1,720,970 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Knowledge Graph Question Answering with Generative Language Models
No full textA Knowledge Graph (KG) is a data structure that stores information about the world in the form of nodes and edges. The nodes represent people, places, things etc., while the edges store the relationships between the nodes. The nodes are also known as entities, while the edges are known as relations or predicates. Several popular search engines today make use of such KGs in the background. Some well-known and freely available KGs are DBpedia and Wikidata.
One way to access information from a KG is through Question Answering. For example, web-based search engines today give people the ability to type their questions and receive answers. Unfortunately, the current state of search engines leaves much to be desired in the complexity of the questions that a user may type. Current search engines work best when the search term is a keyword or a set of words. Processing complete sentences, with complex logical rules, is still an open problem.
One large step in the direction of language understanding has been the arrival of pre-trained Language Models, such as BERT. Such models have been trained on large amounts of text corpus, and surprisingly, some variants of these models, such as T5 and BART, develop a remarkable ability to generate text, the likes of which are difficult to distinguish from that produced by a human author. These models are also called generative Language Models and are a central focus of this thesis.
Given a question by a user, how does one fetch an answer from the KG? This task is commonly known as Knowledge Graph Question Answering (KGQA). One of the techniques is to convert the user's question to a logical form, or a structured query. One popular query language the reader might be familiar with is SQL. SQL, though, is appropriate for relational databases. In the KG world, the analogue would be a language called SPARQL. The task of converting the natural language text to a logical form is known as semantic parsing.
To be able to execute a SPARQL query on a KG, the SPARQL schema must be valid, e.g., it must be syntactically correct, and it should be logically correct, e.g., one can not expect the correct answer if AND is replaced with an OR. The other requirement is that the constants in the query, such as entity and relation IDs, have to be placed in the correct manner in the query.
In this thesis, we explore the abilities of generative Language Models (LMs) in the task of KGQA, with a focus on the semantic parsing approach.
This dissertation hypothesizes that generative LMs can be used effectively for the task of KGQA. We form two research questions over this hypothesis, and to answer these research questions, a series of five topically interconnected publications are presented in a cumulative fashion. We first test two popular generative LMs on the task of semantic parsing. We compare the performance of these models to their non-pre-trained predecessors. To this end, we utilize some well-known and openly available datasets, which address well-known KGs. Later, we develop methods to improve the default performance of such models on this task.
This thesis shows that while generative LMs are not ideal for the semantic parsing task in their default mode, special text-handling mechanisms can be incorporated into the model to improve their performance considerably. With these adaptations, the models produce state-of-the-art performance across five datasets that address four different KGs.
This thesis provides researchers in this field with a set of tools and techniques to work with generative LMs and adapt them appropriately to the task of KGQA. We show in subsequent chapters, that our findings have been used by contemporary researchers to further advance the state-of-the-art. In the end, we produce a new KGQA dataset, built over a smaller domain-specific KG. On this dataset, a challenge was organized in which seven participating teams tried the latest methods, and further pushed the state-of-the-art for this task. All the code and data used and developed in this thesis have been released as open-source
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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