Inherited Properties of $$\mathcal {FL}_0$$ Concept Similarity Measure Under Preference Profile

This paper proposes an algorithm for document plagiarism detection using the provided incremental knowledge construction with formal concept analysis (FCA). The incremental knowledge construction is presented to support document matching between the source document in storage and the suspect document. Thus, a new concept similarity measure is also proposed for retrieving formal concepts in the knowledge construction. The presented concept similarity employs appearance frequencies in the obtained knowledge construction. Our approach can be applied to retrieve relevant information because the obtained structure uses FCA in concept form that is definable by a conjunction of properties. This measure is mathematically proven to be a formal similarity metric. The performance of the proposed similarity measure is demonstrated in document plagiarism detection. Moreover, this paper provides an algorithm to build the information structure for document plagiarism detection. Thai text test collections are used for performance evaluation of the implemented web application.

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simπ: A Concept Similarity Measure under an Agent’s Preferences in Description Logic ELH

Proceedings of the 8th International Conference on Agents and Artificial Intelligence ◽

10.5220/0005813404800487 ◽

2016 ◽

Cited By ~ 6

Author(s):

Teeradaj Racharak ◽

Boontawee Suntisrivaraporn ◽

Satoshi Tojo

Keyword(s):

Similarity Measure ◽

Description Logic ◽

Concept Similarity

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Evaluating Semantic Metrics on Tasks of Concept Similarity

Cross-Disciplinary Advances in Applied Natural Language Processing ◽

10.4018/978-1-61350-447-5.ch021 ◽

2012 ◽

pp. 324-340 ◽

Cited By ~ 1

Author(s):

Hansen A. Schwartz ◽

Fernando Gomez

Keyword(s):

Information Content ◽

Similarity Measure ◽

Similarity Measures ◽

Major Conclusion ◽

Single Measure ◽

Word Similarity ◽

The Difference ◽

Word Senses ◽

Concept Similarity ◽

Semantic Metrics

In this study, first, concept similarity measures are evaluated over human judgments by using existing sets of word similarity pairs that we annotated with word senses. Next, an application-oriented study is presented to evaluate semantic metrics based on integration into an algorithm, first focused on the task of concept similarity then on the task of concept relatedness. The results found no single measure to be most significantly correlated with human-judgments, while an information content-based measure clearly lead to the best results in the application-oriented task of concept similarity. Reinforcing the difference between tasks of concept similarity and concept relatedness, the best measure for an application-oriented task of concept relatedness was a gloss-based relatedness measure rather than a similarity measure. A major conclusion of this work is that similarity measures may perform differently if embedded in specific applications than if they are compared with human judgments.

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Handling WSD using Hierarchical Clustering Algorithm with sentences

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset1841120 ◽

2018 ◽

pp. 83-88

Author(s):

Mohana Priya K ◽

Pooja Ragavi S ◽

Krishna Priya G

Keyword(s):

Hierarchical Clustering ◽

Similarity Measure ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Cosine Similarity Measure ◽

Hierarchical Clustering Algorithm ◽

Multiple Levels ◽

Pos Tagger ◽

Sentence Clustering ◽

The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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