This paper presents an approach for metadata reconciliation, curation and linking for Open Governamental Data Portals (ODPs). ODPs have been lately the standard solution for governments willing to put their public data available for the society. Portal managers use several types of metadata to organize the datasets, one of the most important ones being the tags. However, the tagging process is subject to many problems, such as synonyms, ambiguity or incoherence, among others. As our empiric analysis of ODPs shows, these issues are currently prevalent in most ODPs and effectively hinders the reuse of Open Data. In order to address these problems, we develop and implement an approach for tag reconciliation in Open Data Portals, encompassing local actions related to individual portals, and global actions for adding a semantic metadata layer above individual portals. The local part aims to enhance the quality of tags in a single portal, and the global part is meant to interlink ODPs by establishing relations between tags.
Laniado and Mika did a similar analysis over hashtags on Twitter @cite_2 . Their work is focused in answering if Twitter hashtags constitute for the semantic web. To achieve this, four metrics are used: frequency of hashtags; specificity, which is the deviation from the use of them without being a hashtag; consistency; and stability over time.
@cite_2
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Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities (“NSA” and “Edward Snowden” in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag. As the most popular microblogging platform, the vast amount of content on Twitter is constantly growing so that the retrieval of relevant information (streams) is becoming more and more difficult every day. Representing the semantics of individual Twitter activities and modeling the interests of Twitter users would allow for personalization and therewith countervail the information overload. Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well. However, automatically inferring the semantic meaning of Twitter posts is a non-trivial problem. In this paper we investigate semantic user modeling based on Twitter posts. We introduce and analyze methods for linking Twitter posts with related news articles in order to contextualize Twitter activities. We then propose and compare strategies that exploit the semantics extracted from both tweets and related news articles to represent individual Twitter activities in a semantically meaningful way. A large-scale evaluation validates the benefits of our approach and shows that our methods relate tweets to news articles with high precision and coverage, enrich the semantics of tweets clearly and have strong impact on the construction of semantic user profiles for the Social Web. With over 300 million active users, Twitter is among the largest online news and social networking services in existence today. Open access to information on Twitter makes it a valuable source of data for research on social interactions, sentiment analysis, content diffusion, link prediction, and the dynamics behind human collective behaviour in general. Here we use Twitter data to construct co-occurrence language networks based on hashtags and based on all the words in tweets, and we use these networks to study link prediction by means of different methods and evaluation metrics. In addition to using five known methods, we propose two effective weighted similarity measures, and we compare the obtained outcomes in dependence on the selected semantic context of topics on Twitter. We find that hashtag networks yield to a large degree equal results as all-word networks, thus supporting the claim that hashtags alone robustly capture the semantic context of tweets, and as such are useful and suitable for studying the content and categorization. We also introduce ranking diagrams as an efficient tool for the comparison of the performance of different link prediction algorithms across multiple datasets. Our research indicates that successful link prediction algorithms work well in correctly foretelling highly probable links even if the information about a network structure is incomplete, and they do so even if the semantic context is rationalized to hashtags. Twitter enjoys enormous popularity as a micro-blogging service largely due to its simplicity. On the downside, there is little organization to the Twitterverse and making sense of the stream of messages passing through the system has become a significant challenge for everyone involved. As a solution, Twitter users have adopted the convention of adding a hash at the beginning of a word to turn it into a hashtag. Hashtags have become the means in Twitter to create threads of conversation and to build communities around particular interests. In this paper, we take a first look at whether hashtags behave as strong identifiers, and thus whether they could serve as identifiers for the Semantic Web. We introduce some metrics that can help identify hashtags that show the desirable characteristics of strong identifiers. We look at the various ways in which hashtags are used, and show through evaluation that our metrics can be applied to detect hashtags that represent real world entities.