Document Word Clouds: Visualising Web Documents as Tag Clouds to Aid Users in Relevance Decisions

Περιέχει το πλήρες κείμενοInformation Retrieval systems spend a great effort on determining the significant terms in a document. When, instead, a user is looking at a document he cannot benefit from such information. He has to read the text to understand which words are important. In this paper we take a look at the idea of enhancing the perception of web documents with visualisation techniques borrowed from the tag clouds of Web 2.0. Highlighting the important words in a document by using a larger font size allows to get a quick impression of the relevant concepts in a text. As this process does not depend on a user query it can also be used for explorative search. A user study showed, that already simple TF-IDF values used as notion of word importance helped the users to decide quicker, whether or not a document is relevant to a topic

Analyzing the Evolution of Vocabulary Terms and Their Impact on the LOD Cloud

Vocabularies are used for modeling data in Knowledge Graphs (KGs) like the Linked Open Data Cloud and Wikidata. During their lifetime, vocabularies are subject to changes. New terms are coined, while existing terms are modified or deprecated. We first quantify the amount and frequency of changes in vocabularies. Subsequently, we investigate to which extend and when the changes are adopted in the evolution of KGs. We conduct our experiments on three large-scale KGs: the Billion Triples Challenge datasets, the Dynamic Linked Data Observatory dataset, and Wikidata. Our results show that the change frequency of terms is rather low, but can have high impact due to the large amount of distributed graph data on the web. Furthermore, not all coined terms are used and most of the deprecated terms are still used by data publishers. The adoption time of terms coming from different vocabularies ranges from very fast (few days) to very slow (few years). Surprisingly, we could observe some adoptions before the vocabulary changes were published. Understanding the evolution of vocabulary terms is important to avoid wrong assumptions about the modeling status of data published on the web, which may result in difficulties when querying the data from distributed sources

Science and Technology Issues in the 115th Congress

Science and technology (S&T) have a pervasive influence over a wide range of issues confronting the nation. Public and private research and development spur scientific and technological advancement. Such advances can drive economic growth, help address national priorities, and improve health and quality of life. The constantly changing nature and ubiquity of science and technology frequently create public policy issues of congressional interest. The federal government supports scientific and technological advancement directly by funding and performing research and development and indirectly by creating and maintaining policies that encourage private sector efforts. Additionally, the federal government establishes and enforces regulatory frameworks governing many aspects of S&T activities. This report briefly outlines an array of science and technology policy issues that may come before the 115th Congress. Given the rapid pace of S&T advancement and its importance in many diverse public policy issues, S&T-related issues not discussed in this report may come before the 115th Congress. The selected issues are grouped into 9 categories: - Overarching S&T Policy Issues, - Agriculture, - Biomedical Research and Development, - Defense, - Energy, - Environment and Natural Resources, - Homeland Security, - Information Technology, - Physical and Material Sciences, and - Space. Each of these categories includes concise analysis of multiple policy issues. The material presented in this report should be viewed as illustrative rather than comprehensive. Each section identifies CRS reports, when available, and the appropriate CRS experts to contact for further information and analysis

Providing Alternative Declarative Descriptions for Entity Sets Using Parallel Concept Lattices

We propose an approach for modifying a declarative description of a set of entities (e.g., a SPARQL query) for the purpose of finding alternative declarative descriptions for the entities. Such a shift in representation can help to get new insights into the data, to discover related attributes, or to find a more concise description of the entities of interest. Allowing the alternative descriptions furthermore to be close approximations of the original entity set leads to more flexibility in finding such insights. Our approach is based on the construction of parallel formal concept lattices over different sets of attributes for the same entities. Between the formal concepts in the parallel lattices, we define mappings which constitute approximations of the extent of the concepts. In this paper, we formalise the idea of two types of mappings between parallel concept lattices, provide an implementation of these mappings and evaluate their ability to find alternative descriptions in a scenario of several real-world RDF data sets. In this scenario we use descriptions for entities based on RDF classes and seek for alternative representations based on properties associated with the entities