Related Similarity Measures Research Articles

A Vector Space Approach for Measuring Relationality and Multidimensionality of Meaning in Large Text Collections

In this article, we develop a methodological approach for organizational research regarding the construction of multidimensional and relational similarity measures by using the vector space model in natural language processing (NLP). Our vector space approach draws on the well-established premise in organizational research that texts provide a window into social reality and allow measuring theory-based constructs ( e.g., organizations’ self-representations). Using a vector space approach allows capturing the multidimensionality of these theory-based constructs and computing relational similarities between organizational entities ( e.g., organizations, their members, and subunits) in social spaces and with their environments, such as the organization itself, industries, or countries. Thus, our methodological approach contributes to the recent trend in organizational research to use the potential inherent in big (textual) data by using NLP. In an example, we provide guidance for organizational scholars by illustrating how they can ensure validity when applying our methodological contribution in concrete research practice.

Two-mode relational similarities

In a previous paper, Kovacs (2010) proposed a generalized relational similarity measure based on iterated correlations of entities in a network calibrated by their relational similarity to other entities. Here I show that, in the case of two-mode network data, Kovacs’s approach can be simplified and generalized similarities calculated non-iteratively. The basic idea is to rely on initial similarities calculated from transforming the two-mode data into one-mode projections using the familiar duality approach due to Breiger (1974). I refer to this as two-mode relational similarities and show, using the Southern Women’s data and data from Senate voting in the 112th U.S. Congress, that it yields results substantively indistinguishable from Kovacs’s iterative strategy.

Mobile user identification based on road networks and multiple distance measures

AbstractMobile user identification aims at matching different mobile devices of the same user using trajectory data, which has attracted extensive research in recent years. Most of the previous work extracted trajectory features based on regular grids, which will lead to incorrect feature representation due to lack of geographic information. Besides, most trajectory similarity models only considered one single distance measure to calculate the similarity between users, which ignore the connection between different distance measures and may lead to some false matches. In light of this, we present a novel user identification method based on road networks and multiple distance measures in this article. The proposed method segments a city map into several grids and road segments based on road networks. Then it extracts location and road information of trajectories to jointly construct user features. Multiple distance measures are fused by a discriminant model to improve the effect of user identification. Experiments on real GPS trajectory datasets show that our proposed method outperforms related similarity measure methods and is stable for mobile user identification. Meanwhile, our method can also achieve good identification results even on sparse trajectory datasets.

Hierarchical few-shot learning with feature fusion driven by data and knowledge

Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance.

Simulation evaluation of small samples based on grey estimation and improved bootstrap

PurposeTo solve the problem of simulation evaluation with small samples, a fresh approach of grey estimation is presented based on classical statistical theory and grey system theory. The purpose of this paper is to make full use of the difference of data distribution and avoid the marginal data being ignored.Design/methodology/approachBased upon the grey distribution characteristics of small sample data, the definition about a new concept of grey relational similarity measure comes into being. At the same time, the concept of sample weight is proposed according to the grey relational similarity measure. Based on the new definition of grey weight, the grey point estimation and grey confidence interval are studied. Then the improved Bootstrap resampling is designed by uniform distribution and randomness as an important supplement of the grey estimation. In addition, the accuracy of grey bilateral and unilateral confidence intervals is introduced by using the new grey relational similarity measure approach.FindingsThe new small sample evaluation method can realize the effective expansion and enrichment of data and avoid the excessive concentration of data. This method is an organic fusion of grey estimation and improved Bootstrap method. Several examples are used to demonstrate the feasibility and validity of the proposed methods to illustrate the credibility of some simulation data, which has no need to know the probability distribution of small samples.Originality/valueThis research has completed the combination of grey estimation and improved Bootstrap, which makes more reasonable use of the value of different data than the unimproved method.

An attribute‐diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures

AbstractBased on the framework of attribute diversity (a generalization of Hill numbers of order q), we develop a class of functional diversity measures sensitive not only to species abundances but also to trait‐based species‐pairwise functional distances. The new method refines and improves on the conventional species‐equivalent approach in three areas: (1) the conventional method often gives similar values (close to unity) to assemblages with contrasting levels of functional diversity; (2) when a distance metric is unbounded, the conventional functional diversity depends on the presence/absence of other assemblages in the study; (3) in partitioning functional gamma diversity into alpha and beta components, the conventional gamma is sometimes less than alpha. To resolve these issues, we add to the attribute‐diversity framework a novel concept: τ, the threshold of functional distinctiveness between any two species; here, τ can be chosen to be any positive value. Any two species with functional distance ≥ τ are treated as functionally equally distinct. Our functional diversity quantifies the effective number of functionally equally distinct species (or “virtual functional groups”) with all pairwise distances at least τ for different species pairs. We advocate the use of two complementary diversity profiles (τ profile and q profile), which depict functional diversity with varying levels of τ and q, respectively. Both the conventional species‐equivalent method (i.e., τ is the maximum of species‐pairwise distances) and classic taxonomic diversity (i.e., τ is the minimum of non‐zero species‐pairwise distances) are incorporated into our proposed τ profile for an assemblage. For any type of species‐pairwise distance matrices, our attribute‐diversity approach allows proper diversity partitioning, with the desired property gamma ≥ alpha and thus avoids all the restrictions that apply to the conventional diversity decomposition. Our functional alpha and gamma are interpreted as the effective numbers of functionally equally distinct species, respectively, in an assemblage and in the pooled assemblage, while beta is the effective number of equally large assemblages with no shared species and all species in the assemblages being equally distinct. The resulting beta diversity can be transformed to obtain abundance‐sensitive Sørensen‐ and Jaccard‐type functional (dis)similarity profiles. Hypothetical and real examples are used to illustrate the framework. Online software and R codes are available to facilitate computations.

Multi level object relational similarity based image mining for improved image search using semantic ontology

The problem of image mining has been well studied in literatures and there exist number of approaches towards this issue. But, the most approaches consider only the spatial features rather than the meaning of the images. This deviates the modern image search from the search engines, because the users look for more meaningful results from the search engines. To support the search engines to improve their results, a multi level object relational similarity measure based image retrieval algorithm is presented in this paper. The method works in two levels, first the manifolds are trained by extracting the objects and assisting the labeling with supervised learning process. For each class of images, multi level semantics are maintained which consists of object features and their semantic meanings. At the test phase, the method computes multi level object relational similarity (MORS) measure for each semantic class. Based on the MORS value, a single semantic class has been identified. Based on identified semantic class, the result has been populated to the user. The proposed method improves the performance of image mining and improves the performance of image retrieval.

Significance and Functional Similarity for Identification of Disease Genes.

One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets.

A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System

The rapid development of e-learning systems provides learners with great opportunities to access learning activities online, and this greatly supports and enhances the learning practices. However, an issue reduces the success of application of e-learning systems; too many learning activities (such as various leaning materials, subjects, and learning resources) are emerging in an e-learning system, making it difficult for individual learners to select proper activities for their particular situations/requirements because there is no personalized service function. Recommender systems, which aim to provide personalized recommendations for products or services, can be used to solve this issue. However, e-learning systems need to be able to handle certain special requirements: 1) leaning activities and learners’ profiles often present tree structures; 2) learning activities contain vague and uncertain data, such as the uncertain categories that the learning activities belong to; 3) there are pedagogical issues, such as the precedence relations between learning activities. To deal with the three requirements, this study first proposes a fuzzy tree-structured learning activity model, and a learner profile model to comprehensively describe the complex learning activities and learner profiles. In the two models, fuzzy category trees and related similarity measures are presented to infer the semantic relations between learning activities or learner requirements. Since it is impossible to have two completely same trees, in practice, a fuzzy tree matching method is carefully discussed. A fuzzy tree matching-based hybrid learning activity recommendation approach is then developed. This approach takes advantage of both the knowledge-based and collaborative filtering-based recommendation approaches, and considers both the semantic and collaborative filtering similarities between learners. Finally, an e-learning recommender system prototype is well designed and developed based on the proposed models and recommendation approach. Experiments are done to evaluate the proposed recommendation approach, and the experimental results demonstrate the good accuracy performance of the proposed approach. A comprehensive case study about learning activity recommendation further demonstrates the effectiveness of the fuzzy tree matching-based personalized e-learning recommender system in practice.

Inclusion measure for typical hesitant fuzzy sets, the relative similarity measure and fuzzy entropy

Typical hesitant fuzzy sets (THFSs), possessing a finite-set-valued fuzzy membership degrees called typical hesitant fuzzy elements (THFEs), is a special kind of hesitant fuzzy sets. Fuzzy inclusion relationship, as the order structure in fuzzy mathematics, plays an elementary role in the theoretical research and practical applications of fuzzy sets. In this paper, a new partial order for THFEs is defined via the disjunctive semantic meaning of a set, based on which fuzzy inclusion relationship is defined for THFSs. Furthermore, inclusion measures are defined to present the quantitative ranking of every two THFEs and THFSs and different inclusion measures are constructed. The related similarity measure, distance and fuzzy entropy of THFSs are presented and their relationship with inclusion measures are investigated. Finally, an example is given to show that the inclusion measure can be applied effectively in hesitant fuzzy multi-attribute decision making.

Repetition-free longest common subsequence of random sequences

A repetition-free Longest Common Subsequence (LCS) of two sequences x and y is an LCS of x and y where each symbol may appear at most once. Let R denote the length of a repetition-free LCS of two sequences of n symbols each one chosen randomly, uniformly, and independently over a k-ary alphabet. We study the asymptotic, in n and k, behavior of R and establish that there are three distinct regimes, depending on the relative speed of growth of n and k. For each regime we establish the limiting behavior of R. In fact, we do more, since we actually establish tail bounds for large deviations of R from its limiting behavior.Our study is motivated by the so called exemplar model proposed by Sankoff (1999) and the related similarity measure introduced by Adi et al. (2010). A natural question that arises in this context, which as we show is related to long standing open problems in the area of probabilistic combinatorics, is to understand the asymptotic, in n and k, behavior of parameter R.

The Analysis of Similarity Measure Function in Image Matching Algorithms

Similarity measure function is one of the most important factors influencing the matching precision in the field of computer vision. In this paper, a survey is done on the application frequency of distance similarity measure methods and related similarity measure methods, also the statistic characteristic is been given. The significance of Measure functions variable parameters in image matching is showed. In the real time processing aspect, drawn the conclusion that Manhattan distance measure is the fastest, Euclidean distance take second place, correlation coefficient is worst. However, in the robustness of the noise pollution aspect, correlation coefficient has the strongest robustness, then followed is Manhattan distance, Euclidean distance is worst.

Semantic similarity measures in the biomedical domain by leveraging a web search engine.

Various researches in web related semantic similarity measures have been deployed. However, measuring semantic similarity between two terms remains a challenging task. The traditional ontology-based methodologies have a limitation that both concepts must be resided in the same ontology tree(s). Unfortunately, in practice, the assumption is not always applicable. On the other hand, if the corpus is sufficiently adequate, the corpus-based methodologies can overcome the limitation. Now, the web is a continuous and enormous growth corpus. Therefore, a method of estimating semantic similarity is proposed via exploiting the page counts of two biomedical concepts returned by Google AJAX web search engine. The features are extracted as the co-occurrence patterns of two given terms P and Q, by querying P, Q, as well as P AND Q, and the web search hit counts of the defined lexico-syntactic patterns. These similarity scores of different patterns are evaluated, by adapting support vector machines for classification, to leverage the robustness of semantic similarity measures. Experimental results validating against two datasets: dataset 1 provided by A. Hliaoutakis; dataset 2 provided by T. Pedersen, are presented and discussed. In dataset 1, the proposed approach achieves the best correlation coefficient (0.802) under SNOMED-CT. In dataset 2, the proposed method obtains the best correlation coefficient (SNOMED-CT: 0.705; MeSH: 0.723) with physician scores comparing with measures of other methods. However, the correlation coefficients (SNOMED-CT: 0.496; MeSH: 0.539) with coder scores received opposite outcomes. In conclusion, the semantic similarity findings of the proposed method are close to those of physicians' ratings. Furthermore, the study provides a cornerstone investigation for extracting fully relevant information from digitizing, free-text medical records in the National Taiwan University Hospital database.

SemCiR

PurposeThe purpose of this paper is to propose a novel citation recommendation system that inputs a text and recommends publications that should be cited by it. Its goal is to help researchers in finding related works. Further, this paper seeks to explore the effect of using relational features in addition to textual features on the quality of recommended citations.Design/methodology/approachIn order to propose a novel citation recommendation system, first a new relational similarity measure is proposed for calculating the relatedness of two publications. Then, a recommendation algorithm is presented that uses both relational and textual features to compute the semantic distances of publications of a bibliographic dataset from the input text.FindingsThe evaluation of the proposed system shows that combining relational features with textual features leads to better recommendations, in comparison with relying only on the textual features. It also demonstrates that citation context plays an important role among textual features. In addition, it is concluded that different relational features have different contributions to the proposed similarity measure.Originality/valueA new citation recommendation system is proposed which uses a novel semantic distance measure. This measure is based on textual similarities and a new relational similarity concept. The other contribution of this paper is that it sheds more light on the importance of citation context in citation recommendation, by providing more evidences through analysis of the results. In addition, a genetic algorithm is developed for assigning weights to the relational features in the similarity measure.

Relational Similarity Measure: An Approach Combining Wikipedia and WordNet

Relational similarities between two pairs of words are the degrees of their semantic relations. Vector Space Model (VSM) is used to measure the relational similarity between two pairs of words, however it needs create patterns manually and these patterns are limited. Recently, Latent Relational Analysis (LRA) is proposed and achieves state-of-art results. However, it is time-consuming and cannot express implicit semantic relations. In this study, we propose a new approach to measure relational similarities between two pairs of words by combining Wordnet3.0 and the Web-Wikipedia, thus implicit semantic relations from the very large corpus can be mined. The proposed approach mainly possesses two characters: (1) A new method is proposed in the pattern extraction step, which considers various part-of-speeches of words. (2)Wordnet3.0 is applied to calculate the semantic relatedness between a pair of words so that the implicit semantic relation of the two words can be expressed. Experimental evaluation based on the 374 SAT multiple-choice word-analogy questions, the precision of the proposed approach is 43.9%, which is lower than that of LRA suggested by Turney in 2005, but the suggested approach mainly focuses on mining the semantic relations among words.

Recommending friends and locations based on individual location history

The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply, to some extent, users' interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction and report on a personalized friend and location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system, a particular individual's visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories and recommend to each user a group of potential friends in a GIS community. Third, we estimate an individual's interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchical-graph-based similarity measurement (HGSM), is proposed to uniformly model each individual's location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people's outdoor movements, 2) the visited popularity of a geospatial region, and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate the rating of a user on an item. We evaluated this recommender system based on the GPS data collected by 75 subjects over a period of 1 year in the real world. As a result, HGSM outperforms related similarity measures, namely similarity-by-count, cosine similarity, and Pearson similarity measures. Moreover, beyond the item-based CF method and random recommendations, our system provides users with more attractive locations and better user experiences of recommendation.

A Supervised Classification Approach for Measuring Relational Similarity between Word Pairs

Measuring the relational similarity between word pairs is important in numerous natural language processing tasks such as solving word analogy questions, classifying noun-modifier relations and disambiguating word senses. We propose a supervised classification method to measure the similarity between semantic relations that exist between words in two word pairs. First, each pair of words is represented by a vector of automatically extracted lexical patterns. Then a binary Support Vector Machine is trained to recognize word pairs with similar semantic relations to a given word pair. To train and evaluate the proposed method, we use a benchmark dataset that contains 374 SAT multiple-choice word-analogy questions. To represent the relations that exist between two word pairs, we experiment with 11 different feature functions, including both symmetric and asymmetric feature functions. Our experimental results show that the proposed method outperforms several previously proposed relational similarity measures on this benchmark dataset, achieving an SAT score of 46.9.

Some Similarity Measures of Interval-Valued Intuitionistic Fuzzy Sets and Application to Pattern Recognition

In this paper, we present an effective method for evaluating some similarity measures of interval-valued intuitionistic fuzzy sets(IvIFSs) on the basis of Hausdorff metric. Some properties of the proposed similarity measures are discussed and the related similarity measures are also compared. Finally, one numerical example is given to demonstrate the application of the presented similarity measures of IvIFSs to pattern recognition problem.

QSAR and the ultimate molecular descriptor: the shape of electron density clouds

The advantages of electron density representations of molecules in QSAR studies and the related shape descriptors and shape similarity measures are discussed, with special emphasis on molecules involving pi-bonded systems and aromatic rings.

Graph similarity scoring and matching

We outline a class of graph similarity measures that uses the structural similarity of local neighborhoods to derive pairwise similarity scores for the nodes of two different graphs, and present a related similarity measure that uses a linear update to generate both node and edge similarity scores. This measure is then applied to the task of graph matching.