# A Purely Entity-Based Semantic Search Approach for Document Retrieval
## 3 Materials and Methods
### Retrieval and Ranking Method
#### 3.3.3 Algorithm
However, the issue is the computed annotation score, which means that the selected entity is sure. In other words, the 0.94 score means that there is no way it could be another \"[PERSON]\". However, with TagMe tool the selected entity was \"[PERSON]_(sea_captain)\", \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{
\begin{tabular}{c} **AVGs \(\geq\) Min** \\ **(154 Queries)** \\ \end{tabular} } & **AVGs \(\geq\) 0.85** & **AVGs \(\geq\) 0.95** & **AVGs \(\geq\) 1.0** \\  & & **(132 Queries)** & **(109 Queries)** & **(3 Queries)** \\ \hline Galago (Dirichlet) & 0.4001 & 0.4138 & 0.4392 & 0.6667 \\ LongP (Longformer) & **0.6737** & **0.6896** & **0.6946** & **0.8284** \\ PES4 IR & 0.2599 & 0.2739 & 0.2864 & 0.7380 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: nDCG@5 scores for queries’ classes given by DBpedia Spotlight on Robust collection. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**qID**} & \multirow{2}{*}{**Query Text**} & **REL Annotations** (**\(\mathbf{\text{``Mention''}}\rightarrow\mathbf{\text{{\bf{Entity}}}}\rightarrow\mathbf{ \text{{\bf Score}}}\)**) & **Annotation Avg Scores** \\ \hline
396 & sick building syndrome & “sick building syndrome” \(\rightarrow\) Sick\_building\_syndrome \(\rightarrow\) 1.0 & 1.0 \\
400 & Amazon rain forest & “Amazon rain forest” \(\rightarrow\) Amazon\_rainforest\(\rightarrow\) 1.0 & 1.0 \\
429 & Legionnaires’ disease & “Legionnaires’ disease” \(\rightarrow\) Legionnaires’\_disease \(\rightarrow\) 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: DBpedia Spotlight annotations whose average scores are equal to 1.0.

with 0.3 as the annotation score. Thus, such a case could negatively affect our approach and lead to a weak performance when it is supposed to have a better performance. ## 6 Conclusions

We introduce a purely entity-based semantic search approach for ad hoc information retrieval (PES4 IR) as a novel solution. The main goal of this paper is to analyze the impact of the purely entity-based semantic search on the effectiveness of ad hoc document retrieval by giving clear answers about when such an approach achieves its best and when it does not, showing its strengths and weaknesses. Our proposed approach represents queries and documents only by entity-based representation. It mainly includes its own entity linking method, which is appropriate for document text (EL4 DT); it is an inverted indexing method and a method for document retrieval and ranking which is designed to leverage all the strengths of the approach. To evaluate the approach, we used the TREC 2004 Robust and MSMARCO collections, and we linked queries with two different entity linking methods, DBpedia Spotlight and REL. As our approach uses a purely entity-based representation for queries and documents, only completely annotated queries are considered. Galago (Dirichlet) and LongP (Longformer) models are used to compare the performance on the corresponding group of queries of the two collections and to show how PES4 IR could be compared to any other retrieval method. In the experiments, we used the average annotation score of each query's entities as only completely annotated queries are considered. The results indicate that as the average annotation score increases, the ranking score gets higher as well. Indeed, with the highest-scoring queries annotated by DBpedia Spotlight and REL, our approach outperforms the Galago method based on the nDCG@20 evaluation metric. Thus, our approach offers an added value when used with the Galago (Dirichlet method) or LongP (Longformer) model. For the queries with the highest annotation average score (avg_score \(\geq\) 0.75) among the queries annotated by the REL entity linking method, our approach achieved the maximum nDCG@5 score (1.00), which would be an added value for any ad hoc document retrieval method that does not reach the same score for the same queries.