Semantics and Internet of Things
With the rapid expansion of connected devices and the rise of Internet of Things (IoT) the semantic technologies slowly become an important part of the evolution process of the nowadays information layer of our planet. Semantic sensors (&Actuator) web is an extension of the current Web/Internet in which information is given well-defined meaning , better enabling objects , devices and people to work in co-operation and to also enable autonomous interactions between devices and/or objects. This was made possible because the semantic sensor Web enables interoperability and advanced analytics for situation awareness and other advanced applications from heterogeneous sensors.
Semantic Recommender Systems
Recommender systems have become a popular information filtering device now present on a number of web based media platforms. In such cases the deployed recommender system will attempt to predict the rating that a user has given to the recommended item (i.e. movie, song, book, location, etc.) by understanding what a user likes, and doesn’t like, in the form of a taste profile. With Semantic SVD based algorithms it is possible to create preference profiles which can track users’ tastes and overcomes the factor consistency problem meanwhile enabling modeling of global taste influence. This process is intensively used to boosts recommendation performance.
Semantic Entity Recognition
Named entities specify things such as persons, places and organizations. Semantic Technologies are capable of identifying people, companies, organizations, cities, geographic features and other typed entities from HTML, text, documents ot web-based content. Entity extraction can add a wealth of semantic knowledge to the content to help quickly understand the subject of the text. It is one of the most common starting points for using natural language processing techniques to enrich your content. Named entity extraction is based on sophisticated statistical algorithms and natural language processing technology. It is often associated with multilingual support, linked data, context-sensitive entity disambiguation, comprehensive type support and quotations extraction and more
Document summarization refers to the task of creating document surrogates that are smaller in size but retain various characteristics of the original document. To automate the process of abstracting, researchers generally rely on a two phase process. First, key textual elements, e.g., keywords, clauses, sentences, or paragraphs are extracted from text using linguistic and statistical analyses. In the second step, the extracted text may be used as a summary. Such summaries are referred to as ‘extracts’. Alternatively, textual elements can be used to generate new text, similar to the human authored abstract. Semantic Technologies are largely capable of abstracting and summarization.
Semantic Graph Analytics
Semantic graph analytics offer sophisticated capabilities for analyzing relationships, while traditional analytics focus on summarizing, aggregating and reporting on data. Some common graph analytic techniques include: Centrality analysis: To identify the most central entities in your network, a very useful capability for influencer marketing; Path analysis: To identify all the connections between a pair of entities, useful in understanding risks and exposure; Community detection: To identify clusters or communities, which is of great importance to understanding issues in sociology and biology; Sub-graph isomorphism: To search for a pattern of relationships, useful for validating hypotheses and searching for abnormal situations, such as hacker attacks.
Semantic technologies are facilitating copyright management in the context of User Generated Content. It is a key issue for the media industry these days, in addition to unauthorised media reproduction and distribution, to control the reuse of media in user generated content. To solve this issue and avoid publishing content that infringes copyright, services like YouTube offer mechanism to detect the unauthorised reuse of media, and give the choice to monetarise its use rather than take down the content. However, all the potential of this new revenue stream is at risk if copyright subtleties are not managed appropriately. For instance, if the same song is owned by different rights holders depending on the territory. What is required is a scalable decision support system capable of integrating digital rights languages, like DDEX or ODRL, together with contracts or policies, like talent contracts or business policies. Semantic technologies provide a common and expressive framework where all these copyright information sources can be represented together.
Semantics for Cybersecurity
Current information technology security systems primarily focus on simple threats, such as defending against traffic on specific ports, virus detection, etc. However, adversaries are targeting organizations with complex attacks that appear completely legitimate but have devastating effects. Current security controls might detect such attacks days later. To provide appropriate risk assessment, next-generation information security systems can be built to leverage the power of their underlying semantic and cloud-based technologies including: Infrastructure-Enhanced Security—where cloud computing will likely reduce encryption and decryption times, promoting further adoption of these security controls, while likely demanding and promoting enhanced key strategies. Cloud computing can sustain cutting-edge, near-real-time analytics that mine vast amounts of security data to identify complex threats, and detect intentional and unintentional information access and abuse for both internal and external users. Enhanced Threat Modeling—where cloud computing analytics developed for social network analysis will provide capabilities to analyze large amounts of data about users, network traffic and other interests to detect seemingly safe activities that match larger threats. Semantic Security—where advances in semantic technology, in conjunction with cloud computing, will promote security controls that simulate human cognition and can block and/or report suspect communications in near real-time over Internet scale data. The semantic security evolution will address the adoption of semantic technologies and include software agents that act on behalf of end users.
Semantics for Defense and Security
Based on supporting and exploiting domain specific ontologies, semantic technologies offer advanced capability in heterogeneous content processing analysis, and integration at a higher semantic level-- rather than merely syntactical and structural level approaches based on XML and RDF. These capabilities have been demonstrated in addressing requirements of very demanding Homeland Security and National Security applications such as passenger threat assessment or anti-terorrism. Link analysis, News analysis, Personal Information analysis and cumulative threat analysis are just some of the packages enabled by semantic applications. These systems support the identification of semantic associations and provide analysts with a powerful toolset. Their capabilities are maximized relationships by intelligently correlating content with contextual real-world knowledge, thus making the information more relevant and actionable for enterprise user