Experiments with discrimination-tree indexing and path indexing for term retrieval
Journal of Automated Reasoning
Journal of Automated Reasoning
WALDMEISTER - High-Performance Equational Deduction
Journal of Automated Reasoning
RTA '95 Proceedings of the 6th International Conference on Rewriting Techniques and Applications
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
Dedan: A Kernel of Data Structures and Algorithms for Automated Deduction with Equality Clauses
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
On the Evaluation of Indexing Techniques for Theorem Proving
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
The design and implementation of VAMPIRE
AI Communications - CASC
Higher-order term indexing using substitution trees
ACM Transactions on Computational Logic (TOCL)
AISC'10/MKM'10/Calculemus'10 Proceedings of the 10th ASIC and 9th MKM international conference, and 17th Calculemus conference on Intelligent computer mathematics
A content based mathematical search engine: whelp
TYPES'04 Proceedings of the 2004 international conference on Types for Proofs and Programs
Effects of Terms Recognition Mistakes on Requests Processing for Interactive Information Retrieval
International Journal of Information Retrieval Research
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Indexing data structures have a crucial impact on the performance of automated theorem provers. Examples are discrimination trees, which are like tries where terms are seen as strings and common prefixes are shared, and substitution trees, where terms keep their tree structure and all common contexts can be shared. Here we describe a new indexing data structure, called context trees, where, by means of a limited kind of context variables, common subterms also can be shared, even if they occur below different function symbols. Apart from introducing the concept, we also provide evidence for its practical value. We show how context trees can be implemented by means of abstract machine instructions. Experiments with benchmarks for forward matching show that our implementation is competitive with tightly coded current state-of-the-art implementations of the other main techniques. In particular, space consumption of context trees is significantly less than for other index structures.