With increased dependence on efficient use and inclusion of diverse corporate and Web based data sources for business information analysis, financial information providers will increasingly need agile information integration capabilities. Linked Data is a set of technologies and best practices that provide such a level of agility for information integration, access, and use. Current approaches struggle to cope with multiple data sources inclusion in near real-time, and have looked to Semantic Web technologies for assistance with infrastructure access, and dealing with multiple data formats and their vocabularies. This chapter discusses the challenges of financial data integration, provides the component architecture of Web enabled financial data integration and outlines the emergence of a financial ecosystem, based upon existing Web standards usage. Introductions to Semantic Web technologies are given, and the chapter supports this with insight and discussion gathered from multiple financial services use case implementations. Finally, best practice for integrating Web data based on the Linked Data principles and emergent areas are described.

The ready availability of data is leading to the increased opportunity of their re-use for new applications and for analyses. Most of these data are not necessarily in the format users want, are usually heterogeneous, and highly dynamic, and this necessitates data transformation efforts to re-purpose them. Interactive data transformation (IDT) tools are becoming easily available to lower these barriers to data transformation efforts. This paper describes a principled way to capture data lineage of interactive data transformation processes. We provide a formal model of IDT, its mapping to a provenance representation, and its implementation and validation on Google Refine. Provision of the data transformation process sequences allows assessment of data quality and ensures portability between IDT and other data transformation platforms. The proposed model showed a high level of coverage against a set of requirements used for evaluating systems that provide provenance management solutions.

The increasing availability of data on the Web provided by the emergence of Web 2.0 applications and, more recently by Linked Data, brought additional complexity to data management tasks, where the number of available data sources and their associated heterogeneity drastically increases. In this scenario, where data is reused and repurposed on a new scale, the pattern expressed as Extract-Transform-Load (ETL) emerges as a fundamental and recurrent process for both producers and consumers of data on the Web. In addition to ETL, provenance, the representation of source artifacts, processes and agents behind data, becomes another cornerstone element for Web data management, playing a fundamental role in data quality assessment, data semantics and facilitating the reproducibility of data transformation processes. This paper proposes the convergence of these two Web data management concerns, introducing a principled provenance model for ETL processes in the form of a vocabulary based on the Open Provenance Model (OPM) standard and focusing on the provision of an interoperable provenance model for ETL environments. The proposed ETL provenance model is instantiated in a real-world sustainability reporting scenario.

The eXtensible Business Reporting Language (XBRL) has standardised consolidated financial reporting and through its machine readable format facilitates access to and consumption of financial figures contained within the report. Formalising XBRL as RDF facilitates the leveraging of XBRL with Open Financial Data. Previous XBRL to Semantic Web transformations have however concentrated on making the semantics of its logical model explicit to the exclusion of accounting regulatory validation rules and constraints found within the XBRL calculation linkbases. Using off-the-shelf Semantic Web technologies this paper investigates the use of the SPARQL Inferencing Notation (SPIN) with RDF to formalise these accounting regulations found across XBRL jurisdictional taxonomies. Moving beyond previous RDF to XBRL transformations we investigate how SPIN enhanced formalisation enables financial instrument fact inferencing and sophisticated consistency checking. SPIN formalisations are further used to evaluate the correctness of reported financial data against the calculation requirements imposed by accounting regulation. Our approach illustrated through the use of use case demonstrators outlines that SPIN usage meets central requirements for financial constraint regulatory modelling.

Online Analytical Processing (OLAP) promises an interface to analyse Linked Data containing statistics going beyond other interaction paradigms such as follow-your-nose browsers, faceted-search interfaces and query builders. Transforming statistical Linked Data into a star schema to populate a relational database and applying a common OLAP engine do not allow to optimise OLAP queries on RDF or to directly propagate changes of Linked Data sources to clients. Therefore, as a new way to interact with statistics published as Linked Data, we investigate the problem of executing OLAP queries via SPARQL on an RDF store. First, we define projection, slice, dice and roll-up operations on single data cubes published as Linked Data reusing the RDF Data Cube vocabulary and show how a nested set of operations lead to an OLAP query. Second, we show how to transform an OLAP query to a SPARQL query which generates all required tuples from the data cube. In a small experiment, we show the applicability of our OLAP-to-SPARQL mapping in answering a business question in the financial domain.

Analysts spend a disproportionate amount of time with financial data curation before they are able to compare company performances in an analysis. The Extensible Business Reporting Language (XBRL) for annotating financial facts is suited for automatic processing to increase information quality in financial analytics. Still, XBRL does not solve the problem of data integration as required for a holistic view on companies. Semantic Web technologies promise benefits for financial data integration, yet, existing literature lacks concrete case studies. In this paper, we present the Financial Information Observation System (FIOS) that uses Linked Data and multidimensional modelling based on the RDF Data Cube Vocabulary for accessing and representing relevant financial data. FIOS fulfils the information seeking mantra of "overview first, zoom and filter, then details on demand", integrates yearly and quarterly balance sheets, daily stock quotes as well as company and industry background information and helps analysts creating their own analyses with Excel-like functionality.

This work introduces Distributional Relational Networks (DRNs), a Knowledge Representation (KR) framework which focuses on allowing semantic approximations over large-scale and heterogeneous knowledge bases. The proposed model uses the distributional semantics information embedded in large text/data corpora to provide a comprehensive and principled solution for semantic approximation. DRNs can be applied to open domain knowledge bases and can be used as a KR model for commonsense reasoning. Experimental results show the suitability of DRNs as a semantically flexible KR framework.

Within the operational phase buildings are now producing more data than ever before, from energy usage, utility information, occupancy patterns, weather data, etc. In order to manage a building holistically it is important to use knowledge from across these information sources. However, many barriers exist to their interoperability and there is little interaction between these islands of information. As part of moving building data to the cloud there is a critical need to reflect on the design of cloud-based data services and how they are designed from an interoperability perspective. If new cloud data services are designed in the same manner as traditional building management systems they will suffer from the data interoperability problems. Linked data technology leverages the existing open protocols and W3C standards of the Web architecture for sharing structured data on the web. In this paper we propose the use of linked data as an enabling technology for cloud-based building data services. The objective of linking building data in the cloud is to create an integrated well-connected graph of relevant information for managing a building. This paper describes the fundamentals of the approach and demonstrates the concept within a Small Medium sized Enterprise (SME) with an owner-occupied office building.

Using domain knowledge to instruct domain specific Natural Language Processing (NLP) applications requires that knowledge intensive design challenges associated with developing extraction rules and conceptual models from that knowledge be addressed. Applying the nested problem-driven approach of Design Science Research (DSR) assists knowledge problem reduction to practical problems, delivering artifacts of utility. Where artifact design has to facilitate practitioner and research stakeholder expectations, dual leveraging of design process stages and conceived artifacts is required. This paper presents how an existing Information Systems (IS) framework, previously applied to enterprise architecture research, can be adapted to enhance stakeholder engagement within a collaborative informatics research project. The business motivation behind domain specific NLP is explained and design challenges encountered in framework application to use case development, discussed. Further contributions that outline artifact evolution using problem decomposition are made through integrating expert domain knowledge and design knowledge translation as part of the adapted research process.

There has been considerable efforts in modelling the semantics of Internet of Things and their specific context. Acquiring and managing metadata related to the physical devices and their surrounding environment becomes challenging due to the dynamic nature of environment. This paper focuses on managing metadata for Internet of Things with the help of crowds. Specifically, the paper proposes a collaborative approach for collecting and maintaining metadata through micro tasks that can be performed using variety of platforms e.g. mobiles, laptops, kiosks, etc. The approach allows non-experts to contribute towards metadata management through micro tasks, therefore resulting in reduced cost and time. Applicability of the proposed approach is demonstrated through a use case implementation for managing sensor metadata for energy management in small buildings.

Events are encapsulated pieces of information that flow from one event agent to another. In order to process an event, additional information that is external to the event is often needed. This is achieved using a process called event enrichment. Current approaches to event enrichment are external to event processing engines and are handled by specialized agents. Within large-scale environments with high heterogeneity among events, the enrichment process may become difficult to maintain. This paper examines event enrichment in terms of information completeness and presents a unified model for event enrichment that takes place natively within the event processing engine. The paper describes the requirements of event enrichment and highlights its challenges such as finding enrichment sources, retrieval of information items, finding complementary information and its fusion with events. It then details an instantiation of the model using Semantic Web and Linked Data technologies. Enrichment is realised by dynamically guiding a spreading activation algorithm in a Linked Data graph. Multiple spreading activation strategies have been evaluated on a set of Wikipedia events and experimentation shows the viability of the approach.

This paper demonstrates Treo, a natural language query mechanism for Linked Data graphs. The approach uses a distributional semantic vector space model to semantically match user query terms with data, supporting vocabulary-independent (or schema-agnostic) queries over structured data.

Human computation systems are characterized by the use of human workers to solve computationally difficult problems. Expertise profiling involves assessment and representation of a worker’s expertise, in order to route human computation tasks to appropriate workers. This paper studies the relationship between the assessment workload on workers and the quality of task routing. Three expertise assessment approaches were compared with the help of a user study, using two different groups of human workers. The first approach requests workers to provide selfassessment of their knowledge. The second approach measures the knowledge of workers through their performance against tasks with known responses. We propose a third approach based on a combination of selfassessment and task-assessment. The results suggest that the self-assessment approach requires minimum assessment workload from workers during expertise profiling. By comparison, the task-assessment approach achieved the highest response rate and accuracy. The proposed approach requires less assessment workload, while achieving the response rate and accuracy similar to the task-assessment approach. Author Keywords Human computation, expertise assessment, task routing

This paper describes and demonstrates a distributional semantic search service infrastructure for Linked Dataspaces. The center of the approach relies on the use of a distributional semantics infrastructure to provide semantic search and query services over data for users and applications, improving data accessibility over the Dataspace. By accessing the services through a REST API, users can semantically index and search over data using the distributional semantic knowledge embedded in the reference corpus. The use of distributional semantic models, which rely on the automatic extraction from large corpora, supports a comprehensive and approximative semantic matching mechanism with a low associated adaptation cost for the inclusion of new data sources.

The integration of a small fraction of the information present in the Web of Documents to the Linked Data Web can provide a significant shift on the amount of information available to data consumers. However, information extracted from text does not easily fit into the usually highly normalized structure of ontology-based datasets. While the representation of structured data assumes a high level of regularity, relatively simple and consistent conceptual models, the representation of information extracted from texts need to take into account large terminological variation, complex contextual/dependency patterns, and fuzzy or conflicting semantics. This work focuses on bridging the gap between structured and unstructured data, proposing the representation of text as structured discourse graphs (SDGs), targeting an RDF representation of unstructured data. The representation focuses on a semantic best-effort information extraction scenario, where information from text is extracted under a pay-as-you-go data quality perspective, trading terminological normalization for domain-independency, context capture, wider representation scope and maximization of textual information capture.

The demand to access large amounts of heterogeneous structured data is emerging as a trend for many users and applications. However, the effort involved in querying heterogeneous and distributed third-party databases can create major barriers for data consumers. At the core of this problem is the semantic gap between the way users express their information needs and the representation of the data. This work aims to provide a natural language interface and an associated semantic index to support an increased level of vocabulary independency for queries over Linked Data/Semantic Web datasets, using a distributional-compositional semantics approach. Distributional semantics focuses on the automatic construction of a semantic model based on the statistical distribution of co-occurring words in large-scale texts. The proposed query model targets the following features: (i) a principled semantic approximation approach with low adaptation effort (independent from manually created resources such as ontologies, thesauri or dictionaries), (ii) comprehensive semantic matching supported by the inclusion of large volumes of distributional (unstructured) commonsense knowledge into the semantic approximation process and (iii) expressive natural language queries. The approach is evaluated using natural language queries on an open domain dataset and achieved avg. recall=0.81, mean avg. precision=0.62 and mean reciprocal rank=0.49.