KPs are an abstraction of frames as introduced by Fillmore and Minsky. KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts (i.e., top-down defined artifacts that can be compared to KPs, such as FrameNet frames or Ontology Design Patterns) to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Unfortunately, type paths are not always available. In fact, Linked Data is a knowledge soup because of the heterogeneous semantics of its datasets and because of the limited intentional as well as extensional coverage of ontologies (e.g., DBpedia ontology, YAGO) or other controlled vocabularies (e.g., SKOS, FOAF, etc.). Thus, we propose a solution for enriching Linked Data with additional axioms (e.g., rdf:type axioms) by exploiting the natural language available for example in annotations (e.g. rdfs:comment) or in corpora on which datasets in Linked Data are grounded (e.g. DBpedia is grounded on Wikipedia). Then we present K∼ore, a software architec- ture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. K∼ore is the architectural binding of a set of tools, i.e., K∼tools, which implements the methods for KP transformation and extraction. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.