Browse Prior Art Database

Method and System for Linked Open Data Generation and Publishing

IP.com Disclosure Number: IPCOM000198089D
Publication Date: 2010-Jul-26
Document File: 7 page(s) / 319K

Publishing Venue

The IP.com Prior Art Database

Abstract

We propose a novel way to automatically generate more new LOD based on the exitsing LOD and semantically relevant to the existing LOD. Compared with the known solutions that generate LOD from scratch, our method takes a quite different idea to generate LOD from what-already-existed. Briefly, our solution takes a snowball-rolling style, well leverages the existing LOD data, and based on it propagates more semantically relevant new LOD, which is a standing-up view.

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Method and System for Linked Open Data Generation and Publishing

Linked Open Data (LOD)[4] is a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge. In Linked Open Data, RDF (Resource Framework Description) is used as data source,

which contains

three characteristics: (1) URI takes as the uniform identifier for resources, and resources are connected via dereferenceable URI on the Web . (2) It is in the form of triple pattern ("Subject Predicate Object"). (3) It contains rich explicit/implicit relationship and self-description semantics.

From the perspective of industry, there arise a few promising applications for consumption of Linked Open Data. Examples are linked data navigation, mash-up application for LOD, semantic master data management, and advanced search engine with semantics. It does not lack rich applications to accomodate more linked open data.

Fig.1 Evolving of Linked Open Data world

Fig.2 Linked Open Drug Data in healthcare domain

Fig.1 shows the evolving of Linked Open data, and Fig.2 is an illustration of Linked Open Drug Data(LODD), a case of LOD in healthcare domain.

Although the LOD world is increasingly expanding, the pace of LOD generation is

1

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still very slow.

We don't lack the consumption and application of LOD, but lack the generation of LOD in face of a Web of Data.

The prior art about LOD generation is illustrated as follows:
1) Manually building LOD: Protege[1] is an example as an ontological data editor.
2) Directly transforming (semi-)structured data as virtual RDF view: D2RQ[2] is a famous tool which can publish relational data as virtual RDF view.
3) Extracting LOD from the existing Web of Documents : LDOW08[3]

hierarchical and reference relationship of HTML web pages to construct LOD .

We can observe that all of these methods for LOD generation are standalone , and does not have the capability of data propagation. The so-called data propagation indicates that based on a set of data set,

we can generate more data and expand

the initial set. However, none of the above art can propagate more LOD based the existing LOD ,

. The case is

Why our solution is required?

Quite different from the prior art, our solution provides a novel way to automatically generate more new LOD based on the existing LOD. In this way,

we can

potentially reduce the heavy loads for LOD publishers, 2) increasingly expand the

                           -understandable way, 3) enable more linkage between LOD and further enrich more LOD applications.

This is the potential capability that Web of Data can but Web of Documents cannot, and we try to capture this capability as well as the business opportunity in potential markets.

We propose a novel way to automatically generate more new LOD based on the exitsing LOD and semantically relevant to the existing LOD. Compared with the known solutions that generate LOD from scratch , our method takes...