+However, in doing so he does not choose the subset in advance by forgetting
+the rest of his knowledge. On the contrary he may proceed with a sort of
+top-down strategy: he may always inspect or use part of his knowledge, but
+when he actually does so he should check recursively that inconsistencies are
+not exploited.
+
+Contrarily to the mathematical practice, the usual tendency in the world of
+assisted automation is that of building a logical environment (a consistent
+subset of the library) in a bottom up way, checking the consistency of a
+new axiom or theorem as soon as it is added to the environment. No lemma
+or definition outside the environment can be used until it is added to the
+library after every notion it depends on. Moreover, very often the logical
+environment is the only part of the library that can be inspected,
+that we can search lemmas in and that can be exploited by automatic tactics.
+
+Moving one by one notions from the library to the environment is a costly
+operation since it involves re-checking the correctness of the notion.
+As a consequence mathematical notions are packages into theories that must
+be added to the environment as a whole. However, the consistency problem is
+only raised at the level of theories: theories must be imported in a bottom
+up way and the system must check that no inconsistency arises.
+
+The practice of limiting the scope on the library to the logical environment
+is contrary to our commitment of being able to fully exploit as much as possible
+of the library at any given time. To reconcile consistency and visibility
+we have departed from the traditional implementation of an environment
+allowing environments to be built on demand in a top-down way. The new
+implementation is based on a modified meta-theory that changes the way
+convertibility, type checking, unification and refinement judgements.
+The modified meta-theory is fully described in \cite{libraryenvironments}.
+Here we just remark how a strong commitment on the way the user interacts
+with the library has lead to modifications to the logical core of the proof
+assistant. This is evidence that breakthroughs in the user interfaces
+and in the way the user interacts with the tools and with the library could
+be achieved only by means of strong architectural changes.
+
+\subsubsection{Accessibility}
+A large library that is completely in scope needs effective indexing and
+searching methods to make the user productive. Libraries of formal results
+are particularly critical since they hold a large percentage of technical
+lemmas that do not have a significative name and that must be retrieved
+using advanced methods based on matching, unification, generalization and
+instantiation.
+
+The efficiency of searching inside the library becomes a critical operation
+when automatic tactics exploit the library during the proof search. In this
+scenario the tactics must retrieve a set of candidates for backward or
+forward reasoning in a few milliseconds.
+
+The choice of several proof assistants is to use ad-hoc data structures,
+such as context trees, to index all the terms currently in scope. These
+data structures are expecially designed to quickly retrieve terms up
+to matching, instantiation and generalization. All these data structures
+try to maximize sharing of identical subterms so that matching can be
+reduced to a visit of the tree (or dag) that holds all the maximally shared
+terms together.
+
+Since the terms to be retrieved (or at least their initial prefix)
+are stored (actually ``melted'') in the data structure, these data structures
+must collect all the terms in a single location. In other words, adopting
+such data structures means centralizing the library.
+
+In the \MOWGLI{} project we have tried to follow an alternative approach
+that consists in keeping the library fully distributed and indexing it
+by means of spiders that collect metadata and store them in a database.
+The challenge is to be able to collect only a smaller as possible number
+of metadata that provide enough information to approximate the matching
+operation. A matching operation is then performed in two steps. The first
+step is a query to the remote search engine that stores the metadata in
+order to detect a (hopefully small) complete set of candidates that could
+match. Completeness means that no term that matches should be excluded from
+the set of candiates. The second step consists in retrieving from the
+distributed library all the candidates and attempt the actual matching.
+
+In the last we years we have progressively improved this technique.
+Our achievements can be found in \cite{query1,query2,query3}.
+
+The technique and tools already developed have been integrated in \MATITA{},
+that is able to contact a remote \WHELP{} search engine \cite{whelp} or that
+can be directly linked to the code of the \WHELP. In either case the database
+used to store the metadata can be local or remote.
+
+Our current challenge consists in the exploitation of \WHELP{} inside of
+\MATITA. In particular we are developing a set of tactics, for instance
+based on paramodulation \cite{paramodulation}, that perform queries to \WHELP{}
+to restrict the scope on the library to a set of interesting candidates,
+greatly reducing the search space. Moreover, queries to \WHELP{} are performed
+during parsing of user provided terms to disambiguate them.
+
+In Sect.~\ref{sec:metadata} we describe the technique adopted in \MATITA.
+
+\subsubsection{Library management}