Application of an Improved Synthetical Semantic Similarity Method in Water Knowledge Graph

Requirements association depicts inter-relation between two or more requirements within a software project. It provides necessary information for developers during decision-making processes, such as change management, development milestones, bug prediction, cost estimation, and work breakdown structure generation. Modeling association between requirements became a focus of software requirements researchers. Previous studies indicate that requirements association was pre-defined by requirements engineer based on their expert judgments. The judgments require knowledge on requirements and their class realizations. This paper introduces a method to generate a mapping between a set of requirement statements and a set of classes of a given project that realized the respected requirements. The method also generates associations among requirements based on information on associations between classes and the class-requirement mapping. The method utilizes element of relational information resided in a class diagram of respected project. A semantic similarity method was used to define the requirements with their realization classes. A class is considered realizing a requirement if and only if their semantic similarity is higher than a certain threshold. A set of experimentation on four different projects was conducted. The result of the approach was compared with the output produced by human annotators using kappa statistics. The approach is considered as having a fair agreement level (i.e. with kappa value 0.37) with the human annotators to identify and model requirement associations.

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Comparison of SVM classification method and semantic similarity method for sentiment classification

2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems ◽

10.1109/ccis.2014.7175697 ◽

2014 ◽

Author(s):

Changqin Quan ◽

Xiquan Wei ◽

Fuji Ren

Keyword(s):

Semantic Similarity ◽

Sentiment Classification ◽

Classification Method ◽

Svm Classification ◽

Similarity Method

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Recommendation Model Based on Semantic Features and a Knowledge Graph

Wireless Communications and Mobile Computing ◽

10.1155/2021/2382892 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yudong Liu ◽

Wen Chen

Keyword(s):

Semantic Similarity ◽

Information Science ◽

Absolute Error ◽

Knowledge Graph ◽

Semantic Features ◽

Short Text ◽

Model Based ◽

Recommendation Algorithms ◽

Tremendous Amount ◽

Series Of Experiments

In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.

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Perbandingan Metode Collaborative Filtering dan Hybrid Semantic Similarity

Jurnal Nasional Teknologi Terapan (JNTT) ◽

10.22146/jntt.44938 ◽

2019 ◽

Vol 2 (3) ◽

pp. 334

Author(s):

Imam Fahrurrozi ◽

Estu Muh Dwi Admoko ◽

Anang Susilo

Keyword(s):

Collaborative Filtering ◽

Semantic Similarity ◽

Recommender System ◽

Cold Start ◽

Performance Tests ◽

Online Commerce ◽

Filtering Method ◽

Similarity Method

Recommender system is a component which has been developed for online commerce purposes. In this issue, one of the popular methods that has been widely used is collaborative filtering. However, this method has some drawbacks and needs to be improved. Therefore, in this research a combination of Collaborative Filtering (CF) and semantic similarity method has been compare with original CF, and the result expected reducing some deficiencies on the original collaborative filtering method. Based on the performance tests, the results conclude that the combination can reduce some weaknesses on the original collaborative filtering, especially on the cold-start item and sparsity issue.

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Object-Action Association Extraction from Knowledge Graphs

10.3233/ssw210048 ◽

2021 ◽

Author(s):

Alexandros Vassiliades ◽

Theodore Patkos ◽

Vasilis Efthymiou ◽

Antonis Bikakis ◽

Nick Bassiliades ◽

...

Keyword(s):

Artificial Intelligence ◽

Semantic Similarity ◽

Semantic Information ◽

Research Training ◽

Relevant Literature ◽

Physical World ◽

Knowledge Graph ◽

Training Systems ◽

Novel Method ◽

Knowledge Graphs

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is of utmost importance and a long-lasting goal in Artificial Intelligence (AI) research. Training systems with relevant data is a common approach; yet, it is not always feasible to find the data needed, especially since a big portion of this knowledge is commonsense. In this paper, we propose a novel method for extracting and evaluating relations between objects and actions from knowledge graphs, such as ConceptNet and WordNet. We present a complete methodology of locating, enriching, evaluating, cleaning and exposing knowledge from such resources, taking into consideration semantic similarity methods. One important aspect of our method is the flexibility in deciding how to deal with the noise that exists in the data. We compare our method with typical approaches found in the relevant literature, such as methods that exploit the topology or the semantic information in a knowledge graph, and embeddings. We test the performance of these methods on the Something-Something Dataset.

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A modern semantic similarity method using multiple resources for enhancing influenza detection

Expert Systems with Applications ◽

10.1016/j.eswa.2021.116466 ◽

2022 ◽

pp. 116466

Author(s):

Abdullah Almuhaimeed ◽

Mohammed A. Alhomidi ◽

Mohammed N. Alenezi ◽

Emad Alamoud ◽

Saad Alqahtani

Keyword(s):

Semantic Similarity ◽

Multiple Resources ◽

Similarity Method

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A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

Database ◽

10.1093/database/baaa078 ◽

2020 ◽

Vol 2020 ◽

Author(s):

Carlota Cardoso ◽

Rita T Sousa ◽

Sebastian Köhler ◽

Catia Pesquita

Keyword(s):

Semantic Similarity ◽

Protein Interactions ◽

Knowledge Graph ◽

Data Sets ◽

Biomedical Domain ◽

Protein Protein Interactions ◽

Data Set ◽

Human Phenotype ◽

Benchmark Data ◽

Gene Similarity

Abstract The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein–protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein–protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures. Database URL: https://github.com/liseda-lab/kgsim-benchmark.

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GO-Based Term Semantic Similarity

Ontology Learning and Knowledge Discovery Using the Web ◽

10.4018/978-1-60960-625-1.ch009 ◽

2011 ◽

pp. 174-185 ◽

Cited By ~ 1

Author(s):

Marco A. Alvarez ◽

Xiaojun Qi ◽

Changhui Yan

Keyword(s):

Gene Ontology ◽

Semantic Similarity ◽

Data Sources ◽

Future Research ◽

External Data ◽

Basic Concepts ◽

Similarity Method ◽

Go Terms

As the Gene Ontology (GO) plays more and more important roles in bioinformatics research, there has been great interest in developing objective and accurate methods for calculating semantic similarity between GO terms. In this chapter, the authors first introduce the basic concepts related to the GO and then briefly review the current advances and challenges in the development of methods for calculating semantic similarity between GO terms. Then, the authors introduce a semantic similarity method that does not rely on external data sources. Using this method as an example, the authors show how different properties of the GO can be explored to calculate semantic similarities between pairs of GO terms. The authors conclude the chapter by presenting some thoughts on the directions for future research in this field.

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