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"This book, provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment. After an introduction, in its second part the authors describe the NeOn Methodology framework. The book’s third part details the key activities relevant to the ontology engineering life cycle. For each activity, a general introduction, methodological guidelines, and practical examples are provided. The fourth part then presents a detailed overview of the NeOn Toolkit and its plug-ins. Lastly, case studies from the pharmaceutical and the fishery domain round out the work."

"Saat ini, data dalam bentuk teks semakin berlimpah pada berbagai domain dan media, baik media cetak maupun online. Penambahan kumpulan dokumen teks ini menyebabkan kemudahan akses suatu informasi atau pengetahuan yang ada pada teks semakin berkurang. Selain itu, informasi atau pengetahuan yang ada tersebut semakin sulit untuk diinterpretasi dan dipahami secara menyeluruh. Untuk itu diperlukan suatu cara untuk membantu mempermudah pemahaman suatu data teks. Hal ini dengan melakukan penggalian pengetahuan pada data teks yang melimpah melalui pemrosesan data yang tidak terstruktur (text mining), dengan mengembangkan metode interpretasi berbasis ontologi pada teks untuk memperoleh pengetahuan baru sebagai state of the art. Dalam penelitian ini, dikembangkan beberapa teknik /metode. Pertama adalah pengembangan teknik preprocessing pada data teks (korpus) serta key phrase extraction menggunakan AST (Annotated Suffix Tree) untuk memperoleh key phrase (frasa kunci) dan frekuensi kemunculan. Kedua adalah pengembangan pemodelan ontologi sebagai basis pengetahuan pada suatu domain berupa relasi antar key phrase menggunakan clustering dan Bayesian Network. Ketiga adalah pengembangan metode sparse clustering pada data sparse, yaitu is-FADDIS (iterative scaling Additive Fuzzy Spectral Clustering) untuk proses pemilahan data teks, yang merupakan pengembangan dari metode clustering FADDIS (Additive Fuzzy Spectral Clustering) serta keempat adalah pengembangan metode matching dan correlating terhadap ontologi, sebagai teknik yang digunakan saat interpretasi teks. Secara terintegrasi, pembangunan ontologi dari teks, dengan domain berita, dilakukan diawal dengan tahapan ekstraksi key phrase, clustering (is-FADDIS, opsional) dan structure learning untuk membentuk ontologi-tree. Key phrase sebagai konsep, menjadi node pada ontologi tersebut, yang menjadi basis pengetahuan domain. Tahapan berikutnya adalah melakukan interpretasi teks pada suatu teks input yang terdiri dari satu key phrase atau satu cluster menggunakan ontologi tersebut untuk mendapatkan pengetahuan baru. Interpretasi dilakukan dengan ontologi berasal dari teks dengan dua domain dan satu domain. Hasil interpretasi teks menggunakan ontologi berbasis Additive Fuzzy Spectral Clustering (is-FADDIS) ini dievaluasi menggunakan usulanscore relevansi. Pada teks input dengan satu key phrase sejumlah lima input yang diinterpretasi, hasilnya adalah 40% relevan, 40% kurang relevan dan 20% tidak relevan. Pada teks input satu cluster sejumlah dua input yang diinterpretasi, hasilnya adalah relevan. Nilai score relevansi yang relevan, secara empiris adalah lebih 0,3 dari skala 1, dan score relevansi yang didapat, ada yang mencapai 0,33. Dengan pembandingan hasil interpretasi melalui variasi teknik pada pembangunan ontologi, didapatkan, penggunaan ontologi berbasis is-FADDIS untuk interpretasi teks, relatif pada penelitian ini belum memberikan hasil optimal. Dalam penggunaan teknik-teknik yang dikembangkan, metode ini memberikan keluaran interpretasi teks yang dapat membantu untuk mengolah informasi teks dalam jumlah tidak terlalu besar tetapi cepat.

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Currently, the data in the form of text more abundant on various domains and media, both print and online media. The addition of this text document causes the ease of access to any information or knowledge contained in the text is reduced. In addition, the existing information or knowledge is increasingly difficult to interpret and understand comprehensively. For that background, the purpose of the research is to extract knowledge on abundant text data through the processing of unstructured data (text mining), by developing ontology-based interpretation method on text to gain a new knowledge as state of the art.

In this research, some technique/method were developed. The first is the development of preprocessing techniques on text data (corpus) and key phrase extraction using AST (Annotated Suffix Tree) to obtain key phrase and frequency of occurrence. The second is the development of ontology modeling as a knowledge base on a domain in the form of relationships between key phrases using Bayesian Network. The third is the development of sparse clustering method in sparse data, namely is-FADDIS (iterative scaling-Additive Fuzzy Spectral Clustering) for text grouping process, which is the addition of FADDIS clustering method (Additive Fuzzy Spectral Clustering) and the fourth is the development of matching and correlating method as a technique used at interpreting the text entered using ontology.

In an integrated manner, the ontology development of the text, with news domains, is done by processes include key phrase extraction, clustering (is-FADDIS, optional) and structure learning to form ontology-tree. Key phrase as a concept, being the node on the ontology, which becomes the domain knowledge base. The next step is to interpret the text on an input text consisting of a key phrase or a cluster using the ontology to gain new knowledge. Interpretation done with ontology comes from text with two domains and one domain. Text interpretation results using Fuzzy Spectral Clustering (is-FADDIS) based ontology is evaluated using relevancy scores.

In the input text with one key phrase a total of five interpreted inputs, the result is 40% relevant, 40% less relevant and 20% irrelevant. In one-cluster input text a number of two inputs are interpreted, the result is relevant. Relevant relevance score score, empirically more than 0.3 of scale 1, and score relevance obtained, some reaching 0.33. By comparing the results of interpretation through the variation of techniques on ontology development, it was found, the use of FADDIS-based ontology for textual interpretation, relative to this research has not provided optimal results. In the use of developed techniques, this method provides textual interpretation output that can help to process text information in quantities not too large but fastly."

"This book examines the role ontology engineering can play in providing solutions to the problems of information interoperability and linked data. At the same time as introducing basic concepts of ontology engineering, the book discusses methodological approaches to formal representation of data and information models, thus facilitating information interoperability between heterogeneous, complex and distributed communication systems. In doing so, the text advocates the advantages of using ontology engineering in telecommunications systems. In addition, it offers a wealth of guidance and best-practice techniques for instances in which ontology engineering is applied in cloud services, computer networks and management systems."

"Setiap pembelajar memiliki karakteristik berbeda-beda antara lain berupa gaya belajar, prior knowledge, dan kecerdasan. Permasalahan ini sulit sekali diakomodasi pembelajaran tradisional. Berawal dari e-commerce, personalisasi meluas ke berbagai bidang TI termasuk pembelajaran online. Teknologi untuk personalisasi salah satunya adalah semantic web yang dapat membuat sistem menjadi adaptif. Tahap awal dalam pengembangan sistem berbasis semantic web adalah pemodelan dengan ontologi yang memiliki kemampuan reasoning terhadap banyak data. Salah satu ontologi yang harus dikembangkan adalah student model ontology yang dikembangkan pada penelitian ini dengan mempertimbangkan aspek gaya belajar dan performa sebagai representasi prior knowledge dan kecerdasan siswa.

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Each learner has different characteristics including learning styles, prior knowledge, and intelligence. This problem is very difficult to accommodate by traditional learning. Starting from e-commerce field, personalization extends to various fields of IT including online learning. One of technologies for personalization is semantic web which makes systems adaptive. The first stage is modeling with ontology that has ability of reasoning large of data. One of the ontologies that must be developed is student model ontology, which is developed by considering learning styles and performance aspect that represents students prior knowledge and intelligence."

"An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities."Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines.Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six "provocations" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.--publisher."

"In this book, present an extensive overview of the work done in Semantic Search and other related areas. They explore different technologies and solutions in depth, making their collection a valuable and stimulating reading for both academic and industrial researchers.The book is divided into three parts. The first introduces the readers to the basic notions of the Web of Data. The second part is dedicated to Web Search. It presents different types of search, like the exploratory or the path-oriented, alongside methods for their efficient and effective implementation. The focus of the third part is on linked data, and more specifically, on applying ideas originating in recommender systems on linked data management, and on techniques for the efficiently querying answering on linked data."

"This book constitutes the refereed proceedings of the 18th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2012, held in Galway City, Ireland, in October 2012. The 44 revised full papers were carefully reviewed and selected from 107 submissions. The papers are organized in topical sections on knowledge extraction and enrichment, natural language processing, linked data, ontology engineering and evaluation, social and cognitive aspects of knowledge representation, application of knowledge engineering, and demonstrations."