Knowledge mapping is a relationship-based expression that can easily resolve both of the above issues and is therefore widely used in anti-fraud. First, knowledge mapping can provide a very easy way to add new data sources. Second, knowledge mapping itself is an intuitive expression of relationships that can help to more effectively analyse specific potential risks in complex relationships。
Agriculture: multimedia knowledge guidance
A large amount of agricultural information is stored in decentralized formats, the traditional relationship database model is not suitable for complex and volatile areas, and it is not possible to define all possible knowledge points and build key database models, which can be managed by a more flexible knowledge mapping model. Using extraction and extraction techniques, the knowledge is derived from a variety of multi-source isomeral data and is presented in a uniform map to form a complete knowledge base, mapping crop knowledge, soil knowledge, fertilizer knowledge, disease knowledge and weather knowledge. Multimedia knowledge maps are developed by graph linking to photographic information, which is more visual and user-friendly for farmers than expertise。
Enabling cognitive intelligence
The value of knowledge mapping for artificial intelligence lies in making machines cognitive. Machine cognitive intelligence is broad and diverse in its application, in terms of precision analysis, intelligence search, intelligent recommendation, intelligent interpretation, more natural human interaction and deep relationship reasoning。
Smart analysis
The lack of background knowledge, such as knowledge mapping, limits the means of understanding big data across tools, limits precision and precision analysis based on big data and significantly reduces the potential value of big data. Thus, while a growing number of industries or enterprises have accumulated sizeable data, these data have not only failed to create value, but may even become negative assets because of the high cost of transportation。
The development of knowledge maps provides a strong background base of knowledge that can enable critical analysis, business insight, military intelligence analysis and business intelligence analysis, such as precision analysis based on large data。
Knowledge mapping and cognitive intelligence based on this provide the possibility for fine analysis. Manufacturing enterprises, such as auto manufacturers, want to have individualized manufacturing used in fine analysis cases. The knowledge mapping builds on the background knowledge of car valuations, such as car models, car decorations, power, energy consumption, etc., extracts consumer feedback and feedback on automobiles, consumer improvement proposals, competitive brands, etc., and customizes them on the basis of needs and individuality。
Natural person interaction
Human interaction will become simpler and more natural. The interaction of natural persons includes natural language questions and answers, dialogue, sensory interaction, face-to-face interaction, etc., which requires machines to be able to understand the natural language of human beings and to have a high level of cognitive intelligence and strong background knowledge. Session (co)Questions and answers (qa) will gradually replace traditional keyword-search interactions. In the future, voice assistants like google now, siri, amazon alexa and the next generation of dialogue robots will read, browse, or even watch movies, tv dramas, and answer any questions that concern us。
Deep influence on the social fabric
While deep learning represents significant progress in artificial intelligence, the non-transparent and inexplicable nature of in-depth learning has become an obstacle to its development. “understanding” and “interpretation” are the next challenges that artificial intelligence needs to overcome, while knowledge maps provide an entirely new perspective and opportunity for “explainable artificial intelligence” and bring about a new era of technology, commerce and society – the dawn of the cognitive era。
For human beings, knowledge mapping will empower human beings by enabling artificial intelligence, enabling us to understand and operate complex systems in societies, improving our ability to harness science and technology and improving the living environment of humankind, and making human interaction with machines more natural, predictable and emotional。
Date: november 11, 2022 - november 15, 2022
Objective: to integrate theory and practice closely, to deepen and to evolve. From a conceptual point of view, emphasis is placed on the transformational thinking of building methods and technologies, which helps participants to systematically master the core technical principles of knowledge mapping. Training in examples of core technologies based on encyclopedia, combined with the application of digital libraries, medical, financial, electrician, agricultural, legal etc., helps participants to rapidly accumulate knowledge mapping project experience。
Specific arrangements:
I. Overview of knowledge maps
1. 1 origin and history of knowledge mapping
1. 2 history of development of knowledge maps - from framework, ontological, semantic, linked data to knowledge maps spectrum
1. 3 nature and value of knowledge mapping
1. 4 knowledge mapping vs traditional knowledge database vs relational data library
1. 5 classic knowledge mapping
1. 5. 1 classic cyc, wordnnet, wikidata, dbpedia, yago, nell, etc. Library
1. 5. 2 sector knowledge mapping:
Google knowledge mapping, microsoft physical mapping, ali knowledge mapping, medical knowledge mapping, genetic knowledge mapping, etc
Ii. Knowledge mapping applications
2. 1 knowledge mapping applications
2. 2 introduction to knowledge mapping applications
2. 2. 1 applications of knowledge maps in digital libraries
2. 2. 2 application of knowledge maps in defence, intelligence, public security
2. 2. 3 financial application of knowledge maps
2. 2. 4 application of knowledge mapping in electronic commerce
2. 2. 5 application of knowledge maps in areas such as agriculture, medicine, law
2. 2. 6 application of knowledge mapping in the manufacturing industry
2. 2. 7 application of knowledge maps in large data integration
2. 2. 8 application of knowledge maps in human interaction (smart questions and answers)
Knowledge expression and knowledge modelling
3. 1 concept of knowledge
3. 2 knowledge expression methods
A. Semantic networks b. Generating rules c. Framework system d. Description of logic e. Body f. Rdf and rdfs
G. Owl and owl2 fragments h. Sarql query languages
I. Json-ld, rdfa, HTML5 microdata, etc
3. 3 knowledge expression for typical knowledge base projects
3. 4 knowledge modelling methodology
3. 5 knowledge expression and knowledge modelling practices
1. Presentation and modelling of the three countries ' illustrative knowledge maps
Academic knowledge mapping, etc
