Automatic generation of natural language service descriptions from OWL-S service descriptions

As the web grows in both size and diversity, there is an increased need to automate aspects of its use such as service coordination (e.g., discovery, composition and execution). Semantic web services combine semantic web and web service technologies, providing the support for automatic service coordination. Semantic web services are described using semantic languages (e.g., OWL-S) and can be automatically processed by intelligent agents (agent based coordination). This dissertation aims at enhancing the service coordination process, building upon well-understood and widespread practices on natural language generation. Automated service coordination relies on the existence of formal service descriptions (semantic languages, such as OWL-S or WSML). The use of web services by people is essentially associated with the discovery, composition and execution of services that match their needs. According to the person’s will, the discovered or composed service is or is not executed. This decision can only be made if the person understands the description of the service. Therefore, it is necessary that formal descriptions be converted into more natural descriptions, adequate to human comprehension. This dissertation contributes to empower the users (knowledge engineers and common citizens) of service coordination systems with the capability to better understand and decide about discovered or composed services without the need of understanding the formal language in which the semantic web service is described. We implemented a software program capable of generating natural language service descriptions from OWL-S description. It is a template-based natural language generation system that receives the OWL-S description of a service as input and converts it into an English description. This system will leverage the use of service coordination technology by people and allow them to have a more active role in the various stages of the service coordination process

Semantic model-driven development of web service architectures.

Building service-based architectures has become a major area of interest since the advent of Web services. Modelling these architectures is a central activity. Model-driven development is a recent approach to developing software systems based on the idea of making models the central artefacts for design representation, analysis, and code generation. We propose an ontology-based engineering methodology for semantic model-driven composition and transformation of Web service architectures. Ontology technology as a logic-based knowledge representation and reasoning framework can provide answers to the needs of sharable and reusable semantic models and descriptions needed for service engineering. Based on modelling, composition and code generation techniques for service architectures, our approach provides a methodological framework for ontology-based semantic service architecture

Context constraint integration and validation in dynamic web service compositions

System architectures that cross organisational boundaries are usually implemented based on Web service technologies due to their inherent interoperability benets. With increasing exibility requirements, such as on-demand service provision, a dynamic approach to service architecture focussing on composition at runtime is needed. The possibility of technical faults, but also violations of functional and semantic constraints require a comprehensive notion of context that captures composition-relevant aspects. Context-aware techniques are consequently required to support constraint validation for dynamic service composition. We present techniques to respond to problems occurring during the execution of dynamically composed Web services implemented in WS-BPEL. A notion of context { covering physical and contractual faults and violations { is used to safeguard composed service executions dynamically. Our aim is to present an architectural framework from an application-oriented perspective, addressing practical considerations of a technical framework

A Process Framework for Semantics-aware Tourism Information Systems

The growing sophistication of user requirements in tourism due to the advent of new technologies such as the Semantic Web and mobile computing has imposed new possibilities for improved intelligence in Tourism Information Systems (TIS). Traditional software engineering and web engineering approaches cannot suffice, hence the need to find new product development approaches that would sufficiently enable the next generation of TIS. The next generation of TIS are expected among other things to: enable semantics-based information processing, exhibit natural language capabilities, facilitate inter-organization exchange of information in a seamless way, and evolve proactively in tandem with dynamic user requirements. In this paper, a product development approach called Product Line for Ontology-based Semantics-Aware Tourism Information Systems (PLOSATIS) which is a novel hybridization of software product line engineering, and Semantic Web engineering concepts is proposed. PLOSATIS is presented as potentially effective, predictable and amenable to software process improvement initiatives

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201

Modeling, Simulation and Emulation of Intelligent Domotic Environments

Intelligent Domotic Environments are a promising approach, based on semantic models and commercially off-the-shelf domotic technologies, to realize new intelligent buildings, but such complexity requires innovative design methodologies and tools for ensuring correctness. Suitable simulation and emulation approaches and tools must be adopted to allow designers to experiment with their ideas and to incrementally verify designed policies in a scenario where the environment is partly emulated and partly composed of real devices. This paper describes a framework, which exploits UML2.0 state diagrams for automatic generation of device simulators from ontology-based descriptions of domotic environments. The DogSim simulator may simulate a complete building automation system in software, or may be integrated in the Dog Gateway, allowing partial simulation of virtual devices alongside with real devices. Experiments on a real home show that the approach is feasible and can easily address both simulation and emulation requirement

Semantic processing of EHR data for clinical research

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.Comment: Accepted for publication in Journal of Biomedical Informatics, 2015, preprint versio