Building Enterprise Ready Applications Using Linked Open Data

Abstract. Exploiting open data in the web community is an established movement that is growing these recent years. Government public data is probably the most common and visible part of the later phenomena. What about companies and business data? Even if the kickoff was slow, forward-thinking companies and businesses are embracing semantic technologies to manage their corporate information. The availability of various sources, be they internal or external, the maturity of semantic standards and frameworks, the emergence of big data technologies for managing huge volumes of data have fostered the companies to migrate their internal information systems from traditional silos of corporate data into semantic business data hubs. In other words, the shift from conventional enterprise information management into Linked Opened Data compliant paradigm is a strong trend in enterprise roadmaps. This chapter discusses a set of guidelines and best practices that eases this migration within the context of a corporate application.


Linked Data, Open Data and Linked Open Data (LOD) are three concepts that are very popular nowadays in the semantic community. Various initiatives, like, are gaining ground to promote the openness of data for more transparency of institutions. But what is the difference between these three concepts?

Linked Data refers to the way of structuring the data and creates relationships between them. Open Data similar to open-source, opens content to make it available to citizens, developers, etc. for use with as limited restrictions as possible (legal, technological, financial, license). Linked Open Data, that we refer to as LOD, is the combination of both: to structure data and to make it available for others to be reused.

The LOD paradigm democratized the approach of opening data sources and interlinking content from various locations to express semantic connections like similarity or equivalence relationships for example. In business environment, data interlinking practice is highly recommended for lowering technological and cost barriers of data aggregation processes. In fact, semantic links between data nuggets from separate corporate sources, be they internal or external, facilitate the reconciliation processes between data references, enhance semantic enrichment procedures of data like for example propagating annotations from similar references to incomplete data, etc.

In the context of enterprises, the LOD paradigm opens new scientific and technical challenges to answer emerging semantic requirements in business data integration. The impact of LOD in enterprises can be measured by the deep change that such an approach brings in strategic enterprise processes like domain data workflows. In fact, semantic enrichment and data interlinking contribute to optimize business data lifecycle as they shorten the data integration time and cost. Moreover, when data is semantically managed from its source, i.e. from its acquisition or creation, less time and efforts are required to process

and integrate it in business applications. This semantic management implies a set of procedures and techniques like data identification as resources using uris, metadata annotations using w3c standards, interlink with other data preferably from authority sources or domain taxonomies, etc.

On the other hand, LOD techniques foster the creation of advanced data applications and services by mashing up various heterogeneous content and data:

from internal sources like crm, erp, dbms, filesystems;

from external sources like emails, web sources, social networks, forums.

As a consequence, new perspectives are open to offer innovative channels to consume, exploit and monetize business data and assets. To understand the rationale behind this new perspectives, Fig. 1 depicts a generic enterprise semantic data lifecycle from the acquisition to the final consumption.

Fig. 1. Data workflow in enterprise application

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