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About 10,000 papers for “Domain knowledge”

Domain knowledge base

Samira Noferesti · Figshare · 2015

a knowledge base of known effects and side effects of drugs, was collected from www.dailymed.com and www.webmd.com websites

Cited by 0Open access

Modeling Domain Knowledge

Author(s A. Abu Hanna, W. N. H. Jansweijer · 2016

Copyright It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content licence (like

Cited by 0

Applying domain knowledge to knowledge discovery

Tucker, David Allen · American University Research Archive · 2023

The amount of data being collected and stored in databases is increasing constantly, because of this the process of knowledge discovery can and will become increasingly time consuming. This thesis researches and shows the advantages of applying previous knowledge about the data, known as domain knowledge, to a data set being analyzed when running knowledge discovery programs. Analysis was completed by running several knowledge discovery hypotheses on sample data and comparing the rules generated when applying domain knowledge and not applying domain knowledge. It was shown that applying domain knowledge clearly helps the knowledge discovery process mostly by reducing the amount of non-interesting rules and amount of time to generate the rules.

Cited by 0Open access

Using domain knowledge in knowledge discovery

Suk-Chung Yoon, Lawrence J. Henschen, E. K. Park et al. · 1999

With the explosive growth of the size of databases, many knowledge discovery applications deal with large quantities of data. There is an urgent need to develop methodologies which will allow the applications to focus search to a potentially interesting and relevant portion of the data, which can reduce the computational complexity of the knowledge discovery process and improve the interestingness of discovered knowledge. Previous work on semantic query optimization, which is an approach to take advantage of domain knowledge for query optimization, has demonstrated that significant cost reduction can be achieved by reformulating a query into a less expensive yet equivalent query which produces the same answer as the original one. In this paper, we introduce a method to utilize three types of domain knowledge in reducing the cost of finding a potentially interesting and relevant portion of the data while improving the quality of discovered knowledge. In addition, we propose a method to select relevant domain knowledge without an exhaustive search of all domain knowledge. The contribution of this paper is that we lay out a general framework for using domain knowledge in the knowledge discovery process effectively by providing guidelines.

Cited by 38Open access

Using domain knowledge in knowledge discovery

Lawrence J. Henschen, Suk-chung Yoon, E. K. Park · 1999

With the explosive growth of the size of databases, many knowledge discovery applications deal with large quantities of data. There is an urgent need to develop methodologies which will allow the applications to fo-cus search to a potentially interesting and relevant portion of the data, which can reduce the computa-tional complexity of the knowledge discovery process and improve the int,erestingness of discovered knowl-edge. Previous work on semantic query optimization, which is an approach to take advantage of domain knowledge for query optimization, has demonstrated that significant cost reduction can be achieved by re-formulating a query into a less expensive yet equivalent query which produces the same answer as the original one. In this paper, we introduce a method to utilize three types of domain knowledge in reducing the cost of finding a potentially interesting and relevant portion of the data while improving the quality of discovered knowledge. In addition, we propose a method to se-lect relevant domain knowledge without an exhaustive search of all domain knowledge. The contribution of this paper is that’we lay out a general framework for using domain knowledge in the knowledge discovery process effectively by providing guidelines.

Cited by 0

Cross domain knowledge verification: verifying knowledge in foundation based domain ontologies

Najam A. Anjum (7203740), Jennifer Harding (1258389), Robert I.M. Young (1257699) · Loughborough University Institutional Repository (Loughborough University) · 2010

Knowledge verification refers to the process of making sure that the knowledge shared between knowledge bases of two parties is correctly understood on both sides. Domain ontologies developed out of a foundation ontology have a potential to improve the knowledge verification methods. This can be done by following concepts in domain ontologies to their origin and constituent conceptualisations in the foundation ontology. This is possible when matching ontologies belonging to two different domains but developed out of a single foundation ontology. Along with the concepts, a prescribed way of using these concepts by domain ontology builders also needs to be included in the foundation ontology. This prescribed way can exist in the form of an ontology of constraints which governs and shapes the building of domain ontologies according to the needs of the verification system and thus makes them more interoperable.

Cited by 1Open access

Cross-Domain Knowledge Transfer using LLMs and Domain-Specific Knowledge Graphs

Kumar · Zenodo (CERN European Organization for Nuclear Research) · 2025

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. Through this work, we envision harvesting the expansive language understanding capabilities of LLMs with structured contextual information provided by the specialized knowledge graphs to foster better and more accurate knowledge dispersion between different fields. Our approach with LLMs, using Wikidata, DBpedia, and domain-specific repositories of factual data, demonstrates much better performance in tasks like entity recognition, relationship extraction, and semantic rea- soning on a wide variety of domains. This is because the synergy of LLMs and knowledge graphs brings better model adaptability and precision, bridges gaps between different knowledge areas, and allows applications in healthcare, finance, and environmental science. Our results point to the potential of this integrative methodology to move artificial intelligence systems forward in be- coming more robust, context-sensitive, and capable of exploiting specialized knowledge in complex, real-world problem-solving.

Cited by 0Open access

Cross-Domain Knowledge Transfer using LLMs and Domain-Specific Knowledge Graphs

Kumar · Zenodo (CERN European Organization for Nuclear Research) · 2025

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. Through this work, we envision harvesting the expansive language understanding capabilities of LLMs with structured contextual information provided by the specialized knowledge graphs to foster better and more accurate knowledge dispersion between different fields. Our approach with LLMs, using Wikidata, DBpedia, and domain-specific repositories of factual data, demonstrates much better performance in tasks like entity recognition, relationship extraction, and semantic rea- soning on a wide variety of domains. This is because the synergy of LLMs and knowledge graphs brings better model adaptability and precision, bridges gaps between different knowledge areas, and allows applications in healthcare, finance, and environmental science. Our results point to the potential of this integrative methodology to move artificial intelligence systems forward in be- coming more robust, context-sensitive, and capable of exploiting specialized knowledge in complex, real-world problem-solving.

Cited by 0Open access