Consuming Linked Data within a Large Educational Organization

Large universities tend to spread their services across several departments to serve their substantial student base. It is very common for this to result in developing different systems, which end up in creating many disconnected data silos within the organization. Data isolation is one of the main bottlenecks that prevent unlocking the full potential behind exploiting such data, to provide a better experience at the level of application deployment and data analysis. The Open University is in the process of connecting their data silos by relying on the Linked Data principles within the LUCERO project. We discuss in this paper three use-cases through which we consume Linked Data produced at the Open University: (1) a student services use-case showing how we exploit data connections to deliver learning material related to courses through the university's main course information website; (2) a mobile course application that enables students to easily explore courses by subject, qualification or research topic; and (3) a Leanback TV service that provides students the ability to watch, with a degree of control, a set of podcasts grouped in different channels. Through these use cases, we highlight in this paper the advantages and effects of consuming Linked Data within an organization

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

Co-evolution of RDF Datasets

Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.Comment: 18 pages, 4 figures, Accepted in ICWE, 201

Hypermedia-based discovery for source selection using low-cost linked data interfaces

Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness

A framework for feeding Linked Data to Complex Event Processing engines

A huge volume of Linked Data has been published on the Web, yet is not processable by Complex Event Processing (CEP) or Event Stream Processing (ESP) engines. This paper presents a frame-work to bridge this gap, under which Linked Data are first translated into events conforming to a lightweight ontology, and then fed to CEP engines. The event processing results will also be published back onto the Web of Data. In this way, CEP engines are connected to the Web of Data, and the ontological reasoning is integrated with event processing. Finally, the implementation method and a case study of the framework are presented

放置自転車問題解決に向けた循環型LOD構築システムの提案

現在，国内では駐輪場施設の不足や問題意識の低さ，違法性の認識不足などのため放置自転車の発生が後を絶たず，地域問題・社会問題となっている．放置自転車は，街の美観を損なうだけでなく，歩行や車両通行の妨げ，交通事故，盗難の原因となっている．こうした放置自転車問題の解決に向けて，日々の放置自転車状況をLinked Open Data（LOD）として公開し，データ基盤を構築することが必要であると考える．このLODを活用することで，自転車放置状況の可視化，最適な駐輪場の設置場所の提示，撤去活動の支援など，放置自転車問題解決に寄与するサービスの開発が可能になる．本研究では放置自転車問題解決に向けて必要なデータを収集し，LODとして統一化して公開し，さらに可視化することで市民の問題意識を向上させて次のデータ収集につなげる循環型システムを提案する．本研究ではまず，放置自転車問題に関する統一的なLODスキーマ設計の方法論を示し，次にSNSから813件の実データと行政のWebサイトから放置自転車の台数に影響を与えるデータを収集した．設計したLODスキーマに基づいて収集したデータをLOD化した．さらに，データ収集の際に生じる欠損をベイジアンネットワークにより推定し，70.3%の精度で欠損値を推定した．推定結果をLODに追加し，最終的に219,804トリプルのLODとしてWeb上に公開した．最後に構築したLODを可視化することで地域住民の問題意識向上と持続的なデータの収集につなげた．本システムにより放置自転車問題解決の一助となる有用なデータセットの構築が確認でき，他の地域課題・社会課題にも適応できる可能性を示した．電気通信大学201