Semantic Similarity Approach Research Articles

SUMMARIZATION USING NTC APPROACH BASED ON KEYWORD EXTRACTION FOR DISCUSSION FORUMS

Internet has become a ubiquitous medium of communication, be it through any social networking websites like Facebook, Twitter or any discussion forums like Yahoo Answers, Quora, Stack Overflow. One can participate in any kind of discussion ranging from politics, education, spirituality, philosophy, science and geography to medicine and many more. Often, most of the discussion forums are loaded up with data. Hence, when a new user wants to know the public opinion, it is impossible for him/her to go through all the tens or hundreds of threads or comments under a particular thematic discussion. The problem here is - we are buried in data but we starve for information. So, to solve this problem, we are proposing a novel approach called Discussion Summarization which is aimed at presenting the user with the most relevant summary containing all the important points of the discussion. This allows the user to easily and quickly grasp and catch up on the on-going conversation in a discussion thread. The summary generated follows CRS approach (Clustering and Ranking and Score calculation for each sentence).The Cluster based Summarization technique is coupled with Nested Thematic Clustering (NTC) and Corpus Based Semantic Similarity (CBSS) approaches. The summary produced is the set of top-ranked sentences (of high scores). Results have shown that a completely unbiased summary with the multidimensionality of comments is generated.

Heuristic Inventive Design Problem Solving Based on Semantic Relatedness

A growing number of industries feel the need of formalizing their innovation approaches. Modern innovation theories and methods use different knowledge sources for solving inventive design problems. These sources are generally about similar notions, but the level of detail of their description can be very different. We are interested in finding semantic links among these sources and developing an intelligent way of managing this knowledge, with the goal of assisting the inventive design expert during his activities. This paper explores a short text semantic similarity approach to search potential links among these sources. These links available could facilitate the retrieval for the heuristic solutions of inventive problems for TRIZ users.

A Semantic Similarity Approach for Linking Tweet Messages to Library of Congress Subject Headings using Linked Resources: A Pilot Study

The objective of this study is to propose, implement, and test a framework of assigning relevant Library of Congress (LC) subject headings to tweet messages. In this study, the task of assigning LC headings is considered an automatic classification task that identifies relevant LC subject headings for given tweets. The classification task is conducted in two stages. In the first stage, tweets are clustered so that similar tweets are grouped together. In the second stage, the degree of similarity between a cluster of tweets and LC subject headings is measured by a popular similarity metric, Jaccard Coefficient (JC). In this pilot study, five selected tweet clusters and nine LC subject headings were carefully chosen and used. This pilot study demonstrates a positive result forthe proposed approach of identifying subject headings for tweets. In three cluster cases out of the five, JC selected the most relevant headings as the largest degrees of similarity. For the other two cases, JC was not successful in ranking the most relevant within the top three headings. In the next step, a more sophisticated clustering method will be explored and applied. Also, all possible LC subject headings will be employed to identify LC subjects for tweets in the next steps of this study.

Semantic Similarity Computation Based on Multi-feature Combination using HowNet

Semantic similarity between words is becoming a generic problems for many applications of computational linguistics and artificial intelligence. The difficulty lies in how to develop a computational method that is capable of generating satisfactory results close to how humans perceive. This paper proposes a semantic similarity approach that is based on multi-feature combination. One of the benchmarks is Miller and Charles’ list of 30 noun pairs which had been manually designated similarity measurements. We correlate our experiments with those computed by several other methods. Experiments on Chinese word pairs show that our approach is close to human similarity judgments.

Anticipating industry convergence: semantic analyses vs IPC co-classification analyses of patents

Purpose – The convergence of industries exposes the involved firms to various challenges. In such a setting, a firm's response time becomes key to its future success. Hence, different approaches to anticipating convergence have been developed in the recent past. So far, especially IPC co-classification patent analyses have been successfully applied in different industry settings to anticipate convergence on a broader industry/technology level. Here, the aim is to develop a concept to anticipate convergence even in small samples, simultaneously providing more detailed information on its origin and direction. Design/methodology/approach – The authors assigned 326 US-patents on phytosterols to four different technological fields and measured the semantic similarity of the patents from the different technological fields. Finally, they compared these results to those of an IPC co-classification analysis of the same patent sample. Findings – An increasing semantic similarity of food and pharmaceutical patents and personal care and pharmaceutical patents over time could be regarded as an indicator of convergence. The IPC co-classification analyses proved to be unsuitable for finding evidence for convergence here. Originality/value – Semantic analyses provide the opportunity to analyze convergence processes in greater detail, even if only limited data are available. However, IPC co-classification analyses are still relevant in analyzing large amounts of data. The appropriateness of the semantic similarity approach requires verification, e.g. by applying it to other convergence settings.

Description and Evaluation of Semantic Similarity Measures Approaches

In recent years, semantic similarity measure has a great interest in Semantic Web and Natural Language Processing (NLP). Several similarity measures have been developed, being given the existence of a structured knowledge representation offered by ontologies and corpus which enable semantic interpretation of terms. Semantic similarity measures compute the similarity between concepts/terms included in knowledge sources in order to perform estimations. This paper discusses the existing semantic similarity methods based on structure, information content and feature approaches. Additionally, we present a critical evaluation of several categories of semantic similarity approaches based on two standard benchmarks. The aim of this paper is to give an efficient evaluation of all these measures which help researcher and practitioners to select the measure that best fit for their requirements.

QUANTIFYING NEARNESS IN VISUAL SPACES

Cybernetic vision systems can be deployed in problem domains where the goal is to achieve results similar to those produced by humans. Fundamentally, these problems consist of evaluation of image content between sets of images. This article contrasts two theoretical frameworks for image comparison, namely, the semantic similarity approach used in the earth mover's distance (EMD) and the integrated region matching (IRM) similarity measure, with the tolerance nearness measure (tNM) based on near set theory. The contribution of this article is a comparison of the image similarity measures EMD, IRM, and tNM, as well as a signature-based approach to calculating the tolerance nearness measure.

Spam filtering using semantic similarity approach and adaptive BPNN

This paper proposes a novel approach for spam filtering based on various semantic similarity measures and an adaptive back propagation neural network (ABPNN). Semantic similarity approach is a promising avenue that addresses the problems for keyword based spam filtering model. In this paper, we propose a new method that integrates three kinds of semantic similarity approaches for spam filtering as a case study of data mining application. First, to construct a latent semantic feature space from training data with a statistical method. Second, to build a corpus based thesaurus by extracting the relationship between words based on its co-occurrence in the documents. Third, to combine the latent semantic feature space with the corpus based thesaurus. Back propagation neural network is one of the efficient approaches for classification. However, the traditional BPNN has the problems of slow learning and easy to trap into a local minimum. In this paper, we adopt an adaptive algorithm to improve the traditional BPNN that can overcome these problems. To investigate the effectiveness of our methods, we conduct extensive experiments on ling-spam, PU1 and PU3 data sets. Experimental results show that the proposed system is able to achieve higher performance, especially for the combination of the hybrid semantic similarity approach and the adaptive back propagation neural network.

A semantic similarity approach to predicting Library of Congress subject headings for social tags

Social tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by soc...

Combined measurement approach for semantic similarity of terms

Measuring semantic similarities of terms is a key issue in many research fields.This paper proposed a method based on the Directed Acyclic Graphs (DAG) of terms and the intrinsic information content of terms to measure the semantic similarities of terms.It first calculated the sub-graphs of two terms based on the directed acyclic graph,and then calculated the intersection and union of the sub-graphs.The semantic similarity of two terms is the ratio of the total intrinsic information content of terms in the intersection to the total intrinsic information content of terms in the union.The experimental results show that the method has a higher degree of accuracy.

A semantic similarity approach to predicting Library of Congress subject headings for social tags

AbstractSocial tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by social tags poses a new challenge for social tag‐based information organization and retrieval. A plausible approach for this challenge is linking social tags to a controlled vocabulary. As an introductory step for this approach, this study investigates ways of predicting relevant subject headings for resources from social tags assigned to the resources. The prediction of subject headings was measured by five different similarity measures: tf–idf, cosine‐based similarity (CoS), Jaccard similarity (or Jaccard coefficient; JS), Mutual information (MI), and information radius (IRad). Their results were compared to those by professionals. The results show that a CoS measure based on top five social tags was most effective. Inclusions of more social tags only aggravate the performance. The performance of JS is comparable to the performance of CoS while tf–idf is comparable with up to 70% less than the best performance. MI and IRad have inferior performance compared to the other methods. This study demonstrates the application of the similarity measuring techniques to the prediction of correct Library of Congress subject headings.

An insight into semantic similarity aspects using WordNet

Semantic similarity is becoming a generic issue in variety of applications in areas of information retrieval, computational linguistic and AI, both in the academia and industry. Examples include: computing semantic similarity, word sense disambiguation, text segmentation, multimodal document retrieval, image retrieval, etc. However, semantic similarity measures have been used showing mixed chances of success. The basic problem is that if semantic measures are used bluntly without understanding, they might decrease retrieval efficiency. There is a need to investigate semantic similarity approaches in order to have better understanding of these approaches. Several semantic methods for determining semantic similarity between terms have been proposed in the literature and most of them have been tested on WordNet. In this paper, we investigate the approaches to compute semantic similarity by mapping word concepts to WordNet ontology and by examining their relationship in that ontology. The paper then provides specific examples for explaining these approaches Further, the paper categorises and compares various approaches for measuring semantic similarity using WordNet ontology.

Quantitative assessment of relationship between sequence similarity and function similarity

BackgroundComparative sequence analysis is considered as the first step towards annotating new proteins in genome annotation. However, sequence comparison may lead to creation and propagation of function assignment errors. Thus, it is important to perform a thorough analysis for the quality of sequence-based function assignment using large-scale data in a systematic way.ResultsWe present an analysis of the relationship between sequence similarity and function similarity for the proteins in four model organisms, i.e., Arabidopsis thaliana, Saccharomyces cerevisiae, Caenorrhabditis elegans, and Drosophila melanogaster. Using a measure of functional similarity based on the three categories of Gene Ontology (GO) classifications (biological process, molecular function, and cellular component), we quantified the correlation between functional similarity and sequence similarity measured by sequence identity or statistical significance of the alignment and compared such a correlation against randomly chosen protein pairs.ConclusionVarious sequence-function relationships were identified from BLAST versus PSI-BLAST, sequence identity versus Expectation Value, GO indices versus semantic similarity approaches, and within genome versus between genome comparisons, for the three GO categories. Our study provides a benchmark to estimate the confidence in assignment of functions purely based on sequence similarity.

Incorporating Ontology-Driven Similarity Knowledge into Functional Genomics: An Exploratory Study.

This research explores the feasibility of semantic similarity approaches to supporting predictive tasks in functional genomics. It aims to establish potential relationships between ontology-based similarity of gene products and important functional properties, such as gene expression correlation. Similarity measures based on the information content of the Gene Ontology (GO) were analyzed. Models have been implemented using data obtained from well-known studies in S. cerevisiae. Results suggest that there may exist significant relationships between gene expression correlation and semantic similarity. Analyses of protein complex data show that, in general, there is a significant correlation between the semantic similarity exhibited by a pair of genes and the probability of finding them in the same complex. These results can also be interpreted as an assessment of the quality and consistency of the information represented in the GO.