scholarly journals Semantic Similarity Analysis on Knowledge Based and Prediction Based Models

2020 ◽  
Vol 9 (6) ◽  
pp. 477-481
Keyword(s):  
Semantic Information ◽  
Similarity Measures ◽  
Similarity Analysis ◽  
Document Similarity ◽  
Knowledge Based ◽  
Sentence Similarity ◽  
Semantic Models ◽  
Term Similarity ◽  
Proximity Measures ◽  
Semantic Similarity Analysis

The similarity between two synsets or concepts is a numeral measure of the degree to which the two objects are alike or not and the similarity measures say the degree of closeness between two synsets or concepts. The similarity or dissimilarity represented by the term proximity. Proximity measures are defined to have values in the interval [0, 1]. Term Similarity, Sentence similarity and Document similarity are the areas of text similarity. Term similarity measures used to measure the similarity between individual tokens and words, Sentence similarity is the similarity between two or more sentences and Document similarity used to measure the similarity between two or more corpora. This paper is the study between Knowledge based, Distribution based and prediction based semantic models and shows how knowledge based methods capturing information and prediction based methods preserving semantic information.

scholarly journals A Semantic Similarity Analysis for Data Mappings between Heterogeneous XML Schemas

2011 ◽  
pp. 37-52
Author(s):  
Jaewook Kim ◽  
Yun Peng
Keyword(s):  
Semantic Similarity ◽  
Semantic Information ◽  
Similarity Measures ◽  
Past Research ◽  
Xml Schema ◽  
Similarity Analysis ◽  
Schema Mapping ◽  
Business Applications ◽  
The Hierarchical Structure ◽  
Semantic Similarity Analysis

One of the most critical steps to integrating heterogeneous e-business applications using different XML schemas is schema mapping, which is known to be costly and error-prone. Past research on schema mapping has not made full use of semantic information imbedded in the hierarchical structure of the XML schema. This chapter investigates the existing schema mapping approaches and proposes an innovative semantic similarity analysis approach to facilitate XML schema mapping, merging and reuse. Several key innovations are introduced to better utilize available semantic information. These innovations include: (1) a layered structure analysis of XML schemas, (2) layer-specific semantic similarity measures, and (3) an efficient semantic similarity analysis using parallel and distributed computing technologies. Experimental results using two different schemas from a real world application demonstrate that the proposed approach is valuable for addressing difficulties in XML schema mapping.

Short Tamil sentence similarity calculation using knowledge-based and corpus-based similarity measures

2017 ◽  
Author(s):  
Anutharsha Selvarasa ◽  
Nilasini Thirunavukkarasu ◽  
Niveathika Rajendran ◽  
Chinthoorie Yogalingam ◽  
Surangika Ranathunga ◽  
...  
Keyword(s):  
Similarity Measures ◽  
Knowledge Based ◽  
Sentence Similarity ◽  
Similarity Calculation

scholarly journals Evolution of Semantic Similarity—A Survey

2021 ◽  
Vol 54 (2) ◽  
pp. 1-37
Author(s):  
Dhivya Chandrasekaran ◽  
Vijay Mago
Keyword(s):  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Hybrid Methods ◽  
Research Work ◽  
Similarity Measures ◽  
Text Data ◽  
Knowledge Based ◽  
Open Research ◽  
Research Problems

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.

scholarly journals A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Jin Zhu ◽  
Dayu Cheng ◽  
Weiwei Zhang ◽  
Ci Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Semantic Information ◽  
Edit Distance ◽  
Similarity Measures ◽  
Indoor Positioning ◽  
Synthetic Dataset ◽  
Shopping Mall ◽  
Indoor Space ◽  
Trajectory Similarity ◽  
Indoor Spaces

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

scholarly journals The Austrian Semantic EO Data Cube Infrastructure

2021 ◽  
Vol 13 (23) ◽  
pp. 4807
Author(s):  
Martin Sudmanns ◽  
Hannah Augustin ◽  
Lucas van der Meer ◽  
Andrea Baraldi ◽  
Dirk Tiede
Keyword(s):  
Land Cover ◽  
Knowledge Engineering ◽  
A Priori ◽  
Data Cube ◽  
Color Information ◽  
Data Cubes ◽  
Semantic Enrichment ◽  
Knowledge Based ◽  
Geographical Regions ◽  
Semantic Models

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic reflectance values that lack inherent semantic information (meaning) and require interpretation. However, interpretation is an ill-posed problem that is difficult for many users to solve. Our semantic EO data cube architecture aims to implement computer vision in EO data cubes as an explainable artificial intelligence approach. Automatic semantic enrichment provides semi-symbolic spectral categories for all observations as an initial interpretation of color information. Users graphically create knowledge-based semantic models in a convergence-of-evidence approach, where color information is modelled a-priori as one property of semantic concepts, such as land cover entities. This differs from other approaches that do not use a-priori knowledge and assume a direct 1:1 relationship between reflectance values and land cover. The semantic models are explainable, transferable, reusable, and users can share them in a knowledgebase. We provide insights into our web-based architecture, called Sen2Cube.at, including semantic enrichment, data models, knowledge engineering, semantic querying, and the graphical user interface. Our implemented prototype uses all Sentinel-2 MSI images covering Austria; however, the approach is transferable to other geographical regions and sensors. We demonstrate that explainable, knowledge-based big EO data analysis is possible via graphical semantic querying in EO data cubes.

User-Centric Similarity and Proximity Measures for Spatial Personalization

2010 ◽  
Vol 6 (2) ◽  
pp. 59-78 ◽  
Author(s):  
Yanwu Yang ◽  
Christophe Claramunt ◽  
Marie-Aude Aufaure ◽  
Wensheng Zhang
Keyword(s):  
Information Needs ◽  
Spatial Information ◽  
Personal Information ◽  
Similarity Measures ◽  
Spatial Proximity ◽  
Mobile Environments ◽  
Conceptual Approach ◽  
Semantic Domain ◽  
User Centric ◽  
Proximity Measures

Spatial personalization can be defined as a novel way to fulfill user information needs when accessing spatial information services either on the web or in mobile environments. The research presented in this paper introduces a conceptual approach that models the spatial information offered to a given user into a user-centered conceptual map, and spatial proximity and similarity measures that considers her/his location, interests and preferences. This approach is based on the concepts of similarity in the semantic domain, and proximity in the spatial domain, but taking into account user’s personal information. Accordingly, these spatial proximity and similarity measures could directly support derivation of personalization services and refinement of the way spatial information is accessible to the user in spatially related applications. These modeling approaches are illustrated by some experimental case studies.

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