A major challenge in the analysis of city data is the integration of data from different sources. This paper defines an ontology, called Open 311 Ontology, that provides a unified terminology and a reference model for representing the 311 data. We illustrate how the ontology can be used to map and integrate data from multiple cities, and for answering competency questions.

We have built KaBOB (Knowledge Base of Biomedicine) by integrating information from over 20 existing biomedical data sources about humans and seven major model organisms. The knowledge base is modeled in OWL and grounded in 14 prominent OBOs (Open Biomedical Ontologies). It is comprised of over 419 million RDF triples. Queries can be posed to KaBOB in terms of biomedical concepts and abstractions, instead of requiring knowledge of source-specific encodings and terminology.

In this paper, we describe the D-SCRIBE process used to build ontologies that are expected to have significant domain expansion after their initial introduction and whose coverage of concepts needs to be validated for a series of related applications. This process has been used to build SCRIBE, a very modular, ambitious ontology for the information about events triggered by both humans or nature, response activities by agencies that provide public services in cities by using resources and assets (land parcels, buildings, vehicles, equipment) and their communication (requests, work orders, sensor reports). SCRIBE reuses concepts from previously existing ontologies and data exchange standards, and D-SCRIBE retains traceability to these source influences.

City Indicators are metrics used to measure city per- formance. Global City Indicators, as developed by the Global Cities Institute at the University of Toronto, are metrics that have been agreed to by over 250 cities world wide and have been approved as ISO 37120. The definitions of the indicators exist only in written form. The purpose of this research is to provide an ontology for representing the definition of these indi- cators and their instantiation by cities worldwide so that they can shared across the Semantic Web. This paper describes the requirements for the ontology and provides an example of its use.

As the complexity and abundance of city data increases, reusable semantic models that can integrate heterogeneous data sources in a lightweight manner enable a holistic view of the city data, which is key to Urban Ecology. Our multi-disciplinary team has built an ontology for Urban Ecology that not only captures a field-validated urban model and certification process, but also explores the reuse of semantic models and their interaction with domain experts.

Instance checking is considered a central service for data retrieval from description logic (DL) ontologies. In this paper, we propose a revised most specific concept (MSC) method for DL SHI}, which converts instance checking into subsumption problems. This revised method can generate small concepts that are specific-enough to answer a given query, and allow reasoning to explore only a subset of the ABox data to achieve efficiency. Experiments show effectiveness of our proposed method in terms of concept size reduction and the improvement in reasoning efficiency.

We study the problem of rewriting a disjunctive datalog program into plain datalog. We show that a disjunctive program is rewritable if and only if it is equivalent to a linear disjunctive program, thus providing a novel characterisation of datalog rewritability. Motivated by this result, we propose weakly linear disjunctive datalog -- a novel rule-based KR language that extends both datalog and linear disjunctive datalog and for which reasoning is tractable in data complexity. We then explore applications of weakly linear programs to ontology reasoning and propose a tractable extension of OWL 2 RL with disjunctive axioms. Our empirical results suggest that many non-Horn ontologies can be reduced to weakly linear programs and that query answering over such ontologies using a datalog engine is feasible in practice.

Ontology-based data access (OBDA) is a new paradigm aiming at accessing and managing data by means of an ontology, i.e., a conceptual representation of the domain of interest in the underlying information system. In the last years, this new paradigm has been used for providing users with abstract (independent from technological and system-oriented aspects), effective, and reasoning-intensive mechanisms for querying the data residing at the information system sources. In this paper we argue that OBDA, besides querying data, provides the right principles for devising a formal approach to data quality. In particular, we concentrate on one of the most important dimensions considered both in the literature and in the practice of data quality, namely consistency. We define a general framework for data consistency in OBDA, and present algorithms and complexity analysis for several relevant tasks related to the problem of checking data quality under this dimension, both at the extensional level (content of the data sources), and at the intensional level (schema of the data sources).

ABox abduction is an important reasoning mechanism for description logic ontologies. It computes all minimal explanations (sets of ABox assertions) whose appending to a consistent ontology enforces the entailment of an observation while keeps the ontology consistent. We focus on practical computation for a general problem of ABox abduction, called the query abduction problem, where an observation is a Boolean conjunctive query and the explanations may contain fresh individuals neither in the ontology nor in the observation. However, in this problem there can be infinitely many minimal explanations. Hence we first identify a class of TBoxes called first-order rewritable TBoxes. It guarantees the existence of finitely many minimal explanations and is sufficient for many ontology applications. To reduce the number of explanations that need to be computed, we introduce a special kind of minimal explanations called representative explanations from which all minimal explanations can be retrieved. We develop a tractable method (in data complexity) for computing all representative explanations in a consistent ontology. xperimental results demonstrate that the method is efficient and scalable for ontologies with large ABoxes.

We study the problem of instance alignment between knowledge bases (KBs). Existing approaches, exploiting the “symmetry” of structure and information across KBs, suffer in the presence of asymmetry, which is frequent as KBs are independently built. Specifically, we observe three types of asymmetries (in concepts, in features, and in structures). Our goal is to identify key techniques to reduce accuracy loss caused by each type of asymmetry, then design Asymmetry-Resistant Instance Alignment framework (ARIA). ARIA uses two-phased blocking methods considering concept and feature asymmetries, with a novel similarity measure overcoming structure asymmetry. Compared to a state-of-the-art method, ARIA increased precision by 19% and recall by 2%, and decreased processing time by more than 80% in matching large-scale real-life KBs.