Making Presentation Math Computable

This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book

Making Presentation Math Computable

This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book

Semantic physical science.

The articles in this special issue arise from a workshop and symposium held in January 2012 (Semantic Physical Science'). We invited people who shared our vision for the potential of the web to support chemical and related subjects. Other than the initial invitations, we have not exercised any control over the content of the contributed articles.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Towards efficient data integration and knowledge management in the Agronomic domain

International audienceToday, the revolution in empirical technologies has generated vast amounts of data. This data deluge has created an urgent need to assimilate it with a panoramic view. To this end, information systems play a central role in managing and integrating these data, aiding the biologists in exploiting this integrated information for the extraction of new knowledge. The plant bioinformatics node of the Institut Français de Bioinformatique (IFB) maintains public information systems where a variety of domain specific data are integrated. Currently, efforts are being taken to expose the IFB plant bioinformatics resources as RDF, utilising domain specific ontologies and metadata. Here, we present the overview and the progress of the project

Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology

Wiljes C, Cimiano P. Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology. In: Proceedings of the Workshop on the Semantic Publishing (SePublica 2012). 2012: 48-59.The Web was designed to improve the way people work together. The Semantic Web extends the Web with a layer of Linked Data that offers new paths for scientific publishing and co-operation. Experimental raw data, released as Linked Data, could be discovered automatically, fostering its reuse and validation by scientists in different contexts and across the boundaries of disciplines. However, the technological barrier for scientists who want to publish and share their research data as Linked Data remains rather high. We present two real-life use cases in the fields of chemistry and biology and outline a general methodology for transforming research data into Linked Data. A key element of our methodology is the role of a scientific data curator, who is proficient in Linked Data technologies and works in close co-operation with the scientist

Information Retrieval Service Aspects of the Open Research Knowledge Graph

Information Retrieval (IR) takes a fresh perspective in the context of the next-generation digital libraries such as the Open Research Knowledge Graph (ORKG). As scholarly digital libraries evolve from document-based to knowledge-graph-based representations of content, there is a need for their information technology services to suitably adapt as well. The ORKG enables a structured representation of scholarly contributions data as RDF triples - in turn, it fosters FAIR (Findable, Accessible, Interoperable, and Reusable) scholarly contributions. This thesis has practically examined three different IR service aspects in the ORKG with the aim to help users: (i) easily find and compare relevant scholarly contributions; and (ii) structure new contributions in a manner consistent to the existing ORKG knowledge base of structured contributions. In the first part, it will evaluate and enhance the performance of the default ORKG “Contributions Similarity Service.” An optimal representation of contributions as documents obtains better retrieval performance of the BM25 algorithm in Elasticsearch. To achieve this, evaluation datasets were created and the contributions search index reinitialized with the new documents. In its second part, this thesis will introduce a “Templates Recommendation Service.” Two approaches were tested. A supervised approach with a Natural Language Inference (NLI) objective that tries to infer a contribution template for a given paper if one exists or none. And an unsupervised approach based on search that tries to return the most relevant template for a queried paper. Our experiments favoring ease of practical installation resulted in the conclusion that the unsupervised approach was better suited to the task. In a third and final part, a “Grouped Predicates Recommendation Service” will be introduced. Inspired from prior work, the service implements K-Means clustering with an IR spin. Similar structured papers are grouped, their in-cluster predicate groups computed, and new papers are semantified based on the predicate groups of the most similar cluster. The resulting micro-averaged F-measure of 65.5% using TF-IDF vectors has shown a sufficient homogeneity in the clusters

Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context

Mathematical formulae represent complex semantic information in a concise form. Especially in Science, Technology, Engineering, and Mathematics, mathematical formulae are crucial to communicate information, e.g., in scientific papers, and to perform computations using computer algebra systems. Enabling computers to access the information encoded in mathematical formulae requires machine-readable formats that can represent both the presentation and content, i.e., the semantics, of formulae. Exchanging such information between systems additionally requires conversion methods for mathematical representation formats. We analyze how the semantic enrichment of formulae improves the format conversion process and show that considering the textual context of formulae reduces the error rate of such conversions. Our main contributions are: (1) providing an openly available benchmark dataset for the mathematical format conversion task consisting of a newly created test collection, an extensive, manually curated gold standard and task-specific evaluation metrics; (2) performing a quantitative evaluation of state-of-the-art tools for mathematical format conversions; (3) presenting a new approach that considers the textual context of formulae to reduce the error rate for mathematical format conversions. Our benchmark dataset facilitates future research on mathematical format conversions as well as research on many problems in mathematical information retrieval. Because we annotated and linked all components of formulae, e.g., identifiers, operators and other entities, to Wikidata entries, the gold standard can, for instance, be used to train methods for formula concept discovery and recognition. Such methods can then be applied to improve mathematical information retrieval systems, e.g., for semantic formula search, recommendation of mathematical content, or detection of mathematical plagiarism.Comment: 10 pages, 4 figure

Open Government Data: Fostering Innovation

The provision of public information contributes to the enrichment and enhancement of the data produced by the government as part of its activities, and the transformation of heterogeneous data into information and knowledge. This process of opening changes the operational mode of public administrations, leveraging the data management, encouraging savings and especially in promoting the development of services in subsidiary and collaborative form between public and private entities. The demand for new services also promotes renewed entrepreneurship centred on responding to new social and territorial needs through new technologies. In this sense we speak of Open Data as an enabling infrastructure for the development of innovation and as an instrument to the development and diffusion of Innovation and Communications Technology (ICT) in the public system as well as creating space for innovation for businesses, particularly SMEs, based on the exploitation of information assets of the territory. The Open Data Trentino Project has initiated and fosters the process of opening of public information and develops as a natural consequence of this process of openness, the creation of innovative services for and with the citizens. In this paper we present how our project acts on long-chain, from raw data till reusable meaningful and scalable knowledge base that leads to the production of data reuse through the implementation of services that will enhance and transform the data into information capable of responding to specific questions efficiency and innovation