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Abstract This article provides a systematic review of the network formation literature in the public sector. In particular, we code and categorize the theoretical mechanisms used in empirical network research to motivate collaboration and tie formation. Based on a review of the 107 articles on network formation found in 40 journals of public administration and policy from 1998 to 2019, we identify 15 distinct theoretical categories. For each category, we describe the theory, highlight its use in the literature, and identify limitations and concerns with current applications. Overall, we find that most studies rely on a similar set of general theories of network formation. More importantly, we find that most theoretical mechanisms are not well specified, and empirical tests are often unable to directly assess the specific underlying mechanism. The results of our review highlight the need for our field to embrace experimental designs, develop panel network datasets, and engage in more network-level research. 

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.