  Algebraic characterization of logic programs has received increasing
attention in recent years. Researchers attempt to exploit connections between
linear algebraic computation and symbolic computation in order to perform
logical inference in large scale knowledge bases. This paper proposes further
improvement by using sparse matrices to embed logic programs in vector spaces.
We show its great power of computation in reaching the fixpoint of the
immediate consequence operator from the initial vector. In particular,
performance for computing the least models of definite programs is dramatically
improved in this way. We also apply the method to the computation of stable
models of normal programs, in which the guesses are associated with initial
matrices, and verify its effect when there are small numbers of negation. These
results show good enhancement in terms of performance for computing
consequences of programs and depict the potential power of tensorized logic
programs.
