Use Of Semantic Annotations Research Articles

Answering recreational web searches with relevant things to do results

Recreational queries from users searching for places to go and things to do or see are very common in web and mobile search. Users specify constraints for what they are looking for, like suitability for kids, romantic ambiance or budget. Queries like “restaurants in New York City” are currently served by static local results or the thumbnail carousel. More complex queries like “things to do in San Francisco with kids” or “romantic places to eat in Seattle” require the user to click on every element of the search engine result page to read articles from Yelp, TripAdvisor, or WikiTravel to satisfy their needs. Location data, which is an essential part of web search, is even more prevalent with location-based social networks and offers new opportunities for many ways of satisfying information seeking scenarios.In this paper, we address the problem of recreational queries in information retrieval and propose a solution that combines search query logs with LBSNs data to match user needs and possible options. At the core of our solution is a framework that combines social, geographical, and temporal information for a relevance model centered around the use of semantic annotations on Points of Interest with the goal of addressing these recreational queries. A central part of the framework is a taxonomy derived from behavioral data that drives the modeling and user experience. We also describe in detail the complexity of assessing and evaluating Point of Interest data, a topic that is usually not covered in related work, and propose task design alternatives that work well.We demonstrate the feasibility and scalability of our methods using a data set of 1B check-ins and a large sample of queries from the real-world. Finally, we describe the integration of our techniques in a commercial search engine.

Evolving semantic annotations through multiple versions of controlled medical terminologies

The extensive use of semantic annotations in the medical domain to enhance information retrieval or encode clinical notes to improving information reuse and sharing demands for high quality annotations generation and services for guaranteeing their validity over time. In this paper we present the extension of an existing framework supporting the (semi-)automatic maintenance of semantic annotations rendered outdated by the evolution of the knowledge organization system they are extracted from. This is done by the adding new rules and improving existing ones to overcome some shortcomings of the framework. We also propose an experimental evaluation of the extension using seven successive versions of four standard controlled terminologies within the domain.

SBML Level 3 Package Proposal: Annotation

AbstractThe annotation of Systems Biology Markup Language (SBML) models with semantic terms has been supported for a number of years. The prevalence of such annotated models is growing, with repositories such as Biomodels.net and an increasing number of software tools supporting and encouraging their use and development.With the increasing use of semantic annotations in the context of systems biology modeling has come the realization that the current Core SBML specification defining their use contains limitations that reduce the scope of metadata that can be captured in such models.SBML Level 3 provides the facility to propose and develop optional extensions to the Core specification. One such extension is described here, with an initial proposal of an Annotation package.This proposal extends the current Core annotation specification to provide support for a richer set of semantic annotations while adhering more closely to the existing specification of Resource Description Framework (RDF).

Retrieval, alignment, and clustering of computational models based on semantic annotations

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

CI-Miner: semantically enhancing scientific processes

The realization of an international cyberinfrastructure of shared resources to overcome time and space limitations is challenging scientists to rethink how to document their processes. Many known scientific process requirements that would normally be considered impossible to implement a few years ago are close to becoming a reality for scientists, such as large scale integration and data reuse, data sharing across distinct scientific domains, comprehensive support for explaining process results, and full search capability for scientific products across domains. This article introduces the CI-Miner approach that can be used to aggregate knowledge about scientific processes and their products through the use of semantic annotations. The article shows how this aggregated knowledge is used to benefit scientists during the development of their research activities. The discussion is grounded on lessons learned through the use of CI-Miner to semantically annotate scientific processes in the areas of geo-sciences, environmental sciences and solar physics: A use case in the field of geo-science illustrates the CI-Miner approach in action.

Semantic-based resource discovery, composition and substitution in IEEE 802.11 mobile ad hoc networks

We present a general framework for resource discovery, composition and substitution in mobile ad-hoc networks, exploiting knowledge representation techniques. Key points of the proposed approach are: (1) reuse of discovery information at network layer in order to build a fully unified semantic-based discovery and routing framework; (2) use of semantic annotations in order to perform the orchestration of elementary resources for building personalized services adopting a concept covering procedure, and to allow the automatic substitution of no more suitable/available components. Using ns-2 simulator, we evaluated performances of the proposed framework with reference to a disaster recovery scenario. In particular, the impact of the number of available services and active clients has been investigated in various mobility conditions and for several service covering threshold levels. Obtained results show that: (1) the proposed framework is highly scalable, given that its overall performance is improved by increasing the number of active clients; (2) the traffic load due to clients is negligible; (3) also for a very small number of available service providers very high hit ratios can be reached; (4) increasing the number of providers can lead to hit ratios very close to 100% at the expense of an increased traffic load. Finally, the effectiveness of cross-layer interaction between routing and resource discovery protocols has been also evaluated and discussed.