In this paper an ontological representation and reasoning paradigm has been proposed for interpretation of time-series signals. The signals come from sensors observing a smart environment. The signal chosen for the annotation process is a set of unintuitive and complex gas sensor data. The ontology of this paradigm is inspired form the SSN ontology (Semantic Sensor Network) and used for representation of both the sensor data and the contextual information. The interpretation process is mainly done by an incremental ASP solver which as input receives a logic program that is generated from the contents of the ontology. The contextual information together with high level domain knowledge given in the ontology are used to infer explanations (answer sets) for changes in the ambient air detected by the gas sensors.

OWL 2 EL is a popular ontology language that supports role inclusions---that is, axioms that capture compositional properties of roles. Role inclusions closely correspond to context-free grammars, which was used to show that answering conjunctive queries (CQs) over OWL 2 EL knowledge bases with unrestricted role inclusions is undecidable. However, OWL 2 EL inherits from OWL 2 DL the syntactic regularity restriction on role inclusions, which ensures that role chains implying a particular role can be described using a finite automaton (FA). This is sufficient to ensure decidability of CQ answering; however, the FAs can be worst-case exponential in size so the known approaches do not provide a tight upper complexity bound. In this paper, we solve this open problem and show that answering CQs over OWL 2 EL knowledge bases is PSPACE-complete in combined complexity (i.e., the complexity measured in the total size of the input). To this end, we use a novel encoding of regular role inclusions using bounded-stack pushdown automata---that is, FAs extended with a stack of bounded size. Apart from theoretical interest, our encoding can be used in practical tableau algorithms to avoid the exponential blowup due to role inclusions. In addition, we sharpen the lower complexity bound and show that the problem is PSPACE-hard even if we consider only role inclusions as part of the input (i.e., the query and all other parts of the knowledge base are fixed). Finally, we turn our attention to navigational queries over OWL 2 EL knowledge bases, and we show that answering positive, converse-free conjunctive graph XPath queries is PSPACE-complete as well; this is interesting since allowing the converse operator in queries is known to make the problem EXPTIME-hard. Thus, in this paper we present several important contributions to the landscape of the complexity of answering expressive queries over description logic knowledge bases.

Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning.

Originally designed to model text, topic modeling has become a powerful tool for uncovering latent structure in domains including medicine, finance, and vision. The goals for the model vary depending on the application: in some cases, the discovered topics may be used for prediction or some other downstream task. In other cases, the content of the topic itself may be of intrinsic scientific interest. Unfortunately, even using modern sparse techniques, the discovered topics are often difficult to interpret due to the high dimensionality of the underlying space. To improve topic interpretability, we introduce Graph-Sparse LDA, a hierarchical topic model that leverages knowledge of relationships between words (e.g., as encoded by an ontology). In our model, topics are summarized by a few latent concept-words from the underlying graph that explain the observed words. Graph-Sparse LDA recovers sparse, interpretable summaries on two real-world biomedical datasets while matching state-of-the-art prediction performance.

As a story unfolds, it constructs a depiction of events, and at the same time, it also builds conceptual structure at a higher, interpretive level. This higher-level structure provides the terms for understanding the unfolding story, indicating what kinds of features and consequences characterize it – a story ontology . The process by which a tale constructs a story ontology is not straightforward, and in many ways is just as complex as the action at the event level. It involves an interaction between inferred situations and contexts, each with their own networks of terms and structures, which jostle for dominance. I refer to this interaction as governance . In this work, I demonstrate an example of governance at both levels, using a scene from the series Game of Thrones . When the interpretive terms of a story emerge, an understanding of what kinds of events might come next – the possible causal implications – are also conveyed, even if they are unexpected.

We present an implemented approach to transform natural language sentences into SPARQL, using background knowledge from ontologies and lexicons. Therefore, eligible technologies and data storage possibilities are analyzed and evaluated. The contributions of this paper are twofold. Firstly, we describe the motivation and current needs for a natural language access to industry data. We describe several scenarios where the proposed solution is required. Resulting in an architectural approach based on automatic SPARQL query construction for effective natural language queries. Secondly, we analyze the performance of RDBMS, RDF and Triple Stores for the knowledge representation. The proposed approach will be evaluated on the basis of a query catalog by means of query efficiency, accuracy, and data storage performance. The results show, that natural language access to industry data using ontologies and lexicons, is a simple but effective approach to improve the diagnosis process and the data search for a broad range of users. Furthermore, virtual RDF graphs do support the DB-driven knowledge graph representation process, but do not perform efficient under industry conditions in terms of performance and scalability.

We can also leverage medical ontologies/taxonomies to help Traditionally, artificial intelligence in medical applications abstract specific details to concepts that can be more easily has focused on improving the abilities of medical professionals introduced and then later refined when a patient is ready. Additionally, to perform tasks such as diagnosis (e.g., Shortliffe we can have annotations to provide information 1986; Wyatt and Spiegelhalter 1991; Garg et al. 2005; Vihinen about the authoritativeness of content. Furthermore, in many and Samarghitean 2008) or to aid in managing drug interactions cases information will need to travel beyond the patient to (e.g., Bindoff et al. 2007) or side effects (Edwards family or hired caregivers (Williams et al. 2002, p. 387), and Aronson 2000, p. 1258). These efforts target users who which means that multiple explanations will need to be generated have years of medical experience. In contrast, patients often based on the target individual's knowledge. Explanation have limited medical knowledge, and they may be coping generation also involves applications of user modeling with new life-threatening diagnoses that may require a number (e.g.

Subnational policies pertaining to photovoltaic (PV) systems have increased in volume in recent years and federal incentives are set to be phased out over the next few. Understanding how subnational policies function within and across jurisdictions, thereby impacting PV market development, informs policy decision making. This report was developed for subnational policy-makers and researchers in order to aid the analysis on the function of PV system incentives within the emerging PV deployment market. The analysis presented is based on a ‘logic engine’, a database tool using existing state, utility, and local incentives allowing users to see the interrelationships between PV system incentives and parameters, such as geographic location, technology specifications, and financial factors. Depending on how it is queried, the database can yield insights into which combinations of incentives are available and most advantageous to the PV system owner or developer under particular circumstances. This is useful both for individual system developers to identify the most advantageous incentive packages that they qualify for as well as for researchers and policymakers to better understand the patch work of incentives nationwide as well as how they drive the market. In the case of the latter, findings from initial queries identify a limited connection between incentives and market development (based on current data) and point to differing complexities for system developers depending on system owner and size. The entire effort reveals (or possibly reiterates) a critical lack of data on both local policy environments and the structure of market penetration to be able to understand the impact of subnational incentives on the market.

This paper presents an ongoing collaboration to develop a perceptual anchoring framework which creates and maintains the symbol-percept links concerning household objects. The paper presents an approach to non-trivialize the symbol system using ontologies and allow for HRI via enabling queries about objects properties, their affordances, and their perceptual characteristics as viewed from the robot (e.g. last seen). This position paper describes in brief the objective of creating a long term perceptual anchoring framework for HRI and outlines the preliminary work done this far.

We describe a system which monitors the performance of labor channels within a crowdsourcing platform in an online manner. This allows us to automatically determine if and when to switch between labor channels in order to improve overall performance of crowd tasks.