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DAI for Information Retrieval
Published in Satya Prakash Yadav, Dharmendra Prasad Mahato, Nguyen Thi Dieu Linh, Distributed Artificial Intelligence, 2020
Annu Mishra, Satya Prakash Yadav
The first thing that comes to mind when we see the topic “DAI for Information Retrieval” is what is information retrieval means. As we know, the amount of data available today is increasing at a very high rate—for example, on the Internet and the World Wide Web. Data can be in many forms, including audio, video, image, text, etc. Information retrieval is a way to obtain relevant data from that available. Information retrieval deals with search processes in which we need to identify a suitable subset of information. Distributed artificial intelligence (DAI) is responsible for managing communication between smart and keen agents. DAI endeavors to develop smart operators that settle on choices that permit them to accomplish their objectives in a world populated by other smart operators having their individual objectives. For the past two decades, semantic networks have played an important role in knowledge representation and artificial intelligence, i.e. basically in cognitive science-related research fields. It has a wide area of application, ranging from medical science, cognitive behavioral therapy, data mining, games, and politics. It has also been used in search, discovery, matching, content delivery, and synchronization of activity and information. The use of semantic network analysis has also been observed in thematic analysis of Twitter for the #Metoo hashtag. “Semantic networks” is a keyword that has been used to represent the concept. A semantic network or net is a graph structure for representing knowledge in patterns of interconnected nodes and arcs.
Experts and Expert Systems
Published in Robert W. Proctor, Van Zandt Trisha, Human Factors in Simple and Complex Systems, 2018
Robert W. Proctor, Van Zandt Trisha
None of the representations we have described satisfy these or any other criteria perfectly, so the best representation to use will depend on the purpose of a particular expert system. Production systems are convenient for representing procedural knowledge, since they are in the form of actions to be taken when conditions are satisfied (see examples earlier in this chapter). They also are easy to modify and to understand. Semantic networks are handy for representing declarative knowledge, such as the properties of an object. Frames and scripts are useful representations in situations where consistent, stereotypical patterns of behavior are required to achieve system goals. Sometimes an expert system will use more than one knowledge representation (like the ship design system we describe later on).
Introduction to Expert Systems
Published in Chris Nikolopoulos, Expert Systems, 1997
The Prolog knowledge base given below will be used in the sequel for explaining basic Prolog programming concepts. Figure 3.1 displays the knowledge base in the form of a semantic network. A semantic network is a knowledge representation technique where knowledge is displayed as a directed labeled graph. The nodes correspond to concepts or objects and the arcs correspond to relations between the concept nodes. The arc labels indicate the relation names. An arc (relation) can be bidirectional, like "is the brother of ", or unidirectional like "is a faculty in". Knowledge representation techniques including semantic networks will be examined in more detail in Chapter 5.
English Translation proofreading System based on Information Technology: Construction of semantic Ontology Translation Model
Published in Applied Artificial Intelligence, 2023
In 1973, R. F. Simon of The University of Texas established semantic Networks by using Fillmore’s Case Grammar on the basis of Woods’ ATN (Krüger 2016). Semantic network is used to express a complex concepts and their mutual relations of the directed graph, it is made of a semantic network composed of nodes and a directional arc described diagram to form, the nodes are used to represent all kinds of things, the concept and the situation, the properties, status, events and actions, etc., and with the direction of the curve indicates the semantics of the relationship between the nodes. Semantic network can not only describe the concept, situation, attribute and state of things themselves, but also describe the relationship between things. Semantic network represents knowledge based on basic semantic connections, which is also the basic unit of complex semantic connections. Therefore, it is possible to combine some basic semantic connections into arbitrary complex semantic connections.
Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation
Published in Connection Science, 2022
Tianyuan Li, Xin Su, Wei Liu, Wei Liang, Meng-Yen Hsieh, Zhuhui Chen, XuChong Liu, Hong Zhang
There may be a different proportion of new users and new items in the recommendation system, and interaction between these users and items is sparse. As a result, personalised recommendation for new users is challenging due to the cold-start problem. Deep learning (Liang, Xie, et al., 2020) has achieved great results in a variety of artificial intelligence domains. However, in order to obtain significant generalisation, a large number of examples must be trained. Deep learning becomes ineffective when used in a cold-start recommendation scenario with sparse user–item interactions. Data augmentation at the data level or the provision of auxiliary data (Zhu et al., 2019) are the most typical solutions to cold-start recommendations. There are also some methods involving the high-level representation of the data, such as capturing the rich heterogeneous data (Chang et al., 2021) of the items and the users, using the data representation of the heterogeneous information network, in addition to considering the basic characteristics of the data. Alternatively, a semantic network can be built using a knowledge graph, in which nodes represent entities and edges to reflect various semantic relationships between items. There are also cross-domain recommendations based on mapping of neighbour user attributes, or recommendations based on mining friend lists on social networks. These techniques rely largely on data and have a number of drawbacks.
Variable incremental adaptive learning model based on knowledge graph and its application in online learning system
Published in International Journal of Computers and Applications, 2022
Knowledge graph [14–16] is the product of the development of computer technology in the Internet era. It describes the relationship between things in the real world through entity and entity relationship. Knowledge graph is essentially a large-scale semantic network, which is used to describe the relationship between some things in the real world. It was officially proposed by Google on May 17, 2012 in order to improve the query speed of search engine, optimize the service, and improve the search quality and search experience of users. Knowledge graph is composed of some interconnected entities and attributes, which form a huge semantic network. A more complex knowledge graph, such as the intelligent financial knowledge graph – combined information, is the industry knowledge graph in the financial field. It helps the financial industry get through multiple data, identify the relationship between the main body and risk early warning in a timely and accurate manner, help customers increase revenue, reduce costs, improve efficiency and avoid risks. At present, medical treatment, film and other fields also have a well-established knowledge graph. In this paper, knowledge graph is applied to the research and development of online adaptive system. Through knowledge graph, students, topics and knowledge points are organically combined to reflect the learning effect and practice of students on knowledge points. It can not only improve students’ interest in learning, but also let students practise for specific purpose to improve students’ learning efficiency. Teachers can also check the ranking of learning situation to urge the students who are lower in the ranking.