# Retrieval Verification: SemanticGraph2Vec_Semantic_graph_embedding_for_text_representation_parsed

## Pilar: Background
**Query:** _latar belakang masalah, urgensi penelitian, motivasi mendasar, dilatar belakangi oleh, fenomena krusial, konteks situasi saat ini, dan alasan utama studi ini dilakukan._

### Rank 1 (Score: 0.8440)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```

### Rank 2 (Score: 0.8222)
**Header:** _5.2. Dataset_
**Content:**
```text
To evaluate the ability of the proposed model to improve the task of POS tagging, a custom dataset extracted from news articles was used in the experiments. This dataset was compiled from an Arabic news website called AlJazeera News.[2] It consists of more than 3500 words that were annotated with their corresponding POS tags using the Farasa POS tagger [37]. Farasa is an open-source text-processing toolkit that offers many Arabic text processing libraries for lemmatization, POS tagging, dependency parsing, and name-entity recognition. The Farasa results are chosen as the reference tag because they provided high-quality POS tagging results with 98.1% accuracy [38].  
> 1 http://nlp.stanford.edu:8080/parser/index.jsp  
> 2 www.aljazeera.net  
**Table 2**  
Sensitivity analysis of the proposed model parameters.
```

### Rank 3 (Score: 0.8185)
**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: Methodology
**Query:** _rincian teknis metodologi, arsitektur sistem, desain eksperimen, teknik pengumpulan dataset, algoritma yang diusulkan, sampel, dan prosedur pengujian._

### Rank 1 (Score: 0.8252)
**Header:** _5.2. Dataset_
**Content:**
```text
To evaluate the ability of the proposed model to improve the task of POS tagging, a custom dataset extracted from news articles was used in the experiments. This dataset was compiled from an Arabic news website called AlJazeera News.[2] It consists of more than 3500 words that were annotated with their corresponding POS tags using the Farasa POS tagger [37]. Farasa is an open-source text-processing toolkit that offers many Arabic text processing libraries for lemmatization, POS tagging, dependency parsing, and name-entity recognition. The Farasa results are chosen as the reference tag because they provided high-quality POS tagging results with 98.1% accuracy [38].  
> 1 http://nlp.stanford.edu:8080/parser/index.jsp  
> 2 www.aljazeera.net  
**Table 2**  
Sensitivity analysis of the proposed model parameters.
```

### Rank 2 (Score: 0.8192)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```

### Rank 3 (Score: 0.8163)
**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
```

## Pilar: Gap
**Query:** _keterbatasan penelitian terdahulu, kelemahan metode sebelumnya, kekosongan literatur, masalah yang belum terselesaikan, namun demikian, akan tetapi, dan hasil yang belum jelas._

### Rank 1 (Score: 0.8456)
**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
```

### Rank 2 (Score: 0.8309)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```

### Rank 3 (Score: 0.8127)
**Header:** _5.2. Dataset_
**Content:**
```text
To evaluate the ability of the proposed model to improve the task of POS tagging, a custom dataset extracted from news articles was used in the experiments. This dataset was compiled from an Arabic news website called AlJazeera News.[2] It consists of more than 3500 words that were annotated with their corresponding POS tags using the Farasa POS tagger [37]. Farasa is an open-source text-processing toolkit that offers many Arabic text processing libraries for lemmatization, POS tagging, dependency parsing, and name-entity recognition. The Farasa results are chosen as the reference tag because they provided high-quality POS tagging results with 98.1% accuracy [38].  
> 1 http://nlp.stanford.edu:8080/parser/index.jsp  
> 2 www.aljazeera.net  
**Table 2**  
Sensitivity analysis of the proposed model parameters.
```

## Pilar: Objective
**Query:** _tujuan utama penelitian, sasaran spesifik, kontribusi inti makalah, hasil yang ingin dicapai, mengeksplorasi, dan solusi yang diusulkan untuk menyelesaikan masalah._

### Rank 1 (Score: 0.8306)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```

### Rank 2 (Score: 0.8253)
**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
```

### Rank 3 (Score: 0.8247)
**Header:** **1. Introduction**
**Content:**
```text
The remaining of this paper is organized as follows. The main research objectives and contributions are outlined in Section 3. In Section 2, the related work on graph embedding are briefly reviewed. Section 4 describes the proposed model. Experimental results are discussed in Section 5. The conclusions are summarized in Section 6.
```

## Pilar: Futurework
**Query:** _saran untuk penelitian selanjutnya, rekomendasi pekerjaan di masa depan, arah pengembangan masa depan, dan peluang studi berikutnya untuk mengatasi batasan saat ini._

### Rank 1 (Score: 0.8395)
**Header:** _5.3. Experiments_
**Content:**
```text
The performance of the proposed model depends on a variety of factors, including the consistency of the input semantic graphs and the values of the semantic walk parameters. Because SemanticGraph2Vec considers the semantic relationships in input graphs, semantic walks provide richer knowledge regarding the relationships between words compared to the baseline models. This significant difference between  
_W. Etaiwi and A. Awajan_  
**Table A.4**
```

### Rank 2 (Score: 0.8229)
**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
```

### Rank 3 (Score: 0.8205)
**Header:** _5.2. Dataset_
**Content:**
```text
To evaluate the ability of the proposed model to improve the task of POS tagging, a custom dataset extracted from news articles was used in the experiments. This dataset was compiled from an Arabic news website called AlJazeera News.[2] It consists of more than 3500 words that were annotated with their corresponding POS tags using the Farasa POS tagger [37]. Farasa is an open-source text-processing toolkit that offers many Arabic text processing libraries for lemmatization, POS tagging, dependency parsing, and name-entity recognition. The Farasa results are chosen as the reference tag because they provided high-quality POS tagging results with 98.1% accuracy [38].  
> 1 http://nlp.stanford.edu:8080/parser/index.jsp  
> 2 www.aljazeera.net  
**Table 2**  
Sensitivity analysis of the proposed model parameters.
```
