Linked Data Indexing of Distributed Ledgers

Searching for information in distributed ledgers is currently not an easy task, as information relating to an entity may be scattered throughout the ledger with no index. As distributed ledger technologies become more established, they will increasingly be used to represent real world transactions involving many parties and the search requirements will grow. An index providing the ability to search using domain specific terms across multiple ledgers will greatly enhance to power, usability and scope of these systems. We have implemented a semantic index to the Ethereum blockchain platform, to expose distributed ledger data as Linked Data. As well as indexing block- and transaction-level data according to the BLONDiE ontology, we have mapped smart contracts to the Minimal Service Model ontology, to take the first steps towards connecting smart contracts with Semantic Web Services

Semantic web services for simulation component reuse and interoperability: An ontology approach

Commercial-off-the-shelf (COTS) Simulation Packages (CSPs) are widely used in industry primarily due to economic factors associated with developing proprietary software platforms. Regardless of their widespread use, CSPs have yet to operate across organizational boundaries. The limited reuse and interoperability of CSPs are affected by the same semantic issues that restrict the inter-organizational use of software components and web services. The current representations of Web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging Semantic Web. The authors present new research that partially alleviates the problem of limited semantic reuse and interoperability of simulation components in CSPs. Semantic models, in the form of ontologies, utilized by the authors’ Web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through a simulation component ontology that is used to identify required components at varying levels of granularity (i.e. including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The research presented here is based on the development of an ontology, connector software, and a Web service discovery architecture. The ontology is extracted from simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organizational simulation, by adopting a less intrusive interface between participants Although specific to CSPs this work has wider implications for the simulation community. The reason being that the community as a whole stands to benefit through from an increased awareness of the state-of-the-art in Software Engineering (for example, ontology-supported component discovery and reuse, and service-oriented computing), and it is expected that this will eventually lead to the development of a unique Software Engineering-inspired methodology to build simulations in future

A Semantic Web of Know-How: Linked Data for Community-Centric Tasks

This paper proposes a novel framework for representing community know-how on the Semantic Web. Procedural knowledge generated by web communities typically takes the form of natural language instructions or videos and is largely unstructured. The absence of semantic structure impedes the deployment of many useful applications, in particular the ability to discover and integrate know-how automatically. We discuss the characteristics of community know-how and argue that existing knowledge representation frameworks fail to represent it adequately. We present a novel framework for representing the semantic structure of community know-how and demonstrate the feasibility of our approach by providing a concrete implementation which includes a method for automatically acquiring procedural knowledge for real-world tasks.Comment: 6th International Workshop on Web Intelligence & Communities (WIC14), Proceedings of the companion publication of the 23rd International Conference on World Wide Web (WWW 2014

FABilT – finding answers in a billion triples

This submission presents the application of two coupled systems to the Billion Triples Challenge. The first system (Watson) provides the infrastructure which allows the second one (PowerAqua) to pose natural language queries to the billion triple datasets. Watson is a gateway to the Semantic Web: it crawls and indexes semantic data online to provide a variety of access mechanisms for human users and applications.We show here how we indexed most of the datasets provided for the challenge, thus obtaining an infrastructure (comprising web services, API, web interface, etc.) which supports the exploration of these datasets and makes them available to any Watson-based application. PowerAqua is an open domain question answering system which allows users to pose natural language queries to large scale collections of heterogeneous semantic data. In this paper, we discuss the issues we faced in configuring PowerAqua and Watson for the challenge and report on our results. The system composed of Watson and PowerAqua, and applied to the Billion Triples Challenge, is called FABilT

Structural Regularities in Text-based Entity Vector Spaces

Entity retrieval is the task of finding entities such as people or products in response to a query, based solely on the textual documents they are associated with. Recent semantic entity retrieval algorithms represent queries and experts in finite-dimensional vector spaces, where both are constructed from text sequences. We investigate entity vector spaces and the degree to which they capture structural regularities. Such vector spaces are constructed in an unsupervised manner without explicit information about structural aspects. For concreteness, we address these questions for a specific type of entity: experts in the context of expert finding. We discover how clusterings of experts correspond to committees in organizations, the ability of expert representations to encode the co-author graph, and the degree to which they encode academic rank. We compare latent, continuous representations created using methods based on distributional semantics (LSI), topic models (LDA) and neural networks (word2vec, doc2vec, SERT). Vector spaces created using neural methods, such as doc2vec and SERT, systematically perform better at clustering than LSI, LDA and word2vec. When it comes to encoding entity relations, SERT performs best.Comment: ICTIR2017. Proceedings of the 3rd ACM International Conference on the Theory of Information Retrieval. 201

BlogForever D2.4: Weblog spider prototype and associated methodology

The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype

Pathways: Augmenting interoperability across scholarly repositories

In the emerging eScience environment, repositories of papers, datasets, software, etc., should be the foundation of a global and natively-digital scholarly communications system. The current infrastructure falls far short of this goal. Cross-repository interoperability must be augmented to support the many workflows and value-chains involved in scholarly communication. This will not be achieved through the promotion of single repository architecture or content representation, but instead requires an interoperability framework to connect the many heterogeneous systems that will exist. We present a simple data model and service architecture that augments repository interoperability to enable scholarly value-chains to be implemented. We describe an experiment that demonstrates how the proposed infrastructure can be deployed to implement the workflow involved in the creation of an overlay journal over several different repository systems (Fedora, aDORe, DSpace and arXiv).Comment: 18 pages. Accepted for International Journal on Digital Libraries special issue on Digital Libraries and eScienc

Linked Data - the story so far

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward