# Comprehensive Review and Future Research Directions on Dynamic Faceted Search
## 5 Taxonomy and Research of Faceted Search
### Graphical Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref** & **Time** & **Model** & **Data** & **Main Concepts** & **Structure** & **Ranking** &
\begin{tabular}{c} **Improvement, A: Advantages,** \\ **D: Drawbacks** \\ \end{tabular} \\ \hline
[75] & 2020 & \begin{tabular}{c} Newspaper \\ Navigator \\ \end{tabular} & \begin{tabular}{c} Examples of \\ searching \\ \end{tabular} & \begin{tabular}{c} Open faceted search, \\ which empowers \\ users to specify their \\ own facets in an open \\ domain fashion \\ \end{tabular} & \
\begin{tabular}{c} Users need to \\ knowhow to \\ define and refine \\ facets \\ \end{tabular} & None &
\begin{tabular}{c} Walks through examples of searching \\ with Newspaper Navigator and \\ highlights the facet learning and \\ exploration affordances. D: facet \\ categories must be predefined and may \\ not align with the facets that a user \\ desires during the search process. \\ \end{tabular} \\ \hline
[1] & 2020 & \begin{tabular}{c} Data lake \\ organization \\ \end{tabular} & \begin{tabular}{c} Proposes an \\ approximate \\ algorithm \\ \end{tabular} & \begin{tabular}{c} For the data lake \\ organization problem \\ \end{tabular} & \
\begin{tabular}{c} Structures \\ optimized for \\ dataset \\ discovery \\ \end{tabular} & Participants’ rankings &
\begin{tabular}{c} Probabilistic model of how users \\ interact with an organization proposes \\ an approximate \\ large organization problem. D: plans to \\ compare organizations with existing \\ taxonomies and to provide techniques \\ for metadata enrichment. \\ \end{tabular} \\ \hline
[76] & 2020 & \begin{tabular}{c} Simulation-based \\ evaluation \\ \end{tabular} & \begin{tabular}{c} Size and the \\ granularity of \\ the sought \\ object ranking \\ \end{tabular} & \begin{tabular}{c} Extension of the \\ model with two \\ parameters that \\ enable specifying the \\ desired answer \\ \end{tabular} & \begin{tabular}{c} Extraction of the \\ model with two \\ parameters that \\ ranking \\ \end{tabular} & \begin{tabular}{c} Structured query \\ \end{tabular} & \
\begin{tabular}{c} The Smartisrank \\ ranking \\ \end{tabular} &
\begin{tabular}{c} Extended model for FS that aims at \\ improving the exploration experience of \\ the users. Proposed two parameters that \\ specify the desired properties of the \\ and algorithms for scalability, i.e., for \\ enabling faceted search with automated \\ ranking over very big datasets. \\ \end{tabular} \\ \hline
[77] & 2020 & \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular} \end{tabular} \\ \end{tabular} \\ \end{tabular} & \begin{tabular}{c} Semantic search, \\ faceted navigation, \\ data unification, \\ discovering, and \\ generation of search \\ recommendations \\ over the information \\ contained \\ \end{tabular} & \begin{tabular}{c} Semantic Web \\ \end{tabular} & \begin{tabular}{c} Key ranking \\ techniques \\ \end{tabular} & \begin{tabular}{c} Linked data principles and practices to \\ be adopted by an increasing number of \\ data providers, which leads to the \\ creation of a global data space on the \\ web. LINDASearch is a system for \\ semantic search, faceted navigation, \\ data unification, \\ generation of search recommendation \\ over the information \\ contained \\ \end{tabular} & \begin{tabular}{c} Semantic Web \\ \end{tabular} & \
\begin{tabular}{c} Key ranking \\ techniques \\ \end{tabular} &
\begin{tabular}{c} Linked data principles and practices to \\ be adopted by an increasing number of \\ data providers, which leads to the \\ creation of a global data space on the \\ web. LINDASearch is a system for \\ semantic search, faceted navigation, \\ data unification, discovering, and \\ generation of search recommendation \\ over the information \\ \end{tabular} \\ \hline
[77] & 2020 & \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular} \end{tabular} \\ \end{tabular} \\ \end{tabular} & \begin{tabular}{c} \begin{tabular}{c} Semantic Web \\ \end{tabular} \\ \end{tabular} & \
\begin{tabular}{c} Key ranking \\ techniques \\ \end{tabular} &
\begin{tabular}{c} Framework comes with an intermediate \\ domain-specific language. Thereby, the \\ approach is SPARQL-driven, which \\ means that every faceted search \\ information need is intentionally \\ expressed as a single SPARQL query. \\ \end{tabular} \\ \hline
[78] & 2020 & \begin{tabular}{c} SPARQL engines \\ \end{tabular} & RDF dataset & \begin{tabular}{c} Presenta a \\ schema-agnostic \\ faceted browsing \\ benchmark \\ generation \\ framework for RDF \\ data and SPARQL \\ engines \\ \end{tabular} & \
\begin{tabular}{c} Similarity-based \\ \end{tabular} & None &
\begin{tabular}{c} Presented a schema-agnostic faceted \\ search benchmark generation \\ framework for triple stores. Comparison \\ of the general benchmarks with \\ existing SPARQL-driven benchmarks in \\ order to provide a bigger picture such as \\ by means of assessing the similarities \\ and differences of benchmarks w.r.t. the \\ SPARQL Language features used. \\ \end{tabular} \\ \hline
[79] & 2014 & \begin{tabular}{c} \begin{tabular}{c} \begin{tabular} \end{tabular} \\ \end{tabular} \\ \end{tabular} & Small dataset & \begin{tabular}{c} Described and \\ evaluated Hipplaus, a \\ system that offers \\ exploratory search \\ enriched with \\ preferences \\ \end{tabular} & \begin{tabular}{c} Faceted and \\ dynamic \\ taxonomies \\ \end{tabular} & \
\begin{tabular}{c} Preferences- \\ ranked \\ list \\ \end{tabular} &
\begin{tabular}{c} Hipplaus supports the very popular \\ interaction model of Faceted and \\ Dynamic Taxonies (FDT), enriched \\ with user actions, which allow the users \\ to express their preferences. The \\ Hipplaus system demonstrates the \\ feasibility of this extension. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}