# Retrieval Verification: SemanticGraph2Vec_Semantic_graph_embedding_for_text_representation_parsed

## Pilar: Background
**Query:** _Fenomena ini merupakan isu krusial. Oleh karena itu, studi ini dilakukan untuk merespons urgensi permasalahan yang melatarbelakangi penelitian._

### Rank 1 (Score: 0.8109)
**Header:** **5. Experiments and evaluation**
**Content:**
```text
The ability of SemanticGraph2Vec model to improve NLP tasks that use graph embedding in their techniques was evaluated. In the experiments, the POS tagging problem is used as a case study. The experiments’ main purpose is to address other baseline graph embedding models that are used for improving NLP tasks, rather than achieving better POS tagging performance.
```

### Rank 2 (Score: 0.8059)
**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: 0.8045)
**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:** _Penelitian ini menggunakan pendekatan terstruktur di mana data dikumpulkan dan analisis dilakukan menggunakan teknik serta metode yang dijelaskan pada bagian ini._

### Rank 1 (Score: 0.8445)
**Header:** **1. Introduction**
**Content:**
```text
levels of representation that raise the effectiveness of machine learning models. Deep autoencoders are used in deep learning techniques to reduce dimensionality based on their ability to model the nonlinear structures of data. SDNE [16] and DNGR [17] are two models for deep learning graph embedding that use deep autoencoders to generate embedding models that capture nonlinearity in graphs. Deep-learning-based methods can learn combinations of community and structural equivalences from a graph. The weights of an autoencoder can be used as a structural representation of a graph in deep learning methods.
```

### Rank 2 (Score: 0.8382)
**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: 0.8371)
**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:** _Meskipun banyak studi telah dilakukan, masih terdapat kekosongan literatur (gap), kelemahan, dan batasan pada penelitian sebelumnya yang belum dieksplorasi._

### Rank 1 (Score: 0.8116)
**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: 0.7912)
**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: 0.7880)
**Header:** **3. Research objectives and contributions**
**Content:**
```text
There have been very few studies on graph embedding that have considered the semantic relationships between graph components (e.g., vertices or subgraphs). Additionally, most semantic graph embedding approaches focus on a specific application or specific type of graph (e.g., MetaGraph2Vec [22], which focuses on heterogeneous graphs). Therefore, the broad objective of our research is to develop a novel semantic graph embedding model in which the semantic relationships between words are considered during the walk generation process to enhance various NLP tasks, such as textual entailment and Part-ofSpeech (POS) tagging. To summarize, the main contributions of this research are:  
- A novel semantic graph embedding model that considers the semantic relationships between text components (words) called SemanticGraph2Vev.  
- Enhance the performance of the DeepWalk model in terms of random walk generation process to achieve better results for NLP tasks.  
It is worth to mention that POS tagging is considered as a case study to compare the performance and impact of baseline graph embedding with that of SemanticGraph2Vec. Therefore, the experiments conducted in the paper research do not seek to achieve the best POS tagging performance.
```

## Pilar: Objective
**Query:** _Tujuan utama dan sasaran spesifik dari artikel ini adalah untuk memberikan kontribusi baru, mengeksplorasi, dan menyelesaikan permasalahan tersebut._

### Rank 1 (Score: 0.8211)
**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: 0.8191)
**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: 0.8138)
**Header:** **Data availability**
**Content:**
```text
Semantic relationship ranks.  
|Rank|Semantic relationship|Semantic relationship|
|---|---|---|
||name|description|
|1|SBJ|Subject|
|2|OBJ|Object|
|3|ADJ|Adjective|
|4|IDF|Identifier|
|5|MOD|Modifier|
|6|OP1|Option 1|
|7|OP2|Option 2|
|8|OP3|Option 3|
|9|OP4|Option 4|
|10|OP5|Option 5|
|11|OP6|Option 6|
|12|LOCATION|Location|
|13|PLACE|Place|
|14|DIRECTION|Direction|
|15|SOURCE|Source location|
|16|DESTINATION|Destination location|
|17|START-LOC|Start location|
|18|DATE/TIME|Date or time|
|19|TIME|Time|
|20|EXACT|Exact date|
|21|START|Start date or start time|
|22|FINISH|Finish date or finish|
|||time|
|23|DURATION|Time duration|
|24|WEEKDAY|Week day|
|25|DATE|Date|
|26|MONTH|Month|
|27|YEAR|Year|
|28|DECADE|Decade|
|29|WEEK|Week|
|30|HOUR|Hour|
|31|MINUTE|Minute|
|32|SECOND|Second|  
SemanticGraph2Vec and the baseline models explains the efficiency improvement of NLP tasks. Additionally, DeepWalk can produce redundant pathways that yield smaller distinct sample sizes, which can affect the overall performance of the model.
```

## Pilar: Futurework
**Query:** _Keterbatasan dalam penelitian ini membuka peluang bagi studi selanjutnya. Penelitian di masa depan disarankan untuk memperluas cakupan dan menyelesaikan isu terkait._

### Rank 1 (Score: 0.8035)
**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: 0.7882)
**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: 0.7848)
**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.
```
