In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting. 10

It is common practice for data providers to include text descriptions for each column when publishing data sets in the form of data dictionaries. While these documents are useful in helping an end-user properly interpret the meaning of a column in a data set, existing data dictionaries typically are not machine-readable and do not follow a common specification standard. We introduce the Semantic Data Dictionary, a specification that formalizes the assignment of a semantic representation of data, enabling standardization and harmonization across diverse data sets. In this paper, we present our Semantic Data Dictionary work in the context of our work with biomedical data; however, the approach can and has been used in a wide range of domains. The rendition of data in this form helps promote improved discovery, interoperability, reuse, traceability, and reproducibility. We present the associated research and describe how the Semantic Data Dictionary can help address existing limitations in the related literature. We discuss our approach, present an example by annotating portions of the publicly available National Health and Nutrition Examination Survey data set, present modeling challenges, and describe the use of this approach in sponsored research, including our work on a large National Institutes of Health (NIH)-funded exposure and health data portal and in the RPI-IBM collaborative Health Empowerment by Analytics, Learning, and Semantics project.

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Developing agents capable of commonsense reasoning is an important goal in Artificial Intelligence (AI) research. Because commonsense is broadly defined, a computational theory that can formally categorize the various kinds of commonsense knowledge is critical for enabling fundamental research in this area. In a recent book, Gordon and Hobbs described such a categorization, argued to be reasonably complete. However, the theory’s reliability has not been independently evaluated through human annotator judgments. This paper describes such an experimental study, whereby annotations were elicited across a subset of eight foundational categories proposed in the original Gordon-Hobbs theory. We avoid bias by eliciting annotations on 200 sentences from a commonsense benchmark dataset independently developed by an external organization. The results show that, while humans agree on relatively concrete categories like time and space, they disagree on more abstract concepts. The implications of these findings are briefly discussed.

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