Embedding Knowledge
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Embedding Knowledge
Die Vision der konsequenten Vernetzung verschiedener eingebetter Geräte miteinander und dem bestehenden Internet, das „Internet der Dinge“, steht zunehmend im wissenschaftlichen und wirtschaftlichen Interesse. Aktuelle Standarisierungsbemühungen erstreben eine Interoperabilität unterschiedlichster Geräteklassen bis hin zur Applikationsschicht. Die Darstellung von kommunizierten Informationen auf dieser Schicht allerdings ist üblicherweise in Formaten und Vokabeln kodiert, die spezifisch für eine bestimmte Wissensdomäne sind. Parallel beobachten wir die Entstehung des Semantischen Webs, einer Reihe von Standards, die Wissen im World Wide Web auf universelle und verteilte Weise darstellen. Diese Arbeit untersucht die Verbindung des Internets der Dinge mit dem Semantischen Web, mit dem Ziel einer universellen Wissensdarstellung, die „Dinge“ miteinander und dem bestehenden Internet verbindet. Insbesondere diskutieren wir, inwieweit es möglich ist, semantisches Wissen im Sinne des Sematischen Webs auf verschiedenen ressourcenbeschränkten Systemen zu speichern, zu verarbeiten und abzufragen.
Embedding Knowledge Graphs with RDF2vec
This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode.
Innovations in Big Data Mining and Embedded Knowledge
This book addresses the usefulness of knowledge discovery through data mining. With this aim, contributors from different fields propose concrete problems and applications showing how data mining and discovering embedded knowledge from raw data can be beneficial to social organizations, domestic spheres, and ICT markets. Data mining or knowledge discovery in databases (KDD) has received increasing interest due to its focus on transforming large amounts of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships. The concept of knowledge is broad and speculative and has promoted epistemological debates in western philosophies. The intensified interest in knowledge management and data mining stems from the difficulty in identifying computational models able to approximate human behaviors and abilities in resolving organizational, social, and physical problems. Current ICT interfaces are not yet adequately advanced to support and simulate the abilities of physicians, teachers, assistants or housekeepers in domestic spheres. And unlike in industrial contexts where abilities are routinely applied, the domestic world is continuously changing and unpredictable. There are challenging questions in this field: Can knowledge locked in conventions, rules of conduct, common sense, ethics, emotions, laws, cultures, and experiences be mined from data? Is it acceptable for automatic systems displaying emotional behaviors to govern complex interactions based solely on the mining of large volumes of data? Discussing multidisciplinary themes, the book proposes computational models able to approximate, to a certain degree, human behaviors and abilities in resolving organizational, social, and physical problems. The innovations presented are of primary importance for: a. The academic research community b. The ICT market c. Ph.D. students and early stage researchers d. Schools, hospitals, rehabilitation and assisted-living centers e. Representatives from multimedia industries and standardization bodies