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Deep graph similarity learning: a survey
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.
Journal Article
SOME SIMILARITY MEASURES FOR PICTURE FUZZY SETS AND THEIR APPLICATIONS
In this work, we shall present some novel process to measure the similarity between picture fuzzy sets. Firstly, we adopt the concept of intuitionistic fuzzy sets, interval-valued intuitionistic fuzzy sets and picture fuzzy sets. Secondly, we develop some similarity measures between picture fuzzy sets, such as, cosine similarity measure, weighted cosine similarity measure, set-theoretic similarity measure, weighted set-theoretic cosine similarity measure, grey similarity measure and weighted grey similarity measure. Then, we apply these similarity measures between picture fuzzy sets to building material recognition and minerals field recognition. Finally, two illustrative examples are given to demonstrate the efficiency of the similarity measures for building material recognition and minerals field recognition.
Journal Article
Sorting with snakes
Offers lessons in sorting and similarities along with information on snakes.
Book
An overview of word and sense similarity
Over the last two decades, determining the similarity between words as well as between their meanings, that is, word senses, has been proven to be of vital importance in the field of Natural Language Processing. This paper provides the reader with an introduction to the tasks of computing word and sense similarity. These consist in computing the degree of semantic likeness between words and senses, respectively. First, we distinguish between two major approaches: the knowledge-based approaches and the distributional approaches. Second, we detail the representations and measures employed for computing similarity. We then illustrate the evaluation settings available in the literature and, finally, discuss suggestions for future research.
Journal Article
A study of similarity measures through the paradigm of measurement theory: the fuzzy case
We extend to fuzzy similarity measures the study made for classical ones in a companion paper (Coletti and Bouchon-Meunier in Soft Comput 23:6827–6845, 2019). Using a classic method of measurement theory introduced by Tversky, we establish necessary and sufficient conditions for the existence of a particular class of fuzzy similarity measures, representing a binary relation among pairs of objects which expresses the idea of “no more similar than”. In this fuzzy context, the axioms are strictly dependent on the combination operators chosen to compute the union and the intersection.
Journal Article
Using similarity network analysis to improve text similarity calculations
Similarity-based analysis is a powerful and intuitive tool for exploring large data sets, for instance, for revealing patterns by grouping items by similarity or for recommending items based on selected samples. However, similarity is an abstract and subjective property which makes it hard to evaluate by a purely computational approach. Furthermore, there are usually several possible computational models that could be applied to the data, each with its own strengths and weaknesses. With this in mind, we aim to extend the research frontier regarding what impact the choice of a computational model may have on the results. In this paper, we target the scope of embedding-based similarity calculations on text documents and seek to answer the research question: “How can a better understanding of the continuous similarity distribution captured by different models lead to better similarity calculations on document sets?”. We propose a new and generic methodology based on similarity network comparison, and based on this approach, we have developed a computational pipeline together with a prototype visual analytics tool that allows the user to easily assess the level of model agreement/disagreement. To demonstrate the potential of our method, as well as showing its application to real world scenarios, we apply it in an experimental setup using three state-of-the-art text embedding models and three different text corpora. In view of the surprisingly low level of model agreement regarding the data, we also discuss strategies for handling model disagreement.
Journal Article
Correction: Dynamic Similarity in Titanosaur Sauropods: Ichnological Evidence from the Fumanya Dinosaur Tracksite (Southern Pyrenees)
[This corrects the article DOI: 10.1371/journal.pone.0057408.].
Journal Article