papers

type paper-id label Paper HealthFinland - Finnish Health 35 Information on the SemanticWeb Paper A semantic case-based reasoning 81 framework for text categorization Paper Application of Ontology 89 Translation Paper EIAW: Towards a Businessfriendly Data Warehouse Using 158 Semantic Web Technologies Paper Spatially Augmented 165 Knowledgebase Paper Unlocking the Potential of Public Sector Information with Semantic 188 Web Technology Paper Matching Patient Records to 195 Clinical Trials Using Ontologies Paper Ontology-based Information Extraction for Business 217 Applications Paper DBpedia: A Nucleus for a Web of 222 Open Data Paper Recipes for Semantic Web Dog Food - The ESWC and ISWC 228 Metadata Projects Paper Purpose-Aware Reasoning about Interoperability of Heterogeneous 232 Training Systems Paper A Collaborative Semantic Web 253 Layer to Enhance Legacy Systems track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track Semantic Web in Use track abstract:single This paper shows how semantic web techniques can be applied to solving problems of distributed content creation, discovery, linking, aggregation, and reuse in health information portals, both from end-user’s and content publishers’s viewpoints. As a case study, the national semantic health portal HEALTHFINLAND is presented. It provides citizens with intelligent searching and browsing services to reliable and up-to-date health information created by various health organizations in Finland. The system is based on a shared semantic metadata schema, ontologies, and mash-up ontology services. The content includes metadata of thousands of web documents such as web pages, articles, reports, campaign information, news, services, and other information related to health. This paper presents a semantic case-based reasoning framework for text categorization. Text categorization is the task of classifying text documents under predened categories. Accidentology is our application eld and the goal of our framework is to classify documents describing real road accidents under predened road accident prototpypes, which also are described by text documents. Accidents are described by accident reports while accident prototypes are described by accident scenarios. Thus, text categorization is done by assigning each accident report to an accident scenario, which highlights particular mechanisms leading to accident. We propose a textual case based reasoning approach (TCBR), which allows us to integrate both textual and domain knowledge aspects in order to carry out this categorization. CBR solves a new problem (target case) by identifying its similarity to one or several previously solved problems (source cases) stored in a case base and by adapting their known solutions. Cases of our framework are created from text. Most of TCBR applications create cases from text by using Information Retrieval techniques, which leads to knowledge-poor descriptions of cases. We show that using semantic resources (two ontology of accidentology) makes possible to overcome this diculty, and allows us to enrich cases by using formal knowledge. An ontology provides a precise specification of the vocabulary used by a community of interest (COI). Multiple communities of interest may describe the same concept using the same or different terms. When such communities interact, ontology alignment and translation is required. This is typically a time consuming process. This paper describes Snoggle, an open source tool designed to ease development of ontology translation rules, and discusses its application to geospatial ontologies. Data warehouse is now widely used in business analysis and decision making processes. To adapt the rapidly changing business environment, we develop a tool to make data warehouses more business-friendly by using Semantic Web technologies. The main idea is to make business semantics explicit by uniformly representing the business metadata (i.e. conceptual enterprise data model and multidimensional model) with an extended OWL language. Then a mapping from the business metadata to the schema of the data warehouse is built. When an analysis request is raised, a customized data mart with data populated from the data warehouse can be automatically generated with the help of this built-in knowledge. This tool, called Enterprise Information Asset Workbench (EIAW), is deployed at the Taikang Life Insurance Company, one of the top five insurance companies of China. User feedback shows that OWL provides an excellent basis for the representation of business semantics in data warehouse, but many necessary extensions are also needed in the real application. The user also deemed this tool very helpful because of its flexibility and speeding up data mart deployment in face of business changes. As an increasing number of applications on the web contain some elements of spatial data, there is a need to efficiently integrate Semantic Web technologies and spatial data processing. This paper describes a prototype system for storing spatial data and Semantic Web data together in a SPatially-AUgmented Knowledgebase (SPAUK) without sacrificing query efficiency. The goals are motivated through use several use cases. The prototype’s design and architecture are described, and resulting performance improvements are discussed. Governments often hold very rich data and whilst much of this information is published and available for re-use by others, it is often trapped by poor data structures, locked up in legacy data formats or in fragmented databases. One of the great benefits that Semantic Web (SW) technology offers is facilitating the large scale integration and sharing of distributed data sources. At the heart of information policy in the UK, the Office of Public Sector Information (OPSI) is the part of the UK government charged with enabling the greater re-use of public sector information. This paper described the actions, findings, and lessons learnt from a pilot study, involving several parts of government and the public sector. The aim was to show to government how they can adopt SW technology for the dissemination, sharing and use of its data. This paper describes a large case study that explores the applicability of ontology reasoning to problems in the medical domain. We investigate whether it is possible to use such reasoning to automate common clinical tasks that are currently labor intensive and error prone, and focus our case study on improving cohort selection for clinical trials. An obstacle to automating such clinical tasks is the need to bridge the semantic gulf between raw patient data, such as laboratory tests or specific medications, and the way a clinician interprets this data. Our key insight is that matching patients to clinical trials can be formulated as a problem of semantic retrieval. We describe the technical challenges to building a realistic case study, which include problems related to scalability, the integration of large ontologies, and dealing with noisy, inconsistent data. Our solution is based on the SNOMED CT ontology, and scales to one year of patient records (approx. 240,000 patients). Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of bulding a complete end-to-end solution. DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information can be made available on the Web for humans and machines. We describe some emerging applications from the DBpedia community and show how website operators can reduce costs by facilitating royalty-free DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data sources. Semantic Web conferences such as ESWC and ISWC offer prime opportunities to test and showcase semantic technologies. Conference metadata about people, papers and talks is diverse in nature and neither too small to be uninteresting or too big to be unmanageable. Many metadata-related challenges that may arise in the Semantic Web at large are also present here. Metadata must be generated from sources which are often unstructured and hard to process, and may originate from many different players, therefore suitable workflows must be established. Moreover, the generated metadata must use appropriate formats and vocabularies, and be served in a way that is consistent with the principles of linked data. This paper reports on the metadata efforts from ESWC and ISWC, identifies specific issues and barriers encountered during the projects, and discusses how these were approached. Recommendations are made as to how these may be addressed in the future, and we discuss how these solutions may generalize to metadata production for the Semantic Web at large. We describe a novel approach by which software can assess the ability of a confederation of heterogeneous systems to interoperate to achieve a given purpose. The approach uses ontologies and knowledge bases (KBs) to capture the salient characteristics of systems, on the one hand, and of tasks for which these systems will be employed, on the other. Rules are used to represent the conditions under which the capabilities provided by systems can fulfill the capabilities needed to support the roles and interactions that make up each task. An Analyzer component employs these KBs and rules to determine if a given confederation will be adequate, to generate suitable confederations from a collection of available systems, to pre-diagnose potential interoperability problems that might arise, and to suggest system configuration options that will help to make interoperability possible. We have demonstrated the feasibility of this approach using a prototype Analyzer and KBs. Note: Approval of this paper by the Government client is pending. If approval is not forthcoming, it must be withdrawn. This paper introduces a framework to add a semantic web layer to legacy organizational information, and describes its application to the use case provided by the Italian National Research Council (CNR) intraweb. Building on a traditional web-based view of information from different legacy databases, we have performed a semantic porting of data into a knowledge base, dependent on an OWL domain ontology. We have enriched the knowledge base by means of text mining techniques, in order to discover on-topic relations. Several reasoning techniques have been applied, in order to infer relevant implicit relationships. Finally, the ontology and the knowledge base have been deployed on a semantic wiki by means of the WikiFactory tool, which allows users to browse the ontology and the knowledge base, to introduce new relations, to revise wrong assertions in a collaborative way, and to perform semantic queries. In our experiments, we have been able to easily implement several functionalities, such as expert finding, by simply formulating ad-hoc queries from either an ontology editor or the semantic wiki interface. The result is an intelligent and collaborative front end, which allow users to add information, fill author subject Eero Hyvnen*; Kim Viljanen; Osma Suominen Application*; Framework; Ontology; Industry: Public Sector Valentina Ceausu*; Sylvie Desprès Application*; Tool; Framework; Ontology; Industry: Transport / Logistics James Ressler*; Mike Dean; Edward Benson; Eric Dorner; Chuck Morris Application*; Ontology Guotong Xie; Yang Yang; Shengping Liu*; Zhaoming Qiu; Xiongzhi Zhou Application*; Tool; Ontology; Industry: Banking / Finance Dave Kolas*; Troy Self Framework*; Industry: Other Harith Alani*; David Dupplaw; John Sheridan; Kieron O'Hara; John Darlington; Nigel Shadbolt; Carol Tullo None of the above; Industry: Public Sector* Kavitha Srinivas*; Chintan Patel; James Cimino; Li Ma; Julian Dolby; Achille Fokoue; Aditya Kalyanpur; Aaron Kershenbaum; Edith Schonberg Ontology; Industry: Pharmaceuticals / Health* Horacio Saggion*; Kalina Bontcheva; Adam Funk; Diana Maynard Application*; Tool; Ontology; Industry: Banking / Finance Sören Auer*; Chris Bizer; Jens Lehmann; Georgi Kobilarov; Richard Cyganiak; Zachary Ives Application; Tool; Framework; Ontology* Knud Möller*; Tom Heath; Siegfried Handschuh; John Domingue Application*; Ontology Daniel Elenius*; Mark Johnson; Reginald Ford; Grit Denker; David Martin Application*; Tool; Problem Statement; Ontology Aldo Gangemi*; Alfio Gliozzo; Valentina Presutti Application; Tool; Framework*; Ontology; Industry: Public Sector