Improving Semantic Web Services Discovery Using SPARQL-Based Repository Filtering

Semantic Web Services discovery is commonly a heavyweight task, which has scalability issues when the number of services or the ontology complexity increase, because most approaches are based on Description Logics reasoning. As a higher number of services becomes available, there is a need for solutions that improve discovery performance. Our proposal tackles this scalability problem by adding a preprocessing stage based on two SPARQL queries that filter service repositories, discarding service descriptions that do not refer to any functionality or non-functional aspect requested by the user before the actual discovery takes place. This approach fairly reduces the search space for discovery mechanisms, consequently improving the overall performance of this task. Furthermore, this particular solution does not provide yet another discovery mechanism, but it is easily applicable to any of the existing ones, as our prototype evaluation shows. Moreover, proposed queries are automatically generated from service requests, transparently to the user. In order to validate our proposal, this article showcases an application to the OWL-S ontology, in addition to a comprehensive performance analysis that we carried out in order to test and compare the results obtained from proposed filters and current discovery approaches, discussing the benefits of our proposal

BioSStore: A Client Interface for a Repository of Semantically Annotated Bioinformatics Web Services

Bioinformatics has shown itself to be a domain in which Web services are being used extensively. In this domain, simple but real services are being developed. Thus, there are huge repositories of real services available (for example BioMOBY main repository includes more than 1500 services). Besides, bioinformatics repositories usually have active communities using and working on improvements. However, these kinds of repositories do not exploit the full potential of Web services (and SOA, Service Oriented Applications, in general). On the other hand, sophisticated technologies have been proposed to improve SOA, including the annotation on Web services to explicitly describe them. However, these approaches are lacking in repositories with real services. In the work presented here, we address the drawbacks present in bioinformatics services and try to improve the current semantic model by introducing the use of the W3C standard Semantic Annotations for WSDL and XML Schema (SAWSDL) and related proposals (WSMO Lite). This paper focuses on a user interface that takes advantage of a repository of semantically annotated bioinformatics Web services. In this way, we exploit semantics for the discovery of Web services, showing how the use of semantics will improve the user searches. The BioSStore is available at http://biosstore.khaos.uma.es. This portal will contain also future developments of this proposal

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

A survey of exploratory search systems based on LOD resources

The fact that the existing Web allows people to effortlessly share data over the Internet has resulted in the accumulation of vast amounts of information available on the Web.Therefore, a powerful search technology that will allow retrieval of relevant information is one of the main requirements for the success of the Web which is complicated further due to use of many different formats for storing information. Semantic Web technology plays a major role in resolving this problem by permitting the search engines to retrieve meaningful information. Exploratory search system, a special information seeking and exploration approach, supports users who are unfamiliar with a topic or whose search goals are vague and unfocused to learn and investigate a topic through a set of activities. In order to achieve exploratory search goals Linked Open Data (LOD) can be used to help search systems in retrieving related data, so the investigation task runs smoothly.This paper provides an overview of the Semantic Web Technology, Linked Data and search strategies, followed by a survey of the state of the art Exploratory Search Systems based on LOD.Finally the systems are compared in various aspects such as algorithms, result rankings and explanations

Tsunami-Related Data: A Review of Available Repositories Used in Scientific Literature

Various organizations and institutions store large volumes of tsunami-related data, whose availability and quality should benefit society, as it improves decision making before the tsunami occurrence, during the tsunami impact, and when coping with the aftermath. However, the existing digital ecosystem surrounding tsunami research prevents us from extracting the maximum benefit from our research investments. The main objective of this study is to explore the field of data repositories providing secondary data associated with tsunami research and analyze the current situation. We analyze the mutual interconnections of references in scientific studies published in the Web of Science database, governmental bodies, commercial organizations, and research agencies. A set of criteria was used to evaluate content and searchability. We identified 60 data repositories with records used in tsunami research. The heterogeneity of data formats, deactivated or nonfunctional web pages, the generality of data repositories, or poor dataset arrangement represent the most significant weak points. We outline the potential contribution of ontology engineering as an example of computer science methods that enable improvements in tsunami-related data management

Mobile computing and sensor Web services for coastal buoys

Mobile device technology with the influence of the Internet is creating a lot of Webbased services so that people can have easy and 24-hour access to the services. Recently, the Google’s Android has revolutionized applications development for the mobile platform. As there is an increasing number of companies exposing their services as Web services, enabling flexible mobile access to distributed Web resources is a relevant challenge. However, the current Web is a collection of human readable pages that are unintelligible to computer programs. Semantic Web and Web services have the potential of overcoming this limitation. For this, a standard ontology called Ontology Web Language for Services (OWL-S) is employed. The vision is to automatically discover services like Sensor Web services from mobile. In this thesis, a mobile framework is developed for the automatic discovery of services. The application is implemented for the Coastal Sensor Web and the Semantic Web service

Correcting Knowledge Base Assertions

The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB

Recommender Systems based on Linked Data

Backgrounds: The increase in the amount of structured data published using the principles of Linked Data, means that now it is more likely to find resources in the Web of Data that describe real life concepts. However, discovering resources related to any given resource is still an open research area. This thesis studies Recommender Systems (RS) that use Linked Data as a source for generating recommendations exploiting the large amount of available resources and the relationships among them. Aims: The main objective of this study was to propose a recommendation tech- nique for resources considering semantic relationships between concepts from Linked Data. The specific objectives were: (i) Define semantic relationships derived from resources taking into account the knowledge found in Linked Data datasets. (ii) Determine semantic similarity measures based on the semantic relationships derived from resources. (iii) Propose an algorithm to dynami- cally generate automatic rankings of resources according to defined similarity measures. Methodology: It was based on the recommendations of the Project management Institute and the Integral Model for Engineering Professionals (Universidad del Cauca). The first one for managing the project, and the second one for developing the experimental prototype. Accordingly, the main phases were: (i) Conceptual base generation for identifying the main problems, objectives and the project scope. A Systematic Literature Review was conducted for this phase, which highlighted the relationships and similarity measures among resources in Linked Data, and the main issues, features, and types of RS based on Linked Data. (ii) Solution development is about designing and developing the experimental prototype for testing the algorithms studied in this thesis. Results: The main results obtained were: (i) The first Systematic Literature Re- view on RS based on Linked Data. (ii) A framework to execute and an- alyze recommendation algorithms based on Linked Data. (iii) A dynamic algorithm for resource recommendation based on on the knowledge of Linked Data relationships. (iv) A comparative study of algorithms for RS based on Linked Data. (v) Two implementations of the proposed framework. One with graph-based algorithms and other with machine learning algorithms. (vi) The application of the framework to various scenarios to demonstrate its feasibility within the context of real applications. Conclusions: (i) The proposed framework demonstrated to be useful for develop- ing and evaluating different configurations of algorithms to create novel RS based on Linked Data suitable to users’ requirements, applications, domains and contexts. (ii) The layered architecture of the proposed framework is also useful towards the reproducibility of the results for the research community. (iii) Linked data based RS are useful to present explanations of the recommen- dations, because of the graph structure of the datasets. (iv) Graph-based algo- rithms take advantage of intrinsic relationships among resources from Linked Data. Nevertheless, their execution time is still an open issue. Machine Learn- ing algorithms are also suitable, they provide functions useful to deal with large amounts of data, so they can help to improve the performance (execution time) of the RS. However most of them need a training phase that require to know a priory the application domain in order to obtain reliable results. (v) A log- ical evolution of RS based on Linked Data is the combination of graph-based with machine learning algorithms to obtain accurate results while keeping low execution times. However, research and experimentation is still needed to ex- plore more techniques from the vast amount of machine learning algorithms to determine the most suitable ones to deal with Linked Data