Competitor data constitutes information significantly valuable for many business applications. Meltwater provides users with access to a large Company Information System (CIS), Owler, which contains competitor pairs and other useful information about companies. Meltwater has been seeking a practical solution to discover more competitor pairs in Owler. The first attempt, a fully-manual workflow (called MW_Manual) for finding more competitor pairs in Owler consisted of two manual steps: a filtering step that excludes obvious non-competitor company pairs, and a further inspection process that inspects each left company pair after the filtering step. MW_Manual was cost prohibitive because the results of the filtering step contained too many non-competitor pairs. Inspecting such non-competitor pairs caused an overhead to the overall workload. To reduce the manual workload, especially the required human effort in the manual inspection process, Meltwater has transformed MW_Manual into a semi-automatic workflow (called MW_CPFilter) by replacing the manual filtering with an automatic yet more precise process that adopts a system called CPFilter. This paper presents CPFilter, a system used in the filtering process of MW_CPFilter. CPFilter automatically pre-computes likely competitor pairs from existing competitor pairs in Owler. CPFilter combines (i) the generation of new competitor candidate pairs by inference from existing competitors and other company-specific knowledge, with (ii) the validation of each candidate competitor pair of two companies by checking whether or not empirical evidence that indicates the competitor relationships of these two companies can be found. CPFilter has three key advantages compared with the manual filtering process and previous works: (i) it resulted in a high workload reduction rate of 0.81, (ii) it is domain-independent so that it can be applied to different sectors in Owler, and (iii) its results are explainable so that humans can easily understand its results.

After decades of fruitful research, description logics (DLs) have evolved into a de facto standard in logic-based knowledge representation. In particular, they serve as the formal basis of the standardized and very popular web ontology language (OWL), which also comes with the advantage of readily available user-friendly modeling tools and optimized reasoning engines. In the course of the wide-spread adoption of OWL and DLs, situations have been observed where logically less skilled practitioners are (ab)using these formalisms as constraint languages adopting a closed-world assumption, contrary to the open-world semantics imposed by the classical definitions and the standards. To provide a clear theoretical basis and inferencing support for this often practically reasonable “off-label use” we propose an alternative formal semantics reflecting the intuitive understanding of such scenarios. To that end, we introduce the fixed-domain semantics and argue that this semantics gives rise to an interesting new inferencing task: model enumeration. We describe how the new semantics can be axiomatized in very expressive DLs. We thoroughly investigate the complexities for standard reasoning as well as query answering under the fixed-domain semantics for a wide range of DLs. Further, we present an implementation of a fixed-domain DL reasoner based on a translation into answer set programming (ASP) which is competitive with alternative approaches for standard reasoning tasks and provides the added functionality of model enumeration.