# Retrieval Verification: SemanticGraph2Vec_Semantic_graph_embedding_for_text_representation_parsed

## Pilar: Background
**Query:** _Latar belakang, masalah, dan motivasi penelitian ini. Background, research problem, motivation, context, introduction, underlying issue._

### Rank 1 (Score: 0.2872)
**Header:** **CRediT authorship contribution statement**
**Content:**
```text
**Wael Etaiwi:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft. **Arafat Awajan:** Project administration, Supervision, Writing – review & editing.
```

### Rank 2 (Score: 0.2501)
**Header:** _W. Etaiwi and A. Awajan_
**Content:**
```text
- [38] Darwish K, Abdelali A, Mubarak H. Using stem-templates to improve arabic POS and gender/number tagging. In: Proceedings of the ninth international conference on language resources and evaluation (LREC’14). Reykjavik, Iceland: European Language Resources Association (ELRA); 2014, p. 2926–31.  
**Dr. Wael Etaiwi** is an assistant professor in the Department of Business Information Technology at Princess Sumaya University for Technology, Jordan. He received his B.Sc. degree in Computer Information Systems from the Hashemite University in 2007, his M.Sc. Degree in Computer Science in 2011 from Al Balqaa Applied University, and his Ph.D. in Computer Science from Princess Sumaya University for Technology in 2020. Dr. Al Etaiwi has 13 years of experience in software development and system analyst. His research interests include, but are not limited to, Artificial intelligence, Data mining, and Natural Language Processing.
```

### Rank 3 (Score: 0.2379)
**Header:** **References**
**Content:**
```text
- [17] Cao S, Lu W, Xu Q. Deep neural networks for learning graph representations. In: Proceedings of the thirtieth AAAI conference on artificial intelligence. AAAI ’16, AAAI Press; 2016, p. 1145–52, URL http://dl.acm.org/citation.cfm?id=3015812. 3015982.  
- [18] Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q. Line: Large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web. International World Wide Web Conferences Steering Committee; 2015, p. 1067–77.  
- [19] Cao S, Lu W, Xu Q. Grarep: Learning graph representations with global structural information. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM; 2015, p. 891–900.  
- [20] Ahmed NK, Rossi RA, Zhou R, Lee JB, Kong X, Willke TL, Eldardiry H. Representation learning in large attributed graphs. Stat 2017;1050:25.  
- [21] Rossi RA, Zhou R, Ahmed NK. Deep inductive network representation learning. In: Companion proceedings of the the web conference 2018. ACM Press; 2018, p. 953–60. http://dx.doi.org/10.1145/3184558.3191524.  
- [22] Zhang D, Yin J, Zhu X, Zhang C. MetaGraph2Vec: complex semantic path augmented heterogeneous network embedding. In: Pacific-Asia conference on knowledge discovery and data mining. Springer; 2018, p. 196–208.
```

## Pilar: Methodology
**Query:** _Metode, pendekatan, arsitektur, algoritma, eksperimen, atau dataset yang digunakan. Methodology, proposed method, approach, system architecture, dataset, materials, experimental setup, procedure._

### Rank 1 (Score: 0.4796)
**Header:** _5.3. Experiments_
**Content:**
```text
The sensitivity analysis focused on two parameters: the total number of semantic walks for each vertex and the semantic walk length _𝐿_ . The results of the experiments revealed that the greatest accuracy was obtained when the walk length was _𝐿_ = 10 and the total number of walks was 50. In the experiments, the impact of the proposed SemanticGraph2Vec method was evaluated. The performance of the proposed model was measured based on the average results of 20 runs, which each divided the original dataset into a training dataset (70% of the original dataset) and testing dataset (30% of the original dataset). As indicated in the related work section (Section 2), none of the approaches listed in Table 1 are proposed to handle semantic graphs. As a result, the proposed model is compared with the baseline models that researchers employ as a benchmark. The results are compared to those of the main baseline graph embedding models, namely Deepwalk and Node2Vec. Deepwalk is selected for comparison because it is the first random-walk-based graph embedding model and it is used as a baseline method to compare with (see Table 1). Furthermore, since the proposed SemanticGraph2vec is a graph embedding model to represent vertices, node2vec is selected for comparison because, according to Goyal and Ferrara [12], it outperforms other graph embedding methods on the task of vertex classification. Results for these models (shown in Table 3) were obtained by applying them the same datasets as the
```

### Rank 2 (Score: 0.4098)
**Header:** **CRediT authorship contribution statement**
**Content:**
```text
**Wael Etaiwi:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft. **Arafat Awajan:** Project administration, Supervision, Writing – review & editing.
```

### Rank 3 (Score: 0.3850)
**Header:** **2. Related work**
**Content:**
```text
Over the past years, most proposed graph learning models have focused on preserving the structures of represented graphs, rather than on maintaining the semantic relationships between vertices. MetaGraph2Vec is the first model considering semantic relationships between vertices during the learning process. For semantic graph embedding, the MetaGraph2Vev model learns more informative embeddings by capturing rich semantic relationships between different types of vertices. It guides random walks in heterogeneous information networks to encode the semantic relationships between different types of vertices and generate heterogeneous walks through different types of vertices.  
It should be noted that there are many different datasets used for evaluating various methods and there is no common benchmark dataset that can be used for comparison. This makes it difficult and ambiguous to compare the proposed method to other methods. The majority of
```

## Pilar: Gap
**Query:** _Gap penelitian, keterbatasan, kelemahan, atau masalah yang belum terselesaikan. Research gap, limitations, weakness, unresolved problem, remains a challenge, yet to be explored, however._

### Rank 1 (Score: 0.2834)
**Header:** **Declaration of competing interest**
**Content:**
```text
No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work.  
No data was used for the research described in the article.  
**Appendix. Semantic relationship ranks based on the semantic graph proposed by Etaiwi et al. [29]**  
See Table A.4.
```

### Rank 2 (Score: 0.2745)
**Header:** **CRediT authorship contribution statement**
**Content:**
```text
**Wael Etaiwi:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft. **Arafat Awajan:** Project administration, Supervision, Writing – review & editing.
```

### Rank 3 (Score: 0.2717)
**Header:** _W. Etaiwi and A. Awajan_
**Content:**
```text
- [38] Darwish K, Abdelali A, Mubarak H. Using stem-templates to improve arabic POS and gender/number tagging. In: Proceedings of the ninth international conference on language resources and evaluation (LREC’14). Reykjavik, Iceland: European Language Resources Association (ELRA); 2014, p. 2926–31.  
**Dr. Wael Etaiwi** is an assistant professor in the Department of Business Information Technology at Princess Sumaya University for Technology, Jordan. He received his B.Sc. degree in Computer Information Systems from the Hashemite University in 2007, his M.Sc. Degree in Computer Science in 2011 from Al Balqaa Applied University, and his Ph.D. in Computer Science from Princess Sumaya University for Technology in 2020. Dr. Al Etaiwi has 13 years of experience in software development and system analyst. His research interests include, but are not limited to, Artificial intelligence, Data mining, and Natural Language Processing.
```

## Pilar: Objective
**Query:** _Tujuan utama, sasaran, atau pertanyaan penelitian yang ingin dijawab. Main objective, research goal, purpose of this study, aims to investigate, to evaluate, this paper proposes._

### Rank 1 (Score: 0.3997)
**Header:** **CRediT authorship contribution statement**
**Content:**
```text
**Wael Etaiwi:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft. **Arafat Awajan:** Project administration, Supervision, Writing – review & editing.
```

### Rank 2 (Score: 0.2955)
**Header:** _5.3. Experiments_
**Content:**
```text
Experiments were performed to evaluate the ability of SemanticGraph2Vec to improve POS tagging tasks compare with other baseline graph embedding models. The evaluation metrics considered were precision, recall, and F-score. Precision is the proportion of correct decisions made over the total number of decisions for a given class. Recall refers to the fraction of correct decisions provided by the POS tagger over the total number of POS tags for a given class. The evaluation process is a conditional decision-making process in which the judgement of the proposed model is considered to be correct if and only if it is compatible with the ground truth labels in the data collection. Finally, the F-score is the harmonic mean of precision and recall.  
The sensitivity of the semantic walk length and total number of semantic walks for each vertex was measured and analyzed in separate experiments. Table 2 lists the accuracy results of applying the proposed model to various parameters.
```

### Rank 3 (Score: 0.2811)
**Header:** _W. Etaiwi and A. Awajan_
**Content:**
```text
- [38] Darwish K, Abdelali A, Mubarak H. Using stem-templates to improve arabic POS and gender/number tagging. In: Proceedings of the ninth international conference on language resources and evaluation (LREC’14). Reykjavik, Iceland: European Language Resources Association (ELRA); 2014, p. 2926–31.  
**Dr. Wael Etaiwi** is an assistant professor in the Department of Business Information Technology at Princess Sumaya University for Technology, Jordan. He received his B.Sc. degree in Computer Information Systems from the Hashemite University in 2007, his M.Sc. Degree in Computer Science in 2011 from Al Balqaa Applied University, and his Ph.D. in Computer Science from Princess Sumaya University for Technology in 2020. Dr. Al Etaiwi has 13 years of experience in software development and system analyst. His research interests include, but are not limited to, Artificial intelligence, Data mining, and Natural Language Processing.
```

## Pilar: Futurework
**Query:** _Pekerjaan di masa depan, saran untuk peneliti lain, atau langkah selanjutnya. Future work, recommendations, future research directions, next steps, further investigation._

### Rank 1 (Score: 0.3811)
**Header:** **CRediT authorship contribution statement**
**Content:**
```text
**Wael Etaiwi:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft. **Arafat Awajan:** Project administration, Supervision, Writing – review & editing.
```

### Rank 2 (Score: 0.2961)
**Header:** _W. Etaiwi and A. Awajan_
**Content:**
```text
- [38] Darwish K, Abdelali A, Mubarak H. Using stem-templates to improve arabic POS and gender/number tagging. In: Proceedings of the ninth international conference on language resources and evaluation (LREC’14). Reykjavik, Iceland: European Language Resources Association (ELRA); 2014, p. 2926–31.  
**Dr. Wael Etaiwi** is an assistant professor in the Department of Business Information Technology at Princess Sumaya University for Technology, Jordan. He received his B.Sc. degree in Computer Information Systems from the Hashemite University in 2007, his M.Sc. Degree in Computer Science in 2011 from Al Balqaa Applied University, and his Ph.D. in Computer Science from Princess Sumaya University for Technology in 2020. Dr. Al Etaiwi has 13 years of experience in software development and system analyst. His research interests include, but are not limited to, Artificial intelligence, Data mining, and Natural Language Processing.
```

### Rank 3 (Score: 0.2780)
**Header:** _5.3. Experiments_
**Content:**
```text
Experiments were performed to evaluate the ability of SemanticGraph2Vec to improve POS tagging tasks compare with other baseline graph embedding models. The evaluation metrics considered were precision, recall, and F-score. Precision is the proportion of correct decisions made over the total number of decisions for a given class. Recall refers to the fraction of correct decisions provided by the POS tagger over the total number of POS tags for a given class. The evaluation process is a conditional decision-making process in which the judgement of the proposed model is considered to be correct if and only if it is compatible with the ground truth labels in the data collection. Finally, the F-score is the harmonic mean of precision and recall.  
The sensitivity of the semantic walk length and total number of semantic walks for each vertex was measured and analyzed in separate experiments. Table 2 lists the accuracy results of applying the proposed model to various parameters.
```
