We study the top-k set similarity search problem using semantic overlap. While vanilla overlap requires exact matches between set elements, semantic overlap allows elements that are syntactically different but semantically related to increase the overlap. The semantic overlap is the maximum matching score of a bipartite graph, where an edge weight between two set elements is defined by a user-defined similarity function, e.g., cosine similarity between embeddings. Common techniques like token indexes fail for semantic search since similar elements may be unrelated at the character level. Further, verifying candidates is expensive (cubic versus linear for syntactic overlap), calling for highly selective filters. We propose KOIOS, the first exact and efficient algorithm for semantic overlap search. KOIOS leverages sophisticated filters to minimize the number of required graph-matching calculations. Our experiments show that for medium to large sets less than 5% of the candidate sets need verification, and more than half of those sets are further pruned without requiring the expensive graph matching. We show the efficiency of our algorithm on four real datasets and demonstrate the improved result quality of semantic over vanilla set similarity search.

翻译：我们研究基于语义重叠的Top-k集合相似度搜索问题。传统重叠要求集合元素之间完全匹配，而语义重叠允许语法不同但语义相关的元素增加重叠度。语义重叠定义为二分图的最大匹配分数，其中两个集合元素间的边权重由用户定义的相似度函数（例如嵌入向量的余弦相似度）决定。传统方法（如标记索引）因相似元素在字符层级上可能毫无关联而无法用于语义搜索。此外，候选验证计算成本高昂（语义重叠为立方级复杂度，而语法重叠仅为线性），因此亟需高选择性的过滤机制。我们提出KOIOS——首个精确且高效的语义重叠搜索算法。该算法利用精密的过滤器最小化图匹配计算次数。实验表明，对于中大型集合，需验证的候选集不足5%，其中超过半数可通过过滤机制在不执行昂贵图匹配的情况下进行剪枝。我们在四个真实数据集上验证了算法的效率，并展示了相较于传统集合相似度搜索，语义搜索能显著提升结果质量。