Developing base domain ontology from a reference collection to aid information retrieval

2019 ◽  
Vol 100 ◽  
pp. 180-189 ◽  
Author(s):  
Nai-Wen Chi ◽  
Yu-Huei Jin ◽  
Shang-Hsien Hsieh
2011 ◽  
Vol 25 (2) ◽  
pp. 288-296 ◽  
Author(s):  
Shang-Hsien Hsieh ◽  
Hsien-Tang Lin ◽  
Nai-Wen Chi ◽  
Kuang-Wu Chou ◽  
Ken-Yu Lin
Keyword(s):  

2022 ◽  
Vol 15 (1) ◽  
pp. 1-13
Author(s):  
David Otero ◽  
Patricia Martin-Rodilla ◽  
Javier Parapar

Social networks constitute a valuable source for documenting heritage constitution processes or obtaining a real-time snapshot of a cultural heritage research topic. Many heritage researchers use social networks as a social thermometer to study these processes, creating, for this purpose, collections that constitute born-digital archives potentially reusable, searchable, and of interest to other researchers or citizens. However, retrieval and archiving techniques used in social networks within heritage studies are still semi-manual, being a time-consuming task and hindering the reproducibility, evaluation, and open-up of the collections created. By combining Information Retrieval strategies with emerging archival techniques, some of these weaknesses can be left behind. Specifically, pooling is a well-known Information Retrieval method to extract a sample of documents from an entire document set (posts in case of social network’s information), obtaining the most complete and unbiased set of relevant documents on a given topic. Using this approach, researchers could create a reference collection while avoiding annotating the entire corpus of documents or posts retrieved. This is especially useful in social media due to the large number of topics treated by the same user or in the same thread or post. We present a platform for applying pooling strategies combined with expert judgment to create cultural heritage reference collections from social networks in a customisable, reproducible, documented, and shareable way. The platform is validated by building a reference collection from a social network about the recent attacks on patrimonial entities motivated by anti-racist protests. This reference collection and the results obtained from its preliminary study are available for use. This real application has allowed us to validate the platform and the pooling strategies for creating reference collections in heritage studies from social networks.


2014 ◽  
Vol 926-930 ◽  
pp. 2263-2266
Author(s):  
Li Juan Diao ◽  
Jun Zhong Gu ◽  
Liang Chun

Ontology definition metamodel has been widely adopted in aspect of building ontology. However existing ontology metamodel is only suitable for building ontology in a certain domain. With collaboration and sharing among multiple domains, we face the seriously problem that is how to overcome semantic interoperability. For this problem, we need to combine general ontology with domain ontology and merge all existing ontologies by ontology metamodel. In this paper, we define main components of ontology metamodel and present conditional context and contextual concept unit. In addition, we introduce the method of mapping between conditional context and contextual concept unit. Finally, we use an example about information retrieval to illustrate its function and analysis its feasibility.


2013 ◽  
Vol 321-324 ◽  
pp. 1951-1956
Author(s):  
Guo Wei Yang ◽  
Min Chen ◽  
Xiao Feng Zhang

The study of Concept Similarity is a very important aspect of Knowledge Representation and Information Retrieval in Artificial Intelligence, and it is also a bottleneck that hasn’t been well solved in the Ontology Research. In this article, we take every influencing factor into account, especially the area density, a new method of concept similarity based-on Domain Ontology is suggested. The experiment results show that: the new method we proposed in this article can more reasonably describe the concept similarity.


2013 ◽  
Vol 433-435 ◽  
pp. 1662-1665
Author(s):  
Huan Hai Yang ◽  
Ming Yu Sun

Considering weakness of the traditional retrieval method based on keyword matching, the paper introduced semantic into information retrieval, and proposed a semantic retrieval model based on ontology. The paper offered a construction method of domain ontology and implemented semantic reasoning using Jena and improved a semantic similarity calculation method.


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