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"The contributions assembled in this volume illustrate the band-width of applications of the Linked Data paradigm for representative types of language resources. They cover lexical-semantic resources, annotated corpora, typological databases as well as terminology and metadata repositories. The book includes representative applications from diverse fields, ranging from academic linguistics (e.g., typology and corpus linguistics) over applied linguistics (e.g., lexicography and translation studies) to technical applications (in computational linguistics, natural language processing and information technology).This volume accompanies the Workshop on Linked Data in Linguistics 2012 (LDL-2012) in Frankfurt/M., Germany, organized by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN). It assembles contributions of the workshop participants and, beyond this, it summarizes initial steps in the formation of a Linked Open Data cloud of linguistic resources, the Linguistic Linked Open Data cloud (LLOD)."

"Researchers use different approaches when collecting and managing primary language materials during fieldwork. Yet it is important that this work is done in a transparent way, so that it can be used by other researchers, who might have other aims, as well as by the speaker community who might want to use or take note of the collected materials. In this article we use our research experience in language data collection in and around Indonesia in fieldwork projects of three kinds: descriptive fieldwork, linguistic surveys, and projects investigating language contact. Our aim is to provide an introductory and practical guide for students and professionals who are embarking on fieldwork in or around Indonesia. Describing practical methods of language data collection, processing, and management, our aim is to provide a guide for any research which involves the collection of language materials, including linguistic research, oral history or literature, and ethnography."

"Social tagging (including hashtags) is used over platforms such as Twitter, Facebook, Instagram, Pinterest, WordPress, Tumblr and YouTube across countries and cultures, meaning that one single hashtag can link information from a variety of resources. This new book explores social tagging as a potential form of linked data and shows how it can provide an increasingly important way to categorize and store information resources. The internet is moving rapidly from the social web embodied in Web 2.0, to the semantic web (Web 3.0), where information resources are linked to make them comprehensible to both machines and humans. Traditionally, library discovery systems have pushed information, but did not allow for any interaction with the users of the catalogue, while social tagging provides a means to help library discovery systems become social spaces where users could input and interact with content. The editors and their international contributors explore key issues including the use of hashtags in the dissemination of public policy, the use of hashtags as information portals in library catalogues, social tagging in enterprise environments, the linked data potential of social tagging, [and] sharing and disseminating information needs via social tagging. Social Tagging in a Linked Data Environment will be useful reading for practising library and information professionals involved in electronic access to collections, including cataloguers, system developers, information architects and web developers"

"Manajemen data memilki peranan kunci dalam bagaimana kita mengakses, mengatur, dan mengintegrasikan data. Komunitas riset adalah salah satu domain dimana data disebarkan, contohnyadistribusi data dalam proyek, publikasi dan anggota. Tidak ada standar yang mengatur distribusi data selama ini.Oleh karena itu,value dari data cenderung menurun, contohnya dalam konteksaccessibility, discoverability, dan usability. LinkedLab merupakan sebuah usulanplatform untuk mengelola data untuk komunitas riset dengan menggunakan teknik Linked Data. Kegunaan Linked Data adalah sebuah cara yang efektif untuk mengakses, mengatur, dan mengitegrasikan data.

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Data management has a key role on how we access, organize, and integrate data. Research community is one of the domain on which data is disseminated, e.g., projects, publications, and members.There is no well-established standard for doing so, and therefore the value of the data decreases, e.g. in terms of accessibility, discoverability, and reusability. LinkedLab proposes a platform to manage data for research communites using Linked Data technique. The use of Linked Data affords a more effective way to access, organize, and integrate the data."

"Pembangkitan komentar dari data terstruktur adalah tugas yang menantang dalam bidang pembangkitan bahasa alami dengan berbagai potensi aplikasi dalam bidang olahraga dan esports. Penelitian-penelitian sebelumnya sering berfokus pada penyesuaian model datake-teks khusus, namun eksperimen awal penulis menemukan bahwa pretrained language model menunjukkan performa yang lebih baik dalam pembangkitan komentar. Penelitian ini berfokus pada bagaimana pretrained language model dapat diadaptasi untuk menghasilkan performa yang lebih baik pada permasalahan pembangkitan komentar menggunakan data replay Counter-Strike. Penulis menemukan bahwa penyesuaian pretrained language model dapat meningkatkan kualitas semantik dan alami dari pembangkitan komentar. Namun, penulis juga menemukan bahwa peningkatan kualitas semantik dan alami ini didampingi dengan penurunan pada kualitas leksikal akibat tantangan intrinsik yang ada dalam pembangkitan komentar.

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Generating commentary from structured data poses a significant challenge in natural language generation, with extensive applications in sports and esports domains. Previous research has predominantly centered on tailoring specialized data-to-text models for this task. However, our preliminary investigation indicates that pretrained language models exhibit superior performance in commentary generation. This study focuses on further adapting pretrained language models to enhance their suitability for esports commentary generation, specifically utilizing Counter-Strike replay data. Our findings reveal that through this adaptation, pretrained language models can augment the semantic quality and naturalness of generated commentary. Nevertheless, we observed a trade-off, as the improvement in semantic quality and naturalness was accompanied by a decline in lexical quality, owing to the inherent complexities involved in commentary generation."