Update README.md

README.md

@@ -37,36 +37,37 @@ These levels of **transparency** (or *interpretability*) are generally not avail
 
 ## Sole.jl
 
-*Sole.jl* is a collection of Julia packages for symbolic learning and reasoning; *Sole.jl* covers **a relatively wide range of functionality** that is of interest for the symbolic community, but it also fills some gaps with a few functionalities for standard machine learning pipelines (e.g., feature selection on multimodal (un)structured data). At the time of writing, the framework comprehends the following packages:
-+ [*SoleLogics.jl*](https://github.com/aclai-lab/SoleLogics.jl) lays the **logical foundations** for symbolic learning. It provides a useful codebase for [*computational logic*](https://en.wikipedia.org/wiki/Computational_logic), which features easy manipulation of:
+*Sole.jl* is a collection of Julia packages for symbolic learning and reasoning.
+Although at an embryonic stage, *Sole.jl* covers a relatively wide range of functionality that is of interest for the symbolic community, but it also fills some gaps with a few functionalities for standard machine learning pipelines. At the time of writing, the framework comprehends the following packages:
++ [*SoleLogics.jl*](https://github.com/aclai-lab/SoleLogics.jl) provides the **logical layer** for symbolic learning. It provides a useful codebase for [*computational logic*](https://en.wikipedia.org/wiki/Computational_logic), which features easy manipulation of:
 	+ Propositional and (multi)modal logics (atoms, logical constants, alphabet, grammars, fuzzy algebras);
 	+ [Logical formulas](https://en.wikipedia.org/wiki/Well-formed_formula) (random generation, parsing, minimization);
 	+ [Logical interpretations](https://en.wikipedia.org/wiki/Interpretation_(logic)) (or models, e.g., [Kripke structures](https://en.wikipedia.org/wiki/Kripke_structure_(model_checking)));
 	+ Algorithms for [model checking](https://en.wikipedia.org/wiki/Model_checking) (that is, checking that a formula is satisfied by an interpretation).
-+ [*SoleData.jl*](https://github.com/aclai-lab/SoleData.jl) provides a **data layer** built on top of DataFrames.jl/Tables.jl. Its codebase is machine learning oriented and allows to:
-	+ Instantiate and manipulate [*multimodal*](https://en.wikipedia.org/wiki/Multimodal_learning) datasets for (un)supervised machine learning;
-	+ Deal with [*(un)structured* data](https://en.wikipedia.org/wiki/Unstructured_data) (e.g., graphs, images, time-series, etc.);
-	+ Describe datasets via basic statistical measures;
-	+ Save to/load from *npy/npz* format, as well as a custom CSV-based format (with interesting features such as *lazy loading* of datasets);
-	+ Perform basic data processing operations (e.g., windowing, moving average, etc.).
++ [*SoleData.jl*](https://github.com/aclai-lab/SoleData.jl) provides the **data layer** for representing *logisets*, that is, the logical counterpart to machine learning datasets:
+ 	+ Optimized data structures, useful when learning models from datasets;
+        + Support for [multimodal data](https://en.m.wikipedia.org/wiki/Multimodal_learning).
++ [*SoleModels.jl*](https://github.com/aclai-lab/SoleModels.jl) defines the building blocks of **symbolic modeling**, featuring:
+	+ Definitions for (logic-agnostic) symbolic models (mainly, decision rules/lists/trees/forests);
+	+ Support for mixed, neuro-symbolic computation.
+
+<!--
 + [*SoleFeatures.jl*](https://github.com/aclai-lab/SoleFeatures.jl) provides tools for filter-based **feature selection** on [*(un)structured* data](https://en.wikipedia.org/wiki/Unstructured_data). At this time, the package provides:
 	+ 3 native feature selection methods, plus a wrapper around python implementations from *scikit-learn* and *scikit-feature*;
 	+ Generalized feature selection methods that also apply to dimensional data (e.g., images or time-series), via a step of window-based flattening;
 	+ Specific methods for time-series feature selection, based on [Catch22.jl](https://github.com/brendanjohnharris/Catch22.jl/);
 	+ An easily extendible codebase, with abstraction layers similar to those of *scikit-learn*.
-+ [*SoleModels.jl*](https://github.com/aclai-lab/SoleModels.jl) defines the building blocks of **symbolic modeling and learning**. It is the core of the framework, and it features:
-	+ Definitions for symbolic models (decision trees/forests, rules, etc.);
-	+ Optimized data structures, useful when learning models from datasets;
-	+ Support for mixed, neuro-symbolic computation.
 + [*SolePostHoc.jl*](https://github.com/aclai-lab/SolePostHoc.jl) provides some tools for **analyzing and post-processing the learned symbolic models**. It features:
 	+ Tools for inspecting and simplifying the models;
 	+ Rule extraction from decision forests;
 	+ Feature importance estimation.
+  (e.g., feature selection on multimodal (un)structured data)
+-->
 
 Altogether, *Sole.jl* makes for a powerful tool built with an eye to **formal correctness**, and is of use for both machine learning practitioners and computational logicians.
 
 **Q:** Ok, so what symbolic learning methods do you people provide?
-**A:** At the moment, [*ModalDecisionTrees.jl*](https://github.com/aclai-lab/ModalDecisionTrees.jl) is the only package compatible with Sole.jl, and it provides novel decision tree algorithms based on multimodal temporal and spatial logics for time-series and image classification. Checkout the related [talk at JuliaCon22](https://live.juliacon.org/talk/RQP9TG).
+**A:** At the moment, [*ModalDecisionTrees.jl*](https://github.com/aclai-lab/ModalDecisionTrees.jl) is the only package compatible with Sole.jl, and it provides novel decision tree algorithms based on modal temporal and spatial logics for time-series and image classification. Checkout the related [talk at JuliaCon22](https://live.juliacon.org/talk/RQP9TG).
 
 **Q:** Why the name?
 **A:** *Sole* stands for SymbOlic LEarning; it also means "sun" in Italian, a hint to the enlightening power of transparent modeling.