Towards Building a Link Set Backed by Domain Experts using the Alignment Tool

Discovering semantic relations between entities (entity linking) is one of the most important activity for both semantic web and linked data areas. Either we need link sets of instances or concepts we can rely on automatic systems only to a certain extent. As a result, an automatic linking is accompanied with a user interaction which enables to increase the quality of resulted link sets. Often, in order to reach as much quality of link set as possible the user should be a domain expert for an area of linking task. This user specifics should be considered by designers of interactive entity linking tools. This work presents an experience from an experiment of building a link set for two fiscal code lists where domain experts have been involved. The experiment has been done using the Alignment tool

Towards Budget Comparative Analysis: The Need for Fiscal Code Lists as Linked Data

ABSTRACT Code lists are a key part of budget datasets as they serve for the coding of fiscal concepts within them. However, the great diversity of classifications across countries and concepts does not allow to presume upon their actual value, as dimension properties. In this paper we discuss the need for creating code lists Linked Data for the classifications used in fiscal datasets, in three basic steps. First, code lists have to be extracted from fiscal datasets, especially if there are no relevant metadata in the budget description, which could easily identify them. Next, code lists from different datasets or sources have to be represented in the same way, with SKOS vocabulary, thus they can be linked with each other. Finally, linking of similar code lists will also allow the linking of the containing datasets, increasing their data analysis and knowledge extraction possibilities

Alignment: A Hybrid, Interactive and Collaborative Ontology and Entity Matching Service

Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a user-friendly environment for matching two ontologies with the aid of configurable similarity algorithms

Modeling fiscal data with the Data Cube Vocabulary

<p>We present a fiscal data model based on the Data Cube Vocabulary, which we developed for the OpenBudgets.eu project. The model defines component properties out of which data structure definitions for concrete datasets can be composed. Based on initial usage experiments, simple validation constraints have been formulated.</p