Similarity-Based Retrieval with Structure-Sensitive Sparse Binary Distributed Representations

We present an approach to similarity-based retrieval from knowledge bases that takes into account both the structure and semantics of knowledge base fragments. Those fragments, or analogues, are represented as sparse binary vectors that allow a computationally efficient estimation of structural and semantic similarity by the vector dot product. We present the representation scheme and experimental results for the knowledge base that was previously used for testing of leading analogical retrieval models MAC/FAC and ARCS. The experiments show that the proposed single-stage approach provides results compatible with or better than the results of two-stage models MAC/FAC and ARCS in terms of recall and precision. We argue that the proposed representation scheme is useful for large-scale knowledge bases and free-structured database applications.