Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

Download Full-text

Automatic Ontology Learning from Multiple Knowledge Sources of Text

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2018040101 ◽

2018 ◽

Vol 14 (2) ◽

pp. 1-21 ◽

Cited By ~ 2

Author(s):

B Sathiya ◽

T.V. Geetha

Keyword(s):

Knowledge Base ◽

Web Pages ◽

Knowledge Sources ◽

Statistical Measure ◽

Ontology Learning ◽

Web Page ◽

Semantic Query ◽

Probabilistic Knowledge ◽

Discovery Algorithms ◽

Different Sources

The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

Download Full-text

Event-Based Knowledge Acquisition for Ontology Learning

6th IEEE International Conference on Cognitive Informatics ◽

10.1109/coginf.2007.4341929 ◽

2007 ◽

Cited By ~ 3

Author(s):

Zhou Wen ◽

Liu Zongtian ◽

Liu Ying ◽

Zhao Yan

Keyword(s):

Knowledge Acquisition ◽

Ontology Learning ◽

Event Based

Download Full-text

Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2010.195 ◽

2011 ◽

Vol 23 (11) ◽

pp. 1635-1648 ◽

Cited By ~ 18

Author(s):

Elias Zavitsanos ◽

George Paliouras ◽

George A. Vouros

Keyword(s):

Gold Standard ◽

Ontology Learning ◽

Learning Methods

Download Full-text

Ontology learning with complex data type for Web service clustering

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) ◽

10.1109/cidm.2014.7008658 ◽

2014 ◽

Cited By ~ 1

Author(s):

Banage T. G. S. Kumara ◽

Incheon Paik ◽

Kowatte R. C. Koswatte ◽

Wuhui Chen

Keyword(s):

Web Service ◽

Data Type ◽

Complex Data ◽

Ontology Learning ◽

Service Clustering

Download Full-text

Method for automatic key concepts extraction

The Electronic Library ◽

10.1108/el-01-2018-0012 ◽

2019 ◽

Vol 37 (1) ◽

pp. 2-15 ◽

Cited By ~ 2

Author(s):

Sudarsana Desul ◽

Madurai Meenachi N. ◽

Thejas Venkatesh ◽

Vijitha Gunta ◽

Gowtham R. ◽

...

Keyword(s):

Coming Out ◽

English Language ◽

Hybrid Approach ◽

Ontology Learning ◽

Specific Domain ◽

Content Type ◽

Concept Extraction ◽

Nuclear Domain ◽

Wide Range ◽

Key Concepts

PurposeOntology of a domain mainly consists of a set of concepts and their semantic relations. It is typically constructed and maintained by using ontology editors with substantial human intervention. It is desirable to perform the task automatically, which has led to the development of ontology learning techniques. One of the main challenges of ontology learning from the text is to identify key concepts from the documents. A wide range of techniques for key concept extraction have been proposed but are having the limitations of low accuracy, poor performance, not so flexible and applicability to a specific domain. The propose of this study is to explore a new method to extract key concepts and to apply them to literature in the nuclear domain.Design/methodology/approachIn this article, a novel method for key concept extraction is proposed and applied to the documents from the nuclear domain. A hybrid approach was used, which includes a combination of domain, syntactic name entity knowledge and statistical based methods. The performance of the developed method has been evaluated from the data obtained using two out of three voting logic from three domain experts by using 120 documents retrieved from SCOPUS database.FindingsThe work reported pertains to extracting concepts from the set of selected documents and aids the search for documents relating to given concepts. The results of a case study indicated that the method developed has demonstrated better metrics than Text2Onto and CFinder. The method described has the capability of extracting valid key concepts from a set of candidates with long phrases.Research limitations/implicationsThe present study is restricted to literature coming out in the English language and applied to the documents from nuclear domain. It has the potential to extend to other domains also.Practical implicationsThe work carried out in the current study has the potential of leading to updating International Nuclear Information System thesaurus for ontology in the nuclear domain. This can lead to efficient search methods.Originality/valueThis work is the first attempt to automatically extract key concepts from the nuclear documents. The proposed approach will address and fix the most of the problems that are existed in the current methods and thereby increase the performance.

Download Full-text