# Comprehensive Review and Future Research Directions on Dynamic Faceted Search
## 5 Taxonomy and Research of Faceted Search
### Faceted Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref** & **Time** & **Model** & **Data** & **Main Concepts** & **Structure** & **Ranking** & **Improvement, A. Advantages, D: Drawbacks** \\ \hline
[68] & 2008 & \multirow{4}{*}{FacetZoom} & Continuous and discrete & FacetZoom, a unique widget that the joins the browsing of facets with the expandable user interface & FaceZoom, a unique widget that the joins enables quick traversal in all stages and maintain the context & \multirow{4}{*}{None} & The space-structuring widgets and data are applied and sampled, respectively, using the two prototypes. A: multilateral and enables static search and browsing features in the diversity of application settings. D: needs to differentiate between the performance of all widges to different techniques. \\ \hline
[69] & \multirow{4}{*}{Object property framework} & \multirow{4}{*}{Datasets of DBpedia, LO, and VAGOO2} & Proposed techniques of purifying the subtaxonomy while & & Establishes a faceted taxonomy to arrange the heterogeneous facilities, allow the different categories of facilities using the subtaxonomics, and, uphold the F8 navigation for related information applications. A: framework in which the facets are described using an object feature to extract the relevant data; also contributes to creating the concept taxonomy-generation algorithm. \\ \cline{3-6}  & & & & & (1) several agencies exist in subtaxonomies; (2) it is difficult to realize and understand the concept hierarchies; (3) the identification of entities and its mapping should be realized in generating the taxonomies. \\ \hline
[70] & 2019 & Multifaceted Trust & (1) Yelp, (2) LibraryThing & Yelp, Booking, Expedia, and VAGO2 & Finding general classes of data in order to create products of social networks & \multirow{4}{*}{None} & Multifaceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. A: integrated into collaborative filtering; the resulting system was tested on two public datasets. D: need to evaluate the model on different datasets. \\ \hline
[71] & 2020 & COVIDSeer & \multirow{4}{*}{CORD-19} & Uses CeKE-TA, which uses only the title and abstract & Uses a combination of title, abstract, and available full-texts & \multirow{4}{*}{None} & Built and integrated a filtering mechanism for further accessing the results of a query of interest. A: Allows users to select filters from one or multiple categories; the intersection of all is presented in the search results. D: implements author name & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \hline
[72] & 2021 & \multirow{4}{*}{XNLP} & Metadata structure & Interactive browser-based system embedding a living survey & Keyword search matches & \multirow{4}{*}{None} & Interactive browser-based system embedding a living survey of research in the field of Explainable AI (XAI) within the domain of Natural Language Processing (NLP). D: aware of other papers that should be included. \\ \cline{3-6}  & & & & & & (X) discusses some of the requirements of modern asset storage systems for VFA and animation. A: introduces two systems that were built to address these challenges as part of the collaborative EU funded \"SAUCE\" project; DNEG's search and retrieval/remwork and Foundry’s back-end-end sensor storage. \\ \hline
[73] & 2020 & \multirow{4}{*}{DeepHate} & \multirow{4}{*}{Latent representations that combines multifaceted text representations such as word embeddings} & \multirow{4}{*}{None} & Deep learning framework known as DeepHate, which utilizes multifaceted text representations for automatic hate speech detection. A: evaluated DeepHate on three publicly available real-world datasets; extensive experiments showed that DeepHate outperformed the state-of-the-art baselines. D: incorporating nontext features into the DeepHate model and improving the post’s sentiment and topic representations with more advanced techniques. \\ \cline{3-6}  & & & & & (X) discusses some of the requirements of modern asset storage systems for VFA and animation. A: introduces two systems that were built to address these challenges as part of the collaborative EU funded \"SAUCE\" project; DNEG's search and retrieval/remwork and Foundry’s back-end sensor storage. \\ \hline
[74] & 2020 & \multirow{4}{*}{DeepHate} & \multirow{4}{*}{Latent representations that combines multifaceted text representations such as word embeddings} & \multirow{4}{*}{None} & Deep learning framework known as DeepHate, which utilizes multifaceted text representations for automatic hate speech detection. A: evaluated DeepHate on three publicly available real-world datasets; extensive experiments showed that DeepHate outperformed the state-of-the-art baselines. D: incorporating nontext features into the DeepHate model and improving the post’s sentiment and topic representations with more advanced techniques. \\ \cline{3-6}  & & & & & (X) discusses some of the requirements of modern asset storage systems for VFA and animation. A: introduces two systems that were built to address these challenges as part of the collaborative EU funded \"SAUCE\" project; DNEG's search and retrieval/remwork and Foundry’s back-end sensor storage. \\ \hline \hline \end{tabular}
\end{table}