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AgreementMakerLight (AML) is an ontology matching system designed with scalability, extensibility and satisfiability as its primary guidelines, as well as an emphasis on the ability to incorporate external knowledge. In OAEI 2019, AML’s development focused mainly on expanding its range of complex matching algorithms, but there were also improvements on its instance matching pipeline and ontology parsing algorithm. AML remains the system with the broadest coverage of OAEI tracks, and among the top performing systems overall. 

Faceted interfaces are omnipresent on the web to support data exploration and filtering. A facet is a triple: a domain (e.g., Book), a property (e.g., author, language), and a set of property values (e.g., Austen, Beauvoir, Coelho, Dostoevsky, Eco, Kerouac, Suskind, ..., French, English, German, Italian, Portuguese, Russian, ... ). Given a property (e.g., language), selecting one or more of its values (English and Italian) returns the domain entities (of type Book) that match the given values (the books that are written in English or Italian). To implement faceted interfaces in a way that is scalable to very large datasets, it is necessary to automate facet extraction. Prior work associates a facet domain with a set of homogeneous values, but does not annotate the facet property. In this paper, we annotate the facet property with a predicate from a reference Knowledge Base (KB) so as to maximize the semantic similarity between the property and the predicate. We define semantic similarity in terms of three new metrics: specificity, coverage, and frequency. Our experimental evaluation uses the DBpedia and YAGO KBs and shows that for the facet annotation problem, we obtain better results than a state-of-the-art approach for the annotation of web tables as modified to annotate a set of values. 

This paper describes TRIPLEX-ST, a novel information extraction system for collecting spatio-temporal information from textual resources. TRIPLEX-ST is based on a distantly supervised approach, which leverages rich linguistic annotations together with information in existing knowledge bases. In particular, we leverage triples associated with temporal and/or spatial contexts, e.g., as available from the YAGO knowledge base, so as to infer templates that capture new facts from previously unseen sentences. 

Cities are actively creating open data portals to enable predictive analytics of urban data. However, the large number of observable patterns that can be extracted by techniques such as Association Rule Mining (ARM) makes the task of sifting through patterns a tedious and time-consuming task. In this paper, we explore the use of domain ontologies to: (i) filter and prune rules that are specific variations of a more general concept in the ontology, and (ii) replace specific rules by a single "general" rule, with the intent to downsize the number of general rules while keeping the semantics of the larger generated set. We show how the combination of several methods reduces significantly the number of rules thus effectively allowing city administrators to use open data to understand patterns, use patterns for decision-making, and better direct limited government resources. 

AgreementMakerLight (AML) is an automated ontology matching system based primarily on element-level matching and on the use of external resources as background knowledge. This paper describes its configuration for the OAEI 2016 competition and discusses its results. For this OAEI edition, we tackled instance matching for the first time, thus expanding the coverage of AML to all types of ontology matching tasks. We also explored OBO logical definitions to match ontologies for the first time in the OAEI. AML was the top performing system in five tracks (including the Instance and instance-based Process Model tracks) and one of the top performing systems in three others (including the novel Disease and Phenotype track, in which it was one of three prize recipients).