In this paper, we introduce a new method for querying triadic concepts
through partial or complete matching of triples using an inverted index, to
retrieve already computed triadic concepts that contain a set of terms in their
extent, intent, and/or modus. As opposed to the approximation approach
described in Ananias, this method (i) does not need to keep the initial triadic
context or its three dyadic counterparts, (ii) avoids the application of
derivation operators on the triple components through context exploration, and
(iii) eliminates the requirement for a factorization phase to get triadic
concepts as the answer to one-dimensional queries. Additionally, our solution
introduces a novel metric for ranking the retrieved triadic concepts based on
their similarity to a given query. Lastly, an empirical study is primarily done
to illustrate the effectiveness and scalability of our approach against the
approximation one. Our solution not only showcases superior efficiency, but
also highlights a better scalability, making it suitable for big data
scenarios.

通过倒排索引的完全或部分匹配三元组的方法，我们介绍了一种查询三元概念的新方法，以检索包含一组术语在其范围、意图和 / 或模式中的已经计算过的三元概念。与 Ananias 中描述的近似方法相反，我们的方法不需要保留初始的三元上下文或其三个二元对应项，避免了通过上下文探索对三元组成分应用派生运算符，并且消除了通过分解阶段获得三元概念作为一维查询的答案的要求。此外，我们的解决方案引入了一种基于与给定查询的相似性的检索到的三元概念的新指标进行排序的方法。最后，我们主要进行了一项实证研究，以说明我们的方法相对于近似方法的有效性和可扩展性。我们的解决方案不仅展示了更高的效率，还突显了更好的可扩展性，非常适合大数据场景。