Reorganizing Educational Institutional Domain using Faceted Ontological
Principles
- URL: http://arxiv.org/abs/2306.10300v1
- Date: Sat, 17 Jun 2023 09:06:07 GMT
- Title: Reorganizing Educational Institutional Domain using Faceted Ontological
Principles
- Authors: Subhashis Das, Debashis Naskar and Sayon Roy
- Abstract summary: This work is to find out how different library classification systems and linguistic techniques arrange a particular domain of interest.
We use knowledge representation and languages for a specific domain specific ontology.
This construction would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The purpose of this work is to find out how different library classification
systems and linguistic ontologies arrange a particular domain of interest and
what are the limitations for information retrieval. We use knowledge
representation techniques and languages for construction of a domain specific
ontology. This ontology would help not only in problem solving, but it would
demonstrate the ease with which complex queries can be handled using principles
of domain ontology, thereby facilitating better information retrieval.
Related papers
- Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
Ontologies are widely used for representing domain knowledge and meta data.
One straightforward solution is to integrate statistical analysis and machine learning.
Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
arXiv Detail & Related papers (2024-06-16T14:49:19Z) - Towards Complex Ontology Alignment using Large Language Models [1.3218260503808055]
Ontology alignment is a critical process in Web for detecting relationships between different labels and content.
Recent advancements in Large Language Models (LLMs) presents new opportunities for enhancing engineering practices.
This paper investigates the application of LLM technologies to tackle the complex alignment challenge.
arXiv Detail & Related papers (2024-04-16T07:13:22Z) - Domain Prompt Learning with Quaternion Networks [49.45309818782329]
We propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability of Vision-Language Models to specialized domains.
We present a hierarchical approach that generates vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features.
Our proposed method achieves new state-of-the-art results in prompt learning.
arXiv Detail & Related papers (2023-12-12T08:49:39Z) - DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain
Question Answering over Knowledge Base and Text [73.68051228972024]
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge.
Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
arXiv Detail & Related papers (2023-10-31T04:37:57Z) - DiscoverPath: A Knowledge Refinement and Retrieval System for
Interdisciplinarity on Biomedical Research [96.10765714077208]
Traditional keyword-based search engines fall short in assisting users who may not be familiar with specific terminologies.
We present a knowledge graph-based paper search engine for biomedical research to enhance the user experience.
The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG.
arXiv Detail & Related papers (2023-09-04T20:52:33Z) - Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [100.24095818099522]
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP)
They provide a highly useful, task-agnostic foundation for a wide range of applications.
However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles.
arXiv Detail & Related papers (2023-05-30T03:00:30Z) - Computing Rule-Based Explanations of Machine Learning Classifiers using
Knowledge Graphs [62.997667081978825]
We use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier.
In particular, we introduce a novel method for extracting and representing black-box explanations of its operation, in the form of first-order logic rules expressed in the terminology of the knowledge graph.
arXiv Detail & Related papers (2022-02-08T16:21:49Z) - Extracting Domain-specific Concepts from Large-scale Linked Open Data [0.0]
The proposed method defines search entities by linking the LOD vocabulary with terms related to the target domain.
The occurrences of common upper-level entities and the chain-of-path relationships are examined to determine the range of conceptual connections in the target domain.
arXiv Detail & Related papers (2021-11-22T10:25:57Z) - On the Complexity of Learning Description Logic Ontologies [14.650545418986058]
Ontologies are a popular way of representing domain knowledge, in particular, knowledge in domains related to life sciences.
We provide a formal specification of the exact and the probably correct learning models from learning theory.
arXiv Detail & Related papers (2021-03-25T09:18:12Z) - Topological Deep Learning: Classification Neural Networks [0.913755431537592]
Topological deep learning is a formalism that is aimed at introducing topological language to deep learning.
We show when the classification problem is possible or not possible in the context of neural networks.
arXiv Detail & Related papers (2021-02-16T18:41:09Z) - Natural language technology and query expansion: issues,
state-of-the-art and perspectives [0.0]
Linguistic characteristics that cause ambiguity and misinterpretation of queries as well as additional factors affect the users ability to accurately represent their information needs.
We lay down the anatomy of a generic linguistic based query expansion framework and propose its module-based decomposition.
For each of the modules we review the state-of-the-art solutions in the literature and categorized under the light of the techniques used.
arXiv Detail & Related papers (2020-04-23T11:39:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.