1,721,038 research outputs found
Crowdsourcing tasks within linked data management
No full textMany aspects of Linked Data management – including exposing legacy data and applications to semantic formats, designing vocabularies to describe RDF data, identifying links between entities, query processing, and data curation– are necessarily tackled through the combination of human effort with algorithmic techniques. In the literature on traditional data management the theoretical and technical groundwork to realize and manage such combinations is being established. In this paper we build upon and extend these ideas to propose a framework by which human and computational intelligence can co-exist by augmenting existing Linked Data and Linked Service technology with crowdsourcing functionality. Starting from a motivational scenario we introduce a set of generic tasks which may feasibly be approached using crowdsourcing platforms such as Amazon’s Mechanical Turk, explain how these tasks can be decomposed and translated into MTurk projects, and roadmap the extensions to SPARQL, D2RQ/R2R and Linked Data browsing that are required to achieve this vision
Deriving human-readable labels from SPARQL queries
No full textOver 80% of entities on the Semantic Web lack a human-readable label. This hampers the ability of any tool that uses linked data to offer a meaningful interface to human users. We argue that methods for deriving human-readable labels are essential in order to allow the usage of the Web of Data. In this paper we explore, implement, and evaluate a method for deriving human-readable labels based on the variable names used in a large corpus of SPARQL queries that we built from a set of log files. We analyze the structure of the SPARQL graph patterns and offer a classification scheme for graph patterns. Based on this classification, we identify graph patterns that allow us to derive useful labels. We also provide an overview over the current usage of SPARQL in the newly built corpus
Revisiting reverts: accurate revert detection in Wikipedia
No full textWikipedia is commonly used as a proving ground for research in collaborative systems. This is likely due to its popularity and scale, but also to the fact that large amounts of data about its formation and evolution are freely available to inform and validate theories and models of online collaboration. As part of the development of such approaches, revert detection is often performed as an important pre-processing step in tasks as diverse as the extraction of implicit networks of editors, the analysis of edit or editor features and the removal of noise when analyzing the emergence of the content of an article. The current state of the art in revert detection is based on a rather naive approach, which identifies revision duplicates based on MD5 hash values. This is an efficient, but not very precise technique that forms the basis for the majority of research based on revert relations in Wikipedia. In this paper we prove that this method has a number of important drawbacks - it only detects a limited number of reverts, while simultaneously misclassifying too many edits as reverts, and not distinguishing between complete and partial reverts. This is very likely to hamper the accurate interpretation of the findings of revert-related research. We introduce an improved algorithm for the detection of reverts based on word tokens added or deleted to addresses these drawbacks. We report on the results of a user study and other tests demonstrating the considerable gains in accuracy and coverage by our method, and argue for a positive trade-off, in certain research scenarios, between these improvements and our algorithm's increased runtime
Labels in the web of data
Full text linkEntities on the Web of Data need to have labels in order to be exposable to humans in a meaningful way. These labels can then be used for exploring the data, i.e., for displaying the entities in a linked data browser or other front-end applications, but also to support keyword-based or natural-language based search over the Web of Data. Far too many applications fall back to exposing the URIs of the entities to the user in the absence of more easily understandable representations such as human-readable labels. In this work we introduce a number of label-related metrics: completeness of the labeling, the efficient accessibility of the labels, unambiguity of labeling, and the multilinguality of the labeling. We report our findings from measuring the Web of Data using these metrics. We also investigate which properties are used for labeling purposes, since many vocabularies define further labeling properties beyond the standard property from RDFS
Towards Ontologies for Formalizing Modularization and Communication in Large Software Systems
No full textLarge software systems are modularized in order to improve manageability. The parts of a software system communicate in order to achieve the desired functionality. To better understand, develop, manage, and maintain the resulting complexity, this paper presents a framework of ontologies. The ontologies range from very general, foundational ones to ontologies that elucidate the specificities of particular modularization and communication paradigms. We support two specific paradigms. First, we define an ontology for software components that may be used in traditional middleware architectures, e.g., application servers. Second, we specify an ontology for Web services. Through the reuse of existing foundational ontologies and our new Core Software Ontology, our proposal offers several advantages. In particular, it avoids the typical shortcomings related approaches exhibit and it allows for the concise definition of commonalities and differences of the two paradigms
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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