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Domain-driven knowledge modelling for knowledge acquisition
Knowledge Acquisition, 1994Abstract Knowledge modelling is undoubtedly a major problem in knowledge acquisition. Drawing from industrial case studies that have been carried out, the paper lists some key problems which still dog knowledge modelling. Next, it critically reviews current knowledge modelling techniques and tools and concludes that these real knowledge acquisition ...
Hyacinth S. Nwana +3 more
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Mining Domain Knowledge on Service Goals from Textual Service Descriptions
IEEE Transactions on Services Computing, 2020With the rapid development of service-oriented computing, a large number of software applications have been developed based on the services computing framework.
Neng Zhang, Jian Wang, Yutao Ma
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Cross-Domain Knowledge Transfer using LLMs and Domain-Specific Knowledge Graphs
2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA)Given today’s data-driven landscape, the art of transferring knowledge across diverse domains is really crucial for developing versatile and intelligent systems. This research paper presents an investigation of cross-domain knowledge transfer in Large Language Models combined with domain-specific knowledge graphs.
Rajeev Kumar +2 more
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Knowledge-Reinforced Cross-Domain Recommendation
IEEE Transactions on Neural Networks and Learning SystemsOver the past few years, cross-domain recommendation has gained great attention to resolve the cold-start issue. Many existing cross-domain recommendation methods model a preference bridge between the source and target domains to transfer preferences by the overlapping users.
Ling Huang +5 more
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Domain Knowledge Guided Deep Learning with Electronic Health Records
Industrial Conference on Data Mining, 2019Due to their promising performance in clinical risk prediction with Electronic Health Records (EHRs), deep learning methods have attracted significant interest from healthcare researchers. However, there are 4 challenges: (i) Data insufficiency.
Changchang Yin +4 more
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2011
This chapter is concerned with quantitative approaches to the study of emerging trends and changes in science. A key insight is that a domain of knowledge is determined by the perspective we choose to take. This view echoes what we have seen earlier in Chapter 3 about the role of mental models in developing our understanding of the world.
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This chapter is concerned with quantitative approaches to the study of emerging trends and changes in science. A key insight is that a domain of knowledge is determined by the perspective we choose to take. This view echoes what we have seen earlier in Chapter 3 about the role of mental models in developing our understanding of the world.
openaire +1 more source
Knowledge Fragment Enrichment Using Domain Knowledge Base
2016Knowledge fragment enrichment aims to complete user input concept fragment by augmenting each concept with rich domain information. This is a widely studied problem in cognitive science, but has not been intensively investigated in computer science. In this paper, we formally define the problem of knowledge fragment enrichment in domain knowledge base ...
Jing Zhang +6 more
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Embedding domain knowledge for machine learning of complex material systems
MRS Communications, 2019Machine learning (ML) has revolutionized disciplines within materials science that have been able to generate sufficiently large datasets to utilize algorithms based on statistical inference, but for many important classes of materials the datasets ...
Christopher M. Childs, N. Washburn
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Utilising domain knowledge in inductive knowledge discovery
IEE Colloquium on Knowledge Discovery in Databases, 1995Describes a novel inductive knowledge discovery and learning algorithm, CUPID, and how it is able to utilise basic forms of domain knowledge to guide and improve its efficiency and accuracy. The system is capable of using two forms of domain knowledge-generalisation hierarchies on attribute values, and constructed intensional attribute definitions ...
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Domain Knowledge-Based Compaction
2013Domain knowledge about the problem on hand always leads to an effective solution. In this chapter, we discuss ways to make use of domain knowledge in generating abstraction. We consider binary classifiers such as support vector machine (SVM) and adaptive boosting (AdaBoost) to classify 10-class handwritten digit data.
T. Ravindra Babu +2 more
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