We aim to inform the development of decision support tools for resource managers who need to examine large complex ecosystems and make recommendations in the face of many tradeoffs and conflicting drivers. We take a semantic technology approach, leveraging background ontologies and the growing body of open linked data. In previous work, we designed and implemented a semantically-enabled environmental monitoring framework called SemantEco and used it to build a water quality portal named SemantAqua. In this work, we significantly extend SemantEco to include knowledge required to support resource decisions concerning fish and wildlife species and their habitats. Our previous system included foundational ontologies to support environmental regulation violations and relevant human health effects. Our enhanced framework includes foundational ontologies to support modeling of wildlife observation and wildlife health impacts, thereby enabling deeper and broader support for more holistically examining the effects of environmental pollution on ecosystems. Our results include a refactored and expanded version of the SemantEco portal. Additionally the updated system is now compatible with the emerging best in class Extensible Observation Ontology (OBOE). A wider range of relevant data has been integrated, focusing on additions concerning wildlife health related to exposure to contaminants. The resulting system stores and exposes provenance concerning the source of the data, how it was used, and also the rationale for choosing the data. In this paper, we describe the system, highlight its research contributions, and describe current and envisioned usage.

Wikipedia, one of the best-known wikis and the world’s largest free online encyclopedia, has embraced the power of collaborative editing to harness collective intelligence. However, using such a wiki to create high-quality articles is not as easy as people imagine, given for instance the difficulty of reusing knowledge already available in Wikipedia. As a result, the heavy burden of upbuilding and maintaining the ever-growing online encyclopedia still rests on a small group of people. In this article, we aim at facilitating wiki authoring by providing annotation recommendations, thus lightening the burden of both contributors and administrators. We leverage the collective wisdom of the users by exploiting Semantic Web technologies with Wikipedia data and adopt a unified algorithm to support link, category, and semantic relation recommendation. A prototype system named EachWiki is proposed and evaluated. The experimental results show that it has achieved considerable improvements in terms of effectiveness, efficiency and usability. The proposed approach can also be applied to other wiki-based collaborative editing systems.

This paper is concerned with the problems of understanding the relations in automatically extracted semantic datasets such as DBpedia and utilizing them in semantic queries such as SPARQL. Although DBpedia has achieved a great success in supporting c

Semantic Wikipedia aims to enhance Wikipedia by adding explicit semantics to links between Wikipedia entities. However, we have observed that it currently suffers the following limitations: lack of semantic annotations and lack of semantic annotators

Online advertising represents a growing part of the revenues of ma-jor Internet service providers such as Google and Yahoo. A com-monly used strategy is to place advertisements (ads) on the search result pages according to the users ’ submitted queries. Relevant ads are likely to be clicked by a user and to increase the revenues of both advertisers and publishers. However, bid phrases defined by ad-owners are usually contained in limited number of ads. Directly matching user queries with bid phrases often results in finding few appropriate ads. To address this shortcoming, query expansion is often used to increase the chances to match the ads. Nevertheless, query expansion on top of the traditional inverted index faces ef-ficiency issues such as high time complexity and heavy I/O costs. Moreover, precision cannot always be improved, sometimes even hurt due to the involvement of additional noise.

The Web as a global information space is developing from a Web of documents to a Web of data. This development opens new ways for addressing complex information needs. Search is no longer limited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with precise answers as results. In this paper, we demonstrate Hermes, an infrastructure for data web search. To provide an end-user oriented interface, we support expressive keyword search by translating user information needs into structured queries. We integrate heterogeneous web data sources with automatically computed mappings. Schema-level mappings are exploited in constructing structured queries against the integrated schema. These structured queries are decomposed into queries against the local web data sources, which are then processed in a distributed way.