From raw publications to Linked Data

Groza, Tudor; Grimnes, Gunnar Aastrand; Handschuh, Siegfried; Decker, Stefan

doi:10.1007/s10115-011-0473-6

Cited by 11 publications

(9 citation statements)

References 22 publications

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“…3. Groza et al (2011). On the contrary, our system is simple and works quite well on the real-world papers even under a small training dataset.…”

Section: 32mentioning

confidence: 88%

“…Perceptron with Uneven Margins, Maximum Entropy, Maximum Entropy Markov Model, Voted Perceptron (VP) Model, and CRF), and performed title extraction using the trained models. Groza et al (2011) presented a machine learning pipeline (including SVM classifiers, rule-based convergence procedure, and CRF chunkers) to automatic extraction of heading metadata from scientific publications, and further used linked data-driven mechanism to refine and link the extracted metadata. These methods illustrated machine learning methods can achieve a level of accuracy that is comparable to that achieved by human experts.…”

Section: Introduction 11 Motivation and Related Workmentioning

confidence: 99%

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Extracting bibliographical data for PDF documents with HMM and external resources

Hsiao

Chang

Thomas

2014

Program

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Purpose – The purpose of this paper is to propose an automatic metadata extraction and retrieval system to extract bibliographical information from digital academic documents in portable document formats (PDFs). Design/methodology/approach – The authors use PDFBox to extract text and font size information, a rule-based method to identify titles, and an Hidden Markov Model (HMM) to extract the titles and authors. Finally, the extracted titles and authors (possibly incorrect or incomplete) are sent as query strings to digital libraries (e.g. ACM, IEEE, CiteSeerX, SDOS, and Google Scholar) to retrieve the rest of metadata. Findings – Four experiments are conducted to examine the feasibility of the proposed system. The first experiment compares two different HMM models: multi-state model and one state model (the proposed model). The result shows that one state model can have a comparable performance with multi-state model, but is more suitable to deal with real-world unknown states. The second experiment shows that our proposed model (without the aid of online query) can achieve as good performance as other researcher's model on Cora paper header dataset. In the third experiment the paper examines the performance of our system on a small dataset of 43 real PDF research papers. The result shows that our proposed system (with online query) can perform pretty well on bibliographical data extraction and even outperform the free citation management tool Zotero 3.0. Finally, the paper conducts the fourth experiment with a larger dataset of 103 papers to compare our system with Zotero 4.0. The result shows that our system significantly outperforms Zotero 4.0. The feasibility of the proposed model is thus justified. Research limitations/implications – For academic implication, the system is unique in two folds: first, the system only uses Cora header set for HMM training, without using other tagged datasets or gazetteers resources, which means the system is light and scalable. Second, the system is workable and can be applied to extracting metadata of real-world PDF files. The extracted bibliographical data can then be imported into citation software such as endnote or refworks to increase researchers’ productivity. Practical implications – For practical implication, the system can outperform the existing tool, Zotero v4.0. This provides practitioners good chances to develop similar products in real applications; though it might require some knowledge about HMM implementation. Originality/value – The HMM implementation is not novel. What is innovative is that it actually combines two HMM models. The main model is adapted from Freitag and Mccallum (1999) and the authors add word features of the Nymble HMM (Bikel et al, 1997) to it. The system is workable even without manually tagging the datasets before training the model (the authors just use cora dataset to train and test on real-world PDF papers), as this is significantly different from what other works have done so far. The experimental results have shown sufficient evidence about the feasibility of our proposed method in this aspect.

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“…3. Groza et al (2011). On the contrary, our system is simple and works quite well on the real-world papers even under a small training dataset.…”

Section: 32mentioning

confidence: 88%

Section: Introduction 11 Motivation and Related Workmentioning

confidence: 99%

Extracting bibliographical data for PDF documents with HMM and external resources

Hsiao

Chang

Thomas

2014

Program

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“…Thus, information follows a data-centric organization within the Web of Data. The advances in extraction mechanisms [22] or the annotation of massive amounts of resources [42], among others, have motivated the growth of the Web of Data in which the number (and scale) of semantic applications in use increases, more RDF data are linked together, and increasingly larger datasets are obtained. This popularity is the basis for the development of RDF management systems (referred to as RDF stores), which play a central role in the Web of Data.…”

Section: Introductionmentioning

confidence: 99%

Compressed vertical partitioning for efficient RDF management

et al. 2014

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Artículo de publicación ISIThe Web of Data has been gaining momentum in recent years. This leads to increasingly publish more and more semi-structured datasets following, in many cases, the RDF (Resource Description Framework) data model based on atomic triple units of subject, predicate, and object. Although it is a very simple model, specific compression methods become necessary because datasets are increasingly larger and various scalability issues arise around their organization and storage. This requirement is even more restrictive in RDF stores because efficient SPARQL solution on the compressed RDF datasets is also required. This article introduces a novel RDF indexing technique that supports efficient SPARQL solution in compressed space. Our technique, called -triples, uses the predicate to vertically partition the dataset into disjoint subsets of pairs (subject, object), one per predicate. These subsets are represented as binary matrices of subjects objects in which 1-bits mean that the corresponding triple exists in the dataset. This model results in very sparse matrices, which are efficiently compressed using -trees. We enhance this model with two compact indexes listing the predicates related to each different subject and object in the dataset, in order to address the specific weaknesses of vertically partitioned representations. The resulting technique not only achieves by far the most compressed representations, but also achieves the best overall performance for RDF retrieval in our experimental setup. Our approach uses up to 10 times less space than a state-of-the-art baseline and outperforms its time performance by several orders of magnitude on the most basic query patterns. In addition, we optimize traditional join algorithms on -triples and define a novel one leveraging its specific features. Our experimental results show that our technique also overcomes traditional vertical partitioning for join solution, reporting the best numbers for joins in which the non-joined nodes are provided, and being competitive in most of the cases.Chilean Fondecyt 1-110066 1-14079

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“…RDF/S KBs are often subject to change for various reasons, including changes to the modeled world, new information on the domain (e.g., due to extracted metadata from text [10]), newly-gained access to information previously unknown or classified (e.g., due to entity resolution or disambiguation [11]), and other eventualities [12], [13], [14]. To address this problem, we propose a change framework that supports arbitrarily complex 1.…”

Section: Introductionmentioning

confidence: 99%

Formal foundations for RDF/S KB evolution

et al. 2012

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Abstract-There are ongoing efforts to provide declarative formalisms of integrity constraints over RDF/S data. In this context, addressing the evolution of RDF/S knowledge bases while respecting associated constraints is a challenging issue, yet to receive a formal treatment. We provide a theoretical framework for dealing with both schema and data change requests. We define the notion of a rational change operator as one that satisfies the belief revision principles of Success, Validity and Minimal Change. The semantics of such an operator are designed to be subject to customization, by tuning the properties that a rational change should adhere to. We prove some interesting theoretical results and propose a general-purpose algorithm for implementing rational change operators in knowledge bases with integrity constraints, which allows us to handle uniformly any possible change request in a provably rational and consistent manner. Then, we apply our framework to a well-studied RDF/S variant, for which we suggest a specific notion of minimality. For efficiency purposes, we also describe specialized versions of the general evolution algorithm for the RDF/S case, which provably have the same semantics as the general-purpose one for a limited set of (useful in practice) types of change requests.

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From raw publications to Linked Data

Cited by 11 publications

References 22 publications

Extracting bibliographical data for PDF documents with HMM and external resources

Extracting bibliographical data for PDF documents with HMM and external resources

Compressed vertical partitioning for efficient RDF management

Formal foundations for RDF/S KB evolution

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