305,142 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Neither in the Programs Nor in the Data: Mining the Hidden Financial Knowledge with Knowledge Graphs and Reasoning
No full textVadalog is a logic-based reasoning language for modern AI solutions, in particular for Knowledge Graph (KG) systems. It is showing very effective applicability in the financial realm, with success stories in a vast range of scenarios, including: creditworthiness evaluation, analysis of company ownership and control, prevention of potential takeovers of strategic companies, prediction of hidden links between economic entities, detection of family businesses, smart anonymization of financial data, fraud detection and anti-money laundering. In this work, we first focus on the language itself, giving a self-contained and accessible introduction to Warded Datalog+/-, the formalism at the core of Vadalog, as well as to the Vadalog system, a state-of-the-art KG system. We show the essentials of logic-based reasoning in KGs and touch on recent advances where logical inference works in conjunction with the inductive methods of machine learning and data mining. Leveraging our experience with KGs in Banca d’Italia, we then focus on some relevant financial applications and explain how KGs enable the development of novel solutions, able to combine the knowledge mined from the data with the domain awareness of the business experts
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
No full textThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
Reasoning on company takeovers during the COVID-19 crisis with knowledge graphs
No full textWhen some country takes a disproportionate hit by a large-scale turmoil—just like Italy did during the COVID-19 pandemics—the share prices of its companies plunge. Suddenly, it becomes feasible to attempt foreign takeovers of national assets, including those of strategic interest. To avert this risk, the Government can veto transactions by summoning the so-called “Golden Powers”. Or, it can work to proactively identify structural weaknesses in the control or shareholding chains of key companies, in order to reinforce them without resorting to special powers. Sometimes, vulnerabilities and attacks hide in plain sight due to how complex and intertwined the network of mutual company shareholding is. In this work, we show how to leverage Knowledge Graphs (KGs) as a representation and reasoning framework to analyze both reactive and proactive measures against takeover attempts, however intricate the setting where they take place. We formally characterize a set of reasoning tasks that define when and if to employ Golden Powers, plus others that aim at pinpointing companies prone to attacks. These criteria are exercised on the real network of all Italian companies, built for the occasion. A rich set of experiments is provided, including on several large synthetic instances, to prove the robustness of our method
Mining e-mail content for author identification forensics
No full textWe describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different email topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation
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