1,720,967 research outputs found
Large Scale Semantic Matching: Agrovoc vs CABI
Get PDFAchieving semantic interoperability is a difficult problem with a lot of challenges yet to address. Some of them include matching large-scale data sets, tackling the problem of missing background knowledge, evaluating large scale results, tuning the matching process and doing all of the above in a realistic setting with resource and time constraints. In this paper we report the results of a large-scale matching experiment performed on domain-specific resources: two agricultural thesauri. We share the experience concerning the above mentioned aspects of semantic matching, discuss the results, draw conclusions and outline perspective directions of future work
Descriptive Phrases: Understanding Natural Language Metadata
Get PDFFast development of information and communication technologies made available vast amounts of heterogeneous information. With these amounts growing faster and faster, information integration and search technologies are becoming a key for the success of information society. To handle such amounts efficiently, data needs to be leveraged and analysed at deep levels. Metadata is a traditional way of getting leverage over the data. Deeper levels of analysis include language analysis, starting from purely string-based (keyword) approaches, continuing with syntactic-based approaches and now semantics is about to be included in the processing loop. Metadata gives a leverage over the data. Often a natural language, being the easiest way of expression, is used in metadata. We call such metadata "natural language metadata". The examples include various titles, captions and labels, such as web directory labels, picture titles, classification labels, business directory category names. These short pieces of text usually describe (sets of ) objects. We call them "descriptive phrases". This thesis deals with a problem of understanding natural language metadata for its further use in semantics aware applications. This thesis contributes by portraying descriptive phrases, using the results of analysis of several collected and annotated datasets of natural language metadata. It provides an architecture for the natural language metadata understanding, complete with the algorithms and the implementation. This thesis contains the evaluation of the proposed architecture
Computing minimal and redundant mappings between lightweight ontologies
Get PDFThe minimal mapping between two lightweight ontologies contains that minimal subset of mapping elements such that all the others can be efficiently computed from them. They have clear advantages in visualization and user interaction since they are the minimal amount of information that needs to be dealt with. They make the work of the user much easier, faster and less error prone. Experiments on our proposed algorithm to compute them demonstrate a substantial improvement both in run-time and number of elements 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
Lightweight Parsing of Classifications
Get PDFUnderstanding metadata written in natural language is a crucial requirement towards the successful automated integration of large scale, language-rich, classifications such as the ones used in digital libraries. In this article we analyze natural language labels used in such classifications by exploring their syntactic structure, and then we show how this structure can be used to detect patterns of language that can be processed by a lightweight parser whose average accuracy is 96.82%. This allows for a deep understanding of natural language metadata semantics. In particular we show how we improve the accuracy of the automatic translation of classifications into lightweight ontologies by almost 18% with respect to the previously used approach. The automatic translation is required by applications such as semantic matching, search and classification algorithms
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
- …
