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.

翻译：本文介绍了一种通过倒排索引实现三元组部分或完全匹配来查询三元概念的新方法，用于检索在内涵、外延和/或模态中包含特定术语集合的已计算三元概念。与Ananias中描述的近似方法不同，该方法：(i) 无需保留初始三元语境或其三个二元对应语境，(ii) 通过语境探索避免了在三元组分量上应用推导算子，(iii) 消除了为获取一维查询结果而进行因子化阶段的需求。此外，我们的解决方案引入了一种基于给定查询相似度对检索到的三元概念进行排序的新颖度量指标。最后，通过实证研究主要验证了该方法相较于近似方法的有效性和可扩展性。本方案不仅展现出卓越的效率，更凸显了更优的可扩展性，使其适用于大数据场景。