Extracting Domain-specific Concepts from Large-scale Linked Open Data
We propose a methodology for extracting concepts for a target domain from large-scale linked open data (LOD) to support the construction of domain ontologies providing field-specific knowledge and definitions. The proposed method defines search entities by linking the LOD vocabulary with technical terms related to the target domain. The search entities are then used as a starting point for obtaining upper-level concepts in the LOD, and 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. A technical dictionary index and natural language processing are used to evaluate whether the extracted concepts cover the domain. As an example of extracting a class hierarchy from LOD, we used Wikidata to construct a domain ontology for polymer materials and physical properties. The proposed method can be applied to general datasets with class hierarchies, and it allows ontology developers to create an initial model of the domain ontology for their own purposes.
Code (1)
Similar Papers 제목 키워드 기반
Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learn…
General ClassificationInformation RetrievalRetrievalMulti-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation
Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn …
Deep LearningCHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature
A hallmark of human innovation is the process of recombination -- creating original ideas by integrating elements of existing mechanisms and concepts. In this work, we automatically mine the scientific literature and bui…
ConExion: Concept Extraction with Large Language Models
In this paper, an approach for concept extraction from documents using pre-trained large language models (LLMs) is presented. Compared with conventional methods that extract keyphrases summarizing the important informati…
NLP-AKG: Few-Shot Construction of NLP Academic Knowledge Graph Based on LLM
Large language models (LLMs) have been widely applied in question answering over scientific research papers. To enhance the professionalism and accuracy of responses, many studies employ external knowledge augmentation. …
graph constructionQuestion Answering