Context-aware, ontology-based, service discovery

Service discovery is a process of locating, or discovering, one or more documents, that describe a particular service. Most of the current service discovery approaches perform syntactic matching, that is, they retrieve services descriptions that contain particular keywords from the user’s query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or syntactically similar but semantically different from the terms in a service description. Another drawback of the existing service discovery mechanisms is that the query-service matching score is calculated taking into account only the keywords from the user’s query and the terms in the service descriptions. Thus, regardless of the context of the service user and the context of the services providers, the same list of results is returned in response to a particular query. This paper presents a novel approach for service discovery that uses ontologies to capture the semantics of the user’s query, of the services and of the contextual information that is considered relevant in the matching process

Semantic Heterogeneity Issues on the Web

The Semantic Web is an extension of the traditional Web in which meaning of information is well defined, thus allowing a better interaction between people and computers. To accomplish its goals, mechanisms are required to make explicit the semantics of Web resources, to be automatically processed by software agents (this semantics being described by means of online ontologies). Nevertheless, issues arise caused by the semantic heterogeneity that naturally happens on the Web, namely redundancy and ambiguity. For tackling these issues, we present an approach to discover and represent, in a non-redundant way, the intended meaning of words in Web applications, while taking into account the (often unstructured) context in which they appear. To that end, we have developed novel ontology matching, clustering, and disambiguation techniques. Our work is intended to help bridge the gap between syntax and semantics for the Semantic Web construction

A linked data compliant framework for dynamic and web-scale consumption of web services

The While Semantic Web Services (SWS) research aims at automating Web service tasks such as discovery, orchestration and execution, its take-up is very limited so far. This is due to several reasons, such as inherent complexity of existing SWS frameworks and the considerable costs involved in creating correct SWS descriptions. In addition, while semantics are in use to enable tasks such as discovery, interaction between service consumers, providers and brokering environments is still not supported by semantic message descriptions. On the other hand, the Linked Data approach has produced a set of established principles for sharing and describing data, such as RDF as representation language and the integral use of dereferencable URIs. In this paper we propose to apply those principles to expose Web services and Web APIs and introduce a framework in which service registries as well as services contribute to the automation of service discovery, and hence, workload is distributed more efficiently. This is achieved by developing a Linked Data compliant Web services framework with that communicate with semi-centralised registries but compute their suitability for a given request themselves. All communications among different framework components are using RDF-based message protocols including service input and output. This framework aims at optimizing load balance and performance by dynamically assembling services at run time in a massively distributed Web environment

Combining SAWSDL, OWL-DL and UDDI for Semantically Enhanced Web Service Discovery

UDDI registries are included as a standard offering within the product suite of any major SOA vendor, serving as the foundation for establishing design-time and run-time SOA governance. Despite the success of the UDDI specification and its rapid uptake by the industry, the capabilities of its offered service discovery facilities are rather limited. The lack of machine-understandable semantics in the technical specifications and classification schemes used for retrieving services, prevent UDDI registries from supporting fully automated and thus truly effective service discovery. This paper presents the implementation of a semantically-enhanced registry that builds on the UDDI specification and augments its service publication and discovery facilities to overcome the aforementioned limitations. The proposed solution combines the use of SAWSDL for creating semantically annotated descriptions of service interfaces and the use of OWL-DL for modelling service capabilities and for performing matchmaking via DL reasoning

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferenc