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

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.

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?

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

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.

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

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:

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

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

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

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.

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

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

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

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.

Thank You！ Email：xienengfu@caas.cn