Knowledge map and block chain
according to Xiao Wang, the concept of blockchain as a service (baas) is mainly proposed by Microsoft and IBM. To put it bluntly, it is actually a new type of cloud service, a cloud service combined with blockchain technology. For example, Microsoft's azure cloud computing platform and IBM's bluemix garage cloud platform all provide blockchain as a service baas
blockchain as a service (baas) is a space created by enterprises such as Microsoft and IBM from their own cloud service network to run a blockchain node. Compared with ordinary nodes and exchange nodes, the main purpose of baas nodes is to quickly establish the development environment they need, and provide a series of operation services based on blockchain, such as search query, transaction submission, data analysis, etc. these services can be centralized or decentralized to help developers verify their concepts and models faster. The service of baas node is reflected in: it is more instrumental and easy to create, deploy, run and monitor blockchain.
Blockchain is a new application mode of distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and other computer technologies. Blockchain is an important concept of bitcoin,
in essence, it is a decentralized database. At the same time, as the underlying technology of bitcoin, it is a series of data blocks generated by using cryptographic methods. Each data block contains a batch of information of bitcoin network transactions, Used to verify the validity of its information (anti-counterfeiting) and generate the next block
extended data
most blockchain public chains are limited by scalability. The biggest feature of blockchain technology is decentralization, which requires that all accounts in the network need to deal with the accounting process. Distributed accounting has high security, low misoperation rate, political neutrality and correctness
however, blockchain technology embraces these features at the same time, sacrificing scalability, unable to meet the personalized supervision, and slightly insufficient in protecting data privacy. Moreover, with the increase of the number of ledger, the interaction delay will increase exponentially, that is to say, the more ledger in the blockchain network, the higher the delay
the Internet of things uses blockchain as a communication bridge between things. In order to achieve this, we can use
the programmability provided by Ethereum, which is not only to let people's property be tracked and registered. It has some new uses. Under various methods and rules, Ethereum blockchain embedded in the car key can be sold or rented to generate a new P2P for renting or sharing cars. Further, some people talk about the application of this technology to make self driving cars a social public resource. Under pre-set proceral rules, such vehicles can store their own digital money to pay for them from renting fuel, repairs and parking spaces.
blockchain focuses on keeping accurate records, authentication and execution, while AI helps to make decisions, evaluate and understand certain patterns and data sets, and ultimately proces autonomous interaction. AI and blockchain share several characteristics, which can ensure seamless interaction in the near future. Three main features are listed below
1. AI and blockchain need data sharing
distributed database emphasizes the importance of data sharing among multiple clients on a specific network. Similarly, AI relies on big data, especially data sharing. The more open data available for analysis, the more accurate the prediction and evaluation of the machine, and the more reliable the generated algorithm
2. Security
when dealing with high-value transactions on the blockchain network, there are great requirements for the security of the network. This can be implemented through existing agreements. For artificial intelligence, the autonomy of the machine also needs high security, in order to rece the possibility of catastrophic events
3. Trust is a necessary condition
for any widely accepted technological progress, there is no greater threat than the lack of trust, and artificial intelligence and blockchain cannot be ruled out. In order to make the communication between machines more convenient, we need to have an expected trust level. In order to execute certain transactions on the blockchain network, trust is a necessary condition
the impact of blockchain and artificial intelligence on ordinary people
in short, blockchain is a community-based technology, which can make value exchange more secure. Blockchain is just like its name. Each block contains an encrypted transaction record. The blocks are arranged in chronological order, and the security is guaranteed by a cryptographic system. Blockchain is a technology that can change rules, and its emergence is a revolutionary innovation
there are many functions of blockchain, and the specific application is also very wide. For example: if blockchain is used in the food instry, people will no longer worry about eating harmful food. If blockchain is used for diamond proction, consumers will no longer have to worry about buying fake diamonds. If the blockchain is used in the ecation instry, intellectual property protection can be strengthened. If the blockchain is used in the insurance instry, it can alleviate the information asymmetry of the insurance business and help to improve the security of the insurance business
the impact of blockchain and artificial intelligence on ordinary people is enormous. Just imagine, if blockchain and artificial intelligence are combined, will their role expand? Yes, the combination of the two can really have more impact to change the lives of ordinary people
blockchain and artificial intelligence are two extremely important roles in the field of technology, which bring convenience to our proction and life. If we find an intelligent way to make them work together, the impact of their interaction is unimaginable. This is also the core of OMT. After the combination of these two technologies, the future application scenarios are revolutionary and exciting. In the new ecological construction, data storage, sharing mechanism, platform problems, security problems, etc. can be overcome by using each other's technologies. OMT will create maximum value for global users and enterprises and bring more convenience to ordinary people through blockchain + artificial intelligence technology
the answer is over, hope to adopt, thank you!
Puyin is a high-quality Tibetan tea asset, which is stored, identified, evaluated and authorized by three parties. It is a standard digital currency issued through encryption and digitization. Puyin implements it in strict accordance with the concept of "white paper on standard digital currency" as its operation guideline. It is also a digital currency based on blockchain technology.
Knowledge graph is the current research hotspot. Since Google launched its first version of knowledge map in 2012, it has set off an upsurge in academia and instry. In the following short year, major Internet enterprises have launched their own knowledge mapping procts in response. For example, in China, Internet giant network and Sogou launched "Zhixin" and "cube" respectively to improve their search quality. Then, compared with these traditional Internet companies, what aspects of knowledge mapping can be applied to the instry internet finance, which is on the cusp of today's storm
contents
1. What is knowledge map
2. Representation of knowledge map
3. Storage of knowledge map
4. Application
5. Challenge
6. Conclusion
1. What is knowledge map
knowledge map is essentially a semantic network, which is a graph based data structure composed of points and edges. In the knowledge map, each node represents the "entity" existing in the real world, and each edge is the "relationship" between entities. Knowledge mapping is the most effective representation of relationships. Generally speaking, knowledge mapping is a relational network that connects all kinds of information. Knowledge mapping provides the ability to analyze problems from the perspective of "relationship"
knowledge reasoning
reasoning ability is an important feature of human intelligence, which enables us to find hidden knowledge from existing knowledge. General reasoning often needs the support of some rules [3]. For example, the "friend" of "friend" can infer the "friend" relationship, and the "father" of "father" can infer the "grandfather" relationship. For another example, many of Zhang San's friends are also Li Si's friends, so we can speculate that Zhang San and Li Si are also likely to be friends. Of course, there is a question of probability. When there is a large amount of information, how to combine the side information with the reasoning algorithm is the key. Common reasoning algorithms include logic based reasoning and distributed representation based reasoning. As deep learning becomes more and more important in the field of artificial intelligence, reasoning based on distributed representation method has become a hot research topic. If you are interested, please refer to the current work progress in this area [4,5,6,7]
big data, small samples and building an effective ecological closed-loop are the key
although the amount of data available is very large, we still face the problem of small samples, that is, the number of samples is small. Suppose we need to build an anti fraud scoring system based on machine learning, we need some fraud samples first. But in fact, the number of fraud samples we can get is not much. Even if there are millions of loan applications, there are probably only tens of thousands of samples marked as fraud. This poses a higher challenge to the modeling of machine learning. We get every fraud sample at a very high price. As time goes on, we are bound to collect more samples, but the growth space of samples is still limited. This is different from the traditional machine learning system, such as image recognition, it is not difficult to get hundreds of thousands or even millions of samples
under such a small sample condition, it is particularly important to build an effective ecological closed loop. The so-called ecological closed-loop refers to the construction of an effective self feedback system, which can feed back to our model in real time, and make the model constantly self optimize, so as to improve the accuracy. In order to build this self-learning system, we should not only improve the existing data flow system, but also go deep into each business line and optimize the corresponding process. This is also the necessary process of the whole anti fraud link. We need to know that the whole process is full of games. So we need to constantly adjust our strategy through feedback signals
Conclusion
knowledge mapping has attracted more and more attention in academia and instry. In addition to the application mentioned in this paper, knowledge mapping can also be applied in different fields such as authority management, human resource management and so on. We will talk about this application in detail in the following articles
[1] de Abreu, D., Flores, A., Palma, g., Pestana, v., Pinero, J., queipo, J.,... & amp; Vidal, M. E. (2013). Choosing between graph databases and RDF engines for consuming and mining linked data. In cold.[2] user behavior tutorial
[3] Liu Zhiyuan's knowledge map knowledge base in machine brain Chapter 2: knowledge map knowledge base in machine brain
[4] nickel, M., Murphy, K., tresp, v., & amp; Gabrilovich, E. A Review of Relational Machine Learning for Knowledge Graphs.
5Socher, R., Chen, D., Manning, C. D., & Ng, A. (2013). Reasoning with neural tensor networks for knowledge base completion. In Advances in Neural Information Processing Systems (pp. 926-934).
6Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems (pp. 2787-2795).
7Jenatton, R., Roux, N. L., Bordes, A., & Obozinski, G. R. (2012). A latent factor model for highly multi-relational data. In Advances in Neural Information Processing Systems(pp. 3167-3175).