Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia

<p>Abstract</p> <p>Background</p> <p>The OpenTox Framework, developed by the partners in the OpenTox project (<url>http://www.opentox.org</url>), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing.</p> <p>Results</p> <p>The following related ontologies have been developed for OpenTox: a) Toxicological ontology – listing the toxicological endpoints; b) Organs system and Effects ontology – addressing organs, targets/examinations and effects observed in <it>in vivo</it> studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.</p> <p>OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.</p> <p>The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists).</p> <p>Availability</p> <p>The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page <url>http://www.opentox.org/dev/ontology</url>; the OpenTox ontology is available as OWL at <url>http://opentox.org/api/1 1/opentox.owl</url>, the ToxML - OWL conversion utility is an open source resource available at <url>http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/</url></p

The role of ontologies in biological and biomedical research: a functional perspective.

Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/bib/bbv01

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services

Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-Store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection

A survey of large-scale reasoning on the Web of data

As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

Logical Linked Data Compression

Linked data has experienced accelerated growth in recent years. With the continuing proliferation of structured data, demand for RDF compression is becoming increasingly important. In this study, we introduce a novel lossless compression technique for RDF datasets, called Rule Based Compression (RB Compression) that compresses datasets by generating a set of new logical rules from the dataset and removing triples that can be inferred from these rules. Unlike other compression techniques, our approach not only takes advantage of syntactic verbosity and data redundancy but also utilizes semantic associations present in the RDF graph. Depending on the nature of the dataset, our system is able to prune more than 50% of the original triples without affecting data integrity

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

A Semantic Framework for Declarative and Procedural Knowledge

In any scientic domain, the full set of data and programs has reached an-ome status, i.e. it has grown massively. The original article on the Semantic Web describes the evolution of a Web of actionable information, i.e.\ud information derived from data through a semantic theory for interpreting the symbols. In a Semantic Web, methodologies are studied for describing, managing and analyzing both resources (domain knowledge) and applications (operational knowledge) - without any restriction on what and where they\ud are respectively suitable and available in the Web - as well as for realizing automatic and semantic-driven work\ud ows of Web applications elaborating Web resources.\ud This thesis attempts to provide a synthesis among Semantic Web technologies, Ontology Research, Knowledge and Work\ud ow Management. Such a synthesis is represented by Resourceome, a Web-based framework consisting of two components which strictly interact with each other: an ontology-based and domain-independent knowledge manager system (Resourceome KMS) - relying on a knowledge model where resource and operational knowledge are contextualized in any domain - and a semantic-driven work ow editor, manager and agent-based execution system (Resourceome WMS).\ud The Resourceome KMS and the Resourceome WMS are exploited in order to realize semantic-driven formulations of work\ud ows, where activities are semantically linked to any involved resource. In the whole, combining the use of domain ontologies and work ow techniques, Resourceome provides a exible domain and operational knowledge organization, a powerful engine for semantic-driven work\ud ow composition, and a distributed, automatic and\ud transparent environment for work ow execution