Figure 3- - uploaded by Alan Freihof Tygel
Content may be subject to copyright.
Different uses of data, with process, summary and examples. For each type, the number of instances (n) found is detailed. Source: Davies (2010)

Different uses of data, with process, summary and examples. For each type, the number of instances (n) found is detailed. Source: Davies (2010)

Source publication
Full-text available
The extensive publishing of data in open formats on the Web seems to be an irreversible tendency. Regarding governments, claims for more transparency coming from the civil society are forcing public administrations to publish data government data through Open Data Portals (ODPs). Hence, it is expected a greater transparency of public administration...

Contexts in source publication

Context 1
... (2010) describes five ways of using open data. Figure 3 shows the categories, and the number of cases gathered on the survey. As a contribution for future research, the author cites some challenges in the social and technical fields. ...
Context 2
... two conclusions we can take are: (i) distribution are skewed, i.e., tails are not symmetric around the mean; and (ii) there might be outliers disturbing the standard deviation. Thus, we show in Figure 29, Figure 30 and Figure 31 the boxplot of TCT, TCT nb and TCT c , respectively. ...
Context 3
... commented before, the better performance of STODaP in Figure 30 and Figure 31 has a direct relation to the precision of the answers. This aspect will be analysed in the following. ...
Context 4
... 32 presents a bar plot of the precision averages. In order to verify the distribution behaviour, a boxplot is presented in Figure 33. ...


... Tags are specific keywords and expressions that represent the dataset content and are widely used on information retrieval (IR) systems. However, they are not always well used, because the process of tag attribution is mainly manually driven by portal administrators and data publishers [41], or informally by users [3], resulting in arbitrary choices, weak expressiveness and, sometimes, ambiguity. This poor quality of tags is a limitation that hampers the access of society to OD. ...
... In [41], was developed a tool to clean, enrich, integrate and reuse language independent tags from multiple portals of a common domain. Using a controlled vocabulary, problems with synonyms, singular and plural nouns, as well as variations in terms orthography, were reduced. ...
Conference Paper
This paper proposes an approach for semantic enrichment of dataset tags through the assignment of terms extracted from the dataset content and the association with meaningful external resources complementing existing tags originally attributed. In this approach, a RDF summary graph is generated to support datasets retrieval through the tags graph exploration. The motivation of this study is the need to improve datasets findability on Open Data Portals through the generation of a richer set of interlinked tags. The semantic enrichment approach is divided in four main steps, comprising cleaning, terms extraction and ranking, linking to associated ontologies or vocabularies terms, and the summarization in graph form, providing tag exploration to find other relevant datasets through tag connections. For the process we developed the Relevant Tag Extractor (RTagE), a semi-automatic software that extracts terms from a dataset, ranks and associates them with external resources. We exemplify the approach with datasets from a Web portal about the use of agrochemicals in agriculture, assigning enriched terms from the AGROVOC thesaurus as dataset tags.
... An open data portal, in this context, is a collection of datasets, which can be owned by governments, non-government organizations, universities or other institutions. Open data portals are administrated by authorized users, who are in charge of uploading resources and filling metadata fields [33]. ...
Conference Paper
Citizens and developers are gaining broad access to public data sources, made available in open data portals. These machine-readable datasets enable the creation of applications that help the population in several ways, giving them the opportunity to actively participate in governance processes, such as decision taking and policy-making. While the number of open data portals grows over the years, researchers have been able to identify recurrent problems with the data they provide, such as lack of data standards, difficulty in data access and poor understandability. Such issues make difficult the effective use of data. Several works in literature propose different approaches to mitigate these issues, based on novel or well-known data management techniques. However, there is a lack of general frameworks for tackling these problems. On the other hand, data governance has been applied in large companies to manage data problems, ensuring that data meets business needs and become organizational assets. In this paper, firstly, we highlight the main drawbacks pointed out in literature for government open data portals. Eventually, we bring around how data governance can tackle much of the issues identified.
The fostering of data opening has been largely motivated by sets of laws on access to information, which establish the need to make data related to governmental activities available to citizens and society in general, as well as results from business processes or scientific research, also for accountability and transparency. There are several ways of making data available to the public, from a simple website to sophisticated applications for accessing the data. In this context, one of the options is the construction of an open data portal using platforms for data repositories and catalogs. In the last few years, there has been a rapid proliferation of this type of portals, with domain or organization specific datasets being widely disseminated in platforms like CKAN. In these platforms, datasets are organized in thematic groups and described by keywords and other attributes assigned by the publisher. Usually described by metadata with poor semantics, these datasets very often remain as “data silos”, with no explicit connection or data integration mechanism, making it difficult for the users to locate and interrelate relevant data sources. In contrast, the Semantic Web focuses on a way of modeling and representing data in an easier manner to establish interrelationships between data, accompanied by richer descriptors. Based on this scenario, this paper proposes LigADOS, an approach to create interconnections between datasets considering their content and related metadata. LigADOS is based on the principles of the Semantic Web and associated linked data solutions and technologies, to support rich access strategies to RDF data published using portal platforms like CKAN and others.