Relaunch des Standard-Thesaurus Wirtschaft – Dynamik in der Wissensrepräsentation

2018

Int J Digit Libr

Indexing documents with controlled vocabularies enables a wealth of semantic applications for digital libraries. Due to the rapid growth of scientific publications, machine learning based methods are required that assign subject descriptors automatically. While stability of generative processes behind the underlying data is often assumed tacitly, it is being violated in practice. Addressing this problem, this article studies explicit and implicit concept drift, that is, settings with new descriptor terms and new types of documents, respectively. First, the existence of concept drift in automatic subject indexing is discussed in detail and demonstrated by example. Subsequently, architectures for automatic indexing are analysed in this regard, highlighting individual strengths and weaknesses. The results of the theoretical analysis justify research on fusion of different indexing approaches with special consideration on information sharing among descriptors. Experimental results on titles and author keywords in the domain of economics underline the relevance of the fusion methodology, especially under concept drift. Fusion approaches outperformed non-fusion strategies on the tested data sets, which comprised shifts in priors of descriptors as well as covariates. These findings can help researchers and practitioners in digital libraries to

Section: Subject Indexingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Fusion architectures for automatic subject indexing under concept drift

2018

Int J Digit Libr

“…For dict, f • : BR (LR) + maui and f • : BR (SVM) + maui, we additionally applied the transformation described in Section 4 which led to systems in the following denoted by the su x T or transform. Due to the runtime of the quickly realized implementation of transformation rule learning 9 , transformations were only determined based on the dict method on the rst fold and restricted to highlevel categories of the thesaurus. Because the number of examples per category is expected to be high, we suspected that these rules are representative for all data sets and se ings.…”

Section: Con Gurationsmentioning

confidence: 99%

“…ird, terminology in documents and controlled vocabularies might di er or terminology might change over time. For instance, permanent updates of the STW were performed to re ect substantial changes in economicsrelated literature [9]. Consider phrases like "online advertising" or "smartphone" that suddenly appeared since 1990 [18], just to give an example.…”

Section: Introductionmentioning

confidence: 99%

Descriptor-Invariant Fusion Architectures for Automatic Subject Indexing

2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

2017

Documents indexed with controlled vocabularies enable users of libraries to discover relevant documents, even across language barriers. Due to the rapid growth of scienti c publications, digital libraries require automatic methods that index documents accurately, especially with regard to explicit or implicit concept dri , that is, with respect to new descriptor terms and new types of documents, respectively. is paper rst analyzes architectures of related approaches on automatic indexing. We show that their design determines individual strengths and weaknesses and justify research on their fusion. In particular, systems bene t from statistical associative components as well as from lexical components applying dictionary matching, ranking and binary classi cation. e analysis emphasizes the importance of descriptor-invariant learning, that is, learning based on features, which can be transferred between di erent descriptors. eoretic and experimental results on economic titles and author keywords underline the relevance of the fusion methodology in terms of overall accuracy, and adaptability to dynamic domains. Experiments show, that fusion strategies combining a binary relevance approach and a thesaurusbased system outperform all other strategies on the tested data set. Our ndings can help researchers and practitioners in digital libraries to choose appropriate methods for automatic indexing. CCS CONCEPTS •Computing methodologies →Supervised learning; Machine learning; Natural language processing; •Information systems →Digital libraries and archives;

Towards Semantic Quality Control of Automatic Subject Indexing

Research and Advanced Technology for Digital Libraries

2017

Automatic subject indexing is a key technology for digital libraries, however, factors like concept drift hinder its success in practice. Releasing high-quality results into productive retrieval systems may still be possible when thorough quality control is applied, which may support algorithmic improvements and allow to create high precision filters. Since errors and their relevance can depend on characteristics of concepts and their relations, evaluations should take semantic aspects into account. For this reason, we present the prototype of a web-based reviewing tool which especially aims at fostering semantic analysis and visualization, that is, considering relations, properties and semantic categories of concepts, algorithms and reviews. The tool uses techniques of the Semantic Web. Its application is demonstrated by example.