An inference engine for RDF - Agfa

An inference engine for RDF
etc… This process goes on till no more new facts can be deduced by the rules.
All solutions will then have been found.
In backward reasoning a check is also done whether Q can be unified with a
fact. After that however the system tries to unify Q with the consequent of a
rule. Suppose Q unifies with the consequent Y of the rule (X1,… ,Xn) Æ Y with
the susbtitution s. Then sY will be true if sX1, … ,sXn are true. Important here is
that the rule is interpreted backward. Resolution or backward reasoning applied
to RDF will be explained in detail in chapter 5.
3.1.5. Other mechanisms
There are many other mechanisms for automated reasoning. I will only list some
of them with references.
• sequent deduction [STANFORD] [VAN BENTHEM].
• natural deduction [STANFORD] [VAN BENTHEM].
• the matrix connection method [STANFORD].
• term rewriting [DICK].
• mathematical induction [STANFORD] [WALSH].
• higher order logic [STANFORD].
• non-classical logics [STANFORD].
• lambda calculus [GUPTA] [HARRISON].
• proof planning [BUNDY].
3.1.6. Theorem provers
Three different kinds of provers can be discerned:
• those that want to mimic the human thought processes
• those that do not care about human thought, but try to make optimum use
of the machine
• those that require human interaction.
There are domain specific and general theorem provers. Provers might be
specialised in one mechanism e.g. resolution or they might use a variety of
mechanisms.
In general theorem provers are used to solve difficult or complex problems in
the realm of exact sciences and often those are problems that are difficult to
handle manually.
[NOGIN] gives the following ordering:
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