# A Purely Entity-Based Semantic Search Approach for Document Retrieval
## 3 Materials and Methods
### Retrieval and Ranking Method
#### 3.3.3 Algorithm
The LongP (Longformer) model is the confirmation of this added value reached by PES4 IR, where it is among the current state-of-the-art models. For further research, how to increase the quality and number of completely annotated queries can be investigated. It would also be interesting to investigate how to conduct automatic query reformulation and query recommendation tasks based on knowledge bases, with purely entity-based representation as the output of these techniques. Conceptualization, M.L.S. ; Methodology, M.L.S. ; Software, M.L.S. ; Validation, S.G.; Resources, S.G.; Writing--original draft, M.L.S. ; Supervision, S.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The link ([[https://github.com/[PERSON]/PES4](https://github.com/[PERSON]/PES4) IR-Demo]([https://github.com/[PERSON]/PES4](https://github.com/[PERSON]/PES4) IR-Demo)) provides example of data (index) constructed by our approach. The authors declare no conflict of interest. ## Appendix A

Appendix A provides the query annotations of the TREC 2004 Robust collection, which was used to test our approach. The annotations of the query sets are achieved by two entity linking tools, DBpedia Spotlight and REL. ### DBpedia Spotlight Annotations for Robust04

In the appendix section, we have a query set of the TREC 2004 Robust collection, annotated by the DBpedia Spotlight entity linker. For each query, the average score is pro vided. The information is: (qID: query ID; Y: Yes, query completely annotated; AVG_score: computed average score; Query_Annotations: annotations of a query). ```
qID<++>Y<++>AVG_Score<++>Annotations
```
```
301<++>Y<++>0.9422930034709613<++>International_law_>Organized_crime
```

```
303<++>Y<++>0.9916617532774215<++>Hubble_Space_Telescope>Xbox_Live
```

```
305<++>Y<++>0.82337535772809<++>Bridge_of_Independent_Lists>Dangerous_([PERSON]_album)>Vehicle
```

```
308<++>Y<++>0.999981850897192<++>Dental_implant=>Dentistry
```

```
309<++>Y<++>0.8381166338949552<++>Brapping>Crime
```

```
310<++>Y<++>0.99636187523738652<++>Radio_Waves_([PERSON]_song)>Brain>Cancer
```

```
311<++>Y<++>0.999999998394102<++>Industrial_espionage
```

```
312<++>Y<++>0.9999998938582566<++>Hydroponics
```

```
3134<++>Y<++>0.963758876999175<++>United_States_Marine_Corps>Vegetation
```

```
316<++>Y<++>0.9823531973603806<++>Polygamy>Polyandry>Polygyny
```

321<++>Y<++>0.8304033796129933<++>Woman>Parliament_of_England ```
```
322<++>Y<++>0.9761505135024882<++>International_law>Art->Crime
```

323<++>Y<++>0.9989506398073358<++>Literature>Journalism>Plagiarism ```
324<++>Y<++>0.843523719434736<++>Argentina->United_Kingdom>International_relations
```  
325<++>Y<++>0.99576740995997<++>Cult>Lifestyle_(sociology) ```
327<++>Y<++>0.674173791837178<++>Modern_architecture>Slavery
```

329<++>Y<++>0.902618285189723<++>Mexico>Air_pollution ```
331<++>Y<++>0.9392907092908471<++>World_Bank>Criticism
```  
332<++>Y<++>0.9982067801874498<++>Income_tax>Tax_evasion ```
333<++>Y<++>0.9998904550378483<++>Antibiotic->Bacteria>Disease
```

334<++>Y<++>0.995398106554416<++>Export>Control_system->Cryptography ```
336<++>Y<++>0.810754260324551<++>Race_and_ethnicity_in_the_United_States_Census>Bear>Weather_Underground
```  
37<++>Y<++>0. Appendix B Appendix A provides the query annotations of the MSMARCO collection, which is used to test our approach. The annotations of the query sets are achieved by two entity linking tools DBpedia Spotlight and REL.