and their relations, so the user can get the related knowledge clusters by the relations. You can click the knowledge element node to get its detail content description.
Search by keywords
If the user use a keyword to search the knowledge element base. The system will return the semantic related result, and show the knowledge network that contain the related knowledge elements and their link relations, which will produce great knowledge value for user’s demand.
Knowledge element network of a concept in subject classification
Knowledge elements network of a search keyword

13. Knowledge element-based knowledge discovery
Knowledge element indexing using knowledge organization system has established a network of knowledge chain and semantic chain between knowledge elements. It embodies the relationship between knowledge reference and reference, semantic association. So the knowledge discovery and knowledge Reorganization is possible and feasible, and users can not only access the relevant knowledge of the knowledge element feasible, but also from the knowledge of the library directly to obtain their own knowledge element based on the knowledge network.
Discovery agricultural experts who research on the same problem, which is embodied by knowledge element
Discovery knowledge elements belong to the same knowledge subject .
Discovery the organization which research on the same problem.
Discovery the develop history, Research Area, develop trend, and etc. of a knowledge subject .
2010
1999
Time
2017
2012 B C G F I H E D A J K L M
2008

14. Knowledge element-based knowledge mining
Discovery of useful information and valuable knowledge from knowledge element and their linking relations is feasible.
Subject research hot topic system can firstly classify knowledge elements into different classifications, and then produce different hot topics by clustering algorithm. It can mine some potential hot topics which is not easily found by traditional method.
Hot topics and their relations.
Hot topic and its sources
Hot topic and its intension

15. conclusions
Knowledge element-based knowledge service is an intelligent activity, and is a process of scientists’ knowledge reorganized and extraction.
Knowledge element-based knowledge mining and application research is significant, but very difficult.

16. Thank You！