The aims of this workshop are to (1) Draw the attention of the AI community to the research challenges and opportunities in semantic cities. (2) Draw the attention on the multidisciplinary dimension and its impact on semantic cities such as transportation, energy, water management. (3) Identify unique issues of this domain and what new techniques may be needed. As example, since governments and citizens are involved data security and privacy are first-class concerns (4) Promoting more cities to become semantic cities (5) Elaborating a (semantic data) benchmark for testing AI techniques on semantic cities. (6) Provide a platform for sharing best-practices and discussion.

Efforts by the pervasive, context-aware system development community have over the years produced a wide variety of context-aware techniques and frameworks. However, a bulk of this technology tends to be strictly tied to a native system, thus largely limiting its external adoption. In addressing this limitation, we introduce an interoperable context representation format, in the form of an ontology, which models core context-aware concepts for re-use within pervasive computing environments. The DCON Context Ontology is proposed as a novel vocabulary for the representation of activity context as experienced by a user, and sensed through one or more of their devices. We demonstrate how, combined with other domain ontologies, DCON provides for richer representations of multi-level context interpretations that are integrated with other known background information about a user.

A major challenge of pervasive context-aware computing and intelligent environments resides in the acquisition and modelling of rich and heterogeneous context data. Decisive aspects of this information are the ongoing human activities at different degrees of granularity. We conjecture that ontology-based activity models are key to support interoperable multilevel activity representation and recognition. In this paper, we report on an initial investigation about the application of probabilistic description logics (DLs) to a framework for the recognition of multilevel activities in intelligent environments. In particular, being based on Log-linear DLs, our approach leverages the potential of highly expressive description logics with probabilistic reasoning in one unified framework. While we believe that this approach is very promising, our preliminary investigation suggests that challenging research issues remain open, including extensive support for temporal reasoning, and optimizations to reduce the computational cost.

Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query rewriting algorithms have been proposed in recent years and implemented in highly optimised reasoning systems. Query rewriting systems are complex software programs; even if based on provably correct algorithms, sophisticated optimisations make the systems more complex and errors become more likely to happen. In this paper, we present an algorithm that, given an ontology as input, synthetically generates ``relevant'' test queries. Intuitively, each of these queries can be used to verify whether the system correctly performs a certain set of ``inferences'', each of which can be traced back to axioms in the input ontology. Furthermore, we present techniques that allow us to determine whether a system is unsound and/or incomplete for a given test query and ontology. Our evaluation shows that most publicly available query rewriting systems are unsound and/or incomplete, even on commonly used benchmark ontologies; more importantly, our techniques revealed the precise causes of their correctness issues and the systems were then corrected based on our feedback. Finally, since our evaluation is based on a larger set of test queries than existing benchmarks, which are based on hand-crafted queries, it also provides a better understanding of the scalability behaviour of each system.

A wealth of ontologies, many of which overlap in their scope, has made aligning ontologies an important problem for the semantic Web. Consequently, several algorithms now exist for automatically aligning ontologies, with mixed success in their performances. Crucial challenges for these algorithms involve scaling to large ontologies, and as applications of ontology alignment evolve, performing the alignment in a reasonable amount of time without compromising on the quality of the alignment. A class of alignment algorithms is iterative and often consumes more time than others while delivering solutions of high quality. We present a novel and general approach for speeding up the multivariable optimization process utilized by these algorithms. Specifically, we use the technique of block-coordinate descent in order to possibly improve the speed of convergence of the iterative alignment techniques. We integrate this approach into three well-known alignment systems and show that the enhanced systems generate similar or improved alignments in significantly less time on a comprehensive testbed of ontology pairs. This represents an important step toward making alignment techniques computationally more feasible.

We tackle the problem of defining a well-founded semantics for Datalog rules with existentially quantified variables in their heads and negations in their bodies. In particular, we provide a well-founded semantics (WFS) for the recent Datalog+/- family of ontology languages, which covers several important description logics (DLs). To do so, we generalize Datalog+/- by non-stratified nonmonotonic negation in rule bodies, and we define a WFS for this generalization via guarded fixed-point logic. We refer to this approach as equality-friendly WFS, since it has the advantage that it does not make the unique name assumption (UNA); this brings it close to OWL and its profiles as well as typical DLs, which also do not make the UNA. We prove that for guarded Datalog+/- with negation under the equality-friendly WFS, conjunctive query answering is decidable, and we provide precise complexity results for this problem. From these results, we obtain precise definitions of the standard WFS extensions of EL and of members of the DL-Lite family, as well as corresponding complexity results for query answering.

As many cities around the world provide access to raw public data along the Open Data movement, many questions arise concerning the accessibility of these data. Various data formats, duplicate identifiers, heterogeneous metadata schema descriptions, and diverse means to access or query the data exist. These factors make it difficult for consumers to reuse and integrate data sources to develop innovative applications. The Semantic Web provides a global solution to these problems by providing languages and protocols for describing and accessing datasets. This paper presents Datalift, a framework and a platform helping to lift raw data sources to semantic interlinked data sources.

Existing robot guides are able to offer a tour of a building, such as a museum, bank, science center, to a single person or to a group of participants. Usually the tours are predefined and there is no support for dynamic interactions between multiple robots. This paper focuses on distributed collaboration between several robot guides providing a building tour to groups of participants. Semantic techniques are adopted in order to formally define the tour topics, available content on a specific topic, and the robot and human profiles including their interests and content knowledge. The robot guides select different topics depending on their participants' interests and prior knowledge. Optimization of the topics of interests is achieved through exchange of participants between the robot guides whenever in each others neighborhood. Evaluation of the implemented algorithms presents a 90% content coverage of relevant topics for the individual participants.

In this work, we build a large scale reasoning engine under temporal RDFS semantics using MapReduce. We identify the major challenges of applying MapReduce framework to reason over temporal information, and present our solutions to tackle them.

Details of our work can be found in the technical report, which is available at http://gqi.limewebs.com/aaaist12.pdf. In this paper, we propose a new operator for revising ABoxes in DL-Lite ontologies.