Semantic web and semantic technologies to enhance innovation and technology watch processes

Innovation is a key process for Small and Medium Enterprises in order to survive and evolve in a competitive environment. Ideas and idea management are considered the basis for Innovation. Gathering data on how current technologies and competitors evolve is another key factor for companies' innovation. Therefore, this thesis focuses the application of Information and Communication Technologies and more specifically Semantic Web and Semantic Technologies on Idea Management Systems and Technology Watch Systems. Innovation and Technology Watch platform managers usually face many problems related with the data they collect and manage. Those managers have to deal with a large amount of information distributed in different platforms, not always interoperable among them. It is vital to share data between platforms so it can be converted into knowledge. Many of the tasks they perform are non productive and too much time and effort is expended on them. Moreover, Innovation process managers have difficulties in identifying why an idea contest has been successful. Our proposal is to analyze different Information and Communication Technologies that can assist companies with their Innovation and Technology Watch processes. Thus, we studied several Semantic and Web technologies, we build some conceptual models and tested them in different case studies to see the results achieved in real scenarios. The outcome of this thesis has been the creation of a solution architecture to enable interoperability among platforms and to ease the work of the process' managers. In this framework and to complement the architecture, two ontologies have been developed: (1) Gi2Mo Wave and (2) Mentions Ontology. On one hand, Gi2Mo Wave focused on annotating the background of idea contests, assisting on the analysis of the contests and easing its replication. On the other hand, Mentions Ontology focused on annotating the elements mentioned in plain text content, such as ideas or news items. That way, Mentions Ontology creates a way to link the related content, enabling the interoperability among content from different platforms. In order to test the architecture, a new web Idea Management System and a Technology Watch system have been also developed. The platforms incorporate semantic ontologies and tools to enable interoperability. We also demonstrate how Semantic Technologies reduce human workload by contributing on the automatic classification of content in the Technology Watch process. Finally, conclusions have been gathered according to the results achieved testing the used technologies, identifying the ones with best results.Berrikuntza prozesu oso garrantzitsu bat da Enpresa Txiki eta Ertainen lehiakor eta bizirik irauteko ingurumen lehiakor batean. Berrikuntza prozesuek ideiak eta ideien kudeaketa dituzte oinarri gisa. Teknologiek eta lehiakideek nola eboluzionatzen duten jakitzea ere garrantzitsua da enpresen berrikuntzarako, eta baita ere informazio hori kudeatzea. Beraz, Informazio eta Komunikazio sistemen aplikazioan oinarritzen da tesi hau, zehazkiago Web Semantika eta Teknologia Semantikoetan eta hauen aplikazioa Ideia Kudeaketa eta Zaintza Teknologikoko sistemetan. Berrikuntza eta Zaintza Teknologikoko plataformen kudeatzaileek arazo larriak izaten dituzte jasotako datuekin eta haien kudeaketarekin. Kudeatzaile horiek plataforma ezberdinetan banatutako informazio kantitate handi batekin topo egiten dute eta plataforma horiek ez dira beti elkar eraginkorrak. Beraz, beharrezkoa da plataforma ezberdinetako datuak elkarren artean partekatzea gero datu horiek “ezagutza” bihurtzeko. Gainera, kudeatzaileek egiten dituzten zeregin kopuru handi bat zeregin ez emankorrak dira, denbora eta esfortzu handia suposatzen dute baliozko ezer gehitu gabe. Eta ez hori bakarrik, berrikuntza prozesuko kudeatzaileek zail izaten dute ideia lehiaketen arrakastaren arrazoiak identifikatzen. Gure proposamena Informazio eta Komunikazio Teknologia ezberdinak frogatzea da enpresen berrikuntzako eta zaintza teknologikoko prozesuetan laguntzeko. Honela, hainbat teknologia semantiko eta web teknologia aztertu dira, modelo kontzeptual batzuk eraikitzen eta probatzen benetako erabilpen kasutan lortutako emaitzak konprobatzeko. Tesi honen lorpena plataformen arteko elkar eraginkortasuna ahalbidetzen duen eta prozesuen kudeatzaileen lana errazten duen modelo baten sorpena izan da. Horrela eta sortutako modeloa konplimentatzeko, bi ontologia sortu dira: (1) Gi2Mo- Wave eta (2) Mentions Ontology. Alde batetik, Gi2Mo-Wave ontologia ideien eta ideia lehiaketen testuinguruaren errepresentazio semantikoan oinarritu da. Horrela testuinguruaren analisia errazten da, ideia lehiaketa arrakastatsuak errepikatzea ere errazagoa eginez. Bestalde, Mentions-Ontology ontologia eduki ezberdinen (ideiak edo berriak adibidez) testuetan aipatutako elementuen errepresentazio semantikoan oinarritu da. Horrela, Mentions Ontology ontologiak edukia elkar konektatzeko era bat sortzen du, plataforma ezberdinen edukiaren arteko elkar eraginkortasuna ahalbidetzen. Modelo edo arkitektura hau frogatzeko, Ideia Kudeaketa Sistema eta Zaintza teknologikoko web plataforma berri batzuk garatu dira ere. Plataforma hauek tresna eta ontologia semantikoak dituzte txertatuta, beraien arteko elkar eraginkortasuna ahalbidetzeko. Gainera, teknologia semantikoen aplikazioarekin giza lan kargaren murrizketa nola gauzatu ere frogatzen dugu, Zaintza Teknologikoko edukiaren klasifikazio automatikoan ekarpenak eginez. Bukatzeko, konklusioak bildu dira erabili diren teknologien frogetatik jasotako emaitzetan oinarrituta eta emaitza onenak lortu dituztenak identifikatu dira.El proceso de Innovación es un proceso clave para la supervivencia y evolución de las Pequeñas y Medianas Empresas en un entorno competitivo. Las ideas y la gestión de ideas se consideran la base de la innovación. Recopilar datos sobre cómo evolucionan las actuales tecnologías y los competidores es otro factor clave para la innovación de las empresas. Por lo tanto, esta tesis se centra en la aplicación de Tecnologías de la Información y Comunicación, más concretamente la aplicación de Web Semántica y Tecnologías Semánticas en los Sistemas de Gestión de ideas y de Vigilancia Tecnológica. Los gestores de las plataformas de innovación y de vigilancia tecnológica se enfrentan a muchos problemas relacionados con los datos que recogen y gestionan. Esos gestores se enfrentan a una gran cantidad de información distribuida en diferentes plataformas, no siempre interoperables entre ellas. Es de vital importancia que las diferentes plataformas sean capaces de compartir datos entre ellas, de modo que esos datos puedan convertirse en el conocimiento. Muchas de las tareas realizadas por estos gestores son tareas no productivas y se invierte demasiado tiempo y esfuerzo en realizarlas. Además, los responsables de los procesos de innovación tienen dificultades para identificar por qué un concurso de ideas ha sido un éxito. Nuestra propuesta es analizar diferentes Tecnologías de Información y Comunicación que puedan ayudar a las empresas con sus procesos de Innovación y Vigilancia Tecnológica. Por ello, hemos estudiado varias tecnologías semánticas y Web, hemos desarrollado algunos modelos conceptuales y los hemos probado en diferentes casos de estudio para ver los resultados obtenidos en escenarios reales. El resultado de este trabajo ha sido la creación de una arquitectura que permite la interoperabilidad entre plataformas y que facilita el trabajo de los responsables de los procesos. En este marco, y para complementar la arquitectura, se han desarrollado dos ontologías: (1) Gi2Mo Wave y (2) Mentions Ontology. Gi2Mo Wave se centra en la anotación del contexto de los de ideas, ayudando en el análisis de los concursos y facilitando su replicación. Por otro lado, Mentions Ontology se centra en la anotación de los elementos mencionados en el texto plano de contenidos de diferente índole, como por ejemplo ideas o noticias. Así, Mentions Ontology crea una forma de encontrar relaciones entre contenidos, lo que permite la interoperabilidad entre los contenidos de diferentes plataformas. Con el fin de probar la arquitectura, también se han desarrollado dos plataformas: un Sistema de Gestión de Ideas y un Sistema de Vigilancia Tecnológica. Las plataformas incorporan ontologías semánticas y herramientas para permitir su interoperabilidad. Además, demostramos cómo reducir la carga de trabajo humana, mediante el uso de tecnologías semánticas para la clasificación automática del contenido del proceso de la Vigilancia Tecnológica. Por último, probando las tecnologías y herramientas se han recogido las conclusiones de acuerdo con los resultados obtenidos, identificando las que obtienen los mejores resultados

Role of Semantic web in the changing context of Enterprise Collaboration

In order to compete with the global giants, enterprises are concentrating on their core competencies and collaborating with organizations that compliment their skills and core activities. The current trend is to develop temporary alliances of independent enterprises, in which companies can come together to share skills, core competencies and resources. However, knowledge sharing and communication among multidiscipline companies is a complex and challenging problem. In a collaborative environment, the meaning of knowledge is drastically affected by the context in which it is viewed and interpreted; thus necessitating the treatment of structure as well as semantics of the data stored in enterprise repositories. Keeping the present market and technological scenario in mind, this research aims to propose tools and techniques that can enable companies to assimilate distributed information resources and achieve their business goals

Managing corporate memory on the semantic web

Corporate memory (CM) is the total body of data, information and knowledge required to deliver the strategic aims and objectives of an organization. In the current market, the rapidly increasing volume of unstructured documents in the enterprises has brought the challenge of building an autonomic framework to acquire, represent, learn and maintain CM, and efficiently reason from it to aid in knowledge discovery and reuse. The concept of semantic web is being introduced in the enterprises to structure information in a machine readable way and enhance the understandability of the disparate information. Due to the continual popularity of the semantic web, this paper develops a framework for CM management on the semantic web. The proposed approach gleans information from the documents, converts into a semantic web resource using resource description framework (RDF) and RDF Schema and then identifies relations among them using latent semantic analysis technique. The efficacy of the proposed approach is demonstrated through empirical experiments conducted on two case studies. © 2014 Springer Science+Business Media New York

Business Intelligence Technology, Applications, and Trends

Enterprises are considering substantial investment in Business Intelligence (BI) theories and technologies to maintain their competitive advantages. BI allows massive diverse data collected from virus sources to be transformed into useful information, allowing more effective and efficient production. This paper briefly and broadly explores the business intelligence technology, applications and trends while provides a few stimulating and innovate theories and practices. The authors also explore several contemporary studies related to the future of BI and surrounding fields

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

Technical Research Priorities for Big Data

To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data

Web knowledge bases

Knowledge is key to natural language understanding. References to specific people, places and things in text are crucial to resolving ambiguity and extracting meaning. Knowledge Bases (KBs) codify this information for automated systems — enabling applications such as entity-based search and question answering. This thesis explores the idea that sites on the web may act as a KB, even if that is not their primary intent. Dedicated kbs like Wikipedia are a rich source of entity information, but are built and maintained at an ongoing cost in human effort. As a result, they are generally limited in terms of the breadth and depth of knowledge they index about entities. Web knowledge bases offer a distributed solution to the problem of aggregating entity knowledge. Social networks aggregate content about people, news sites describe events with tags for organizations and locations, and a diverse assortment of web directories aggregate statistics and summaries for long-tail entities notable within niche movie, musical and sporting domains. We aim to develop the potential of these resources for both web-centric entity Information Extraction (IE) and structured KB population. We first investigate the problem of Named Entity Linking (NEL), where systems must resolve ambiguous mentions of entities in text to their corresponding node in a structured KB. We demonstrate that entity disambiguation models derived from inbound web links to Wikipedia are able to complement and in some cases completely replace the role of resources typically derived from the KB. Building on this work, we observe that any page on the web which reliably disambiguates inbound web links may act as an aggregation point for entity knowledge. To uncover these resources, we formalize the task of Web Knowledge Base Discovery (KBD) and develop a system to automatically infer the existence of KB-like endpoints on the web. While extending our framework to multiple KBs increases the breadth of available entity knowledge, we must still consolidate references to the same entity across different web KBs. We investigate this task of Cross-KB Coreference Resolution (KB-Coref) and develop models for efficiently clustering coreferent endpoints across web-scale document collections. Finally, assessing the gap between unstructured web knowledge resources and those of a typical KB, we develop a neural machine translation approach which transforms entity knowledge between unstructured textual mentions and traditional KB structures. The web has great potential as a source of entity knowledge. In this thesis we aim to first discover, distill and finally transform this knowledge into forms which will ultimately be useful in downstream language understanding tasks

Semantic technologies: from niche to the mainstream of Web 3? A comprehensive framework for web Information modelling and semantic annotation

Context: Web information technologies developed and applied in the last decade have considerably changed the way web applications operate and have revolutionised information management and knowledge discovery. Social technologies, user-generated classification schemes and formal semantics have a far-reaching sphere of influence. They promote collective intelligence, support interoperability, enhance sustainability and instigate innovation. Contribution: The research carried out and consequent publications follow the various paradigms of semantic technologies, assess each approach, evaluate its efficiency, identify the challenges involved and propose a comprehensive framework for web information modelling and semantic annotation, which is the thesis’ original contribution to knowledge. The proposed framework assists web information modelling, facilitates semantic annotation and information retrieval, enables system interoperability and enhances information quality. Implications: Semantic technologies coupled with social media and end-user involvement can instigate innovative influence with wide organisational implications that can benefit a considerable range of industries. The scalable and sustainable business models of social computing and the collective intelligence of organisational social media can be resourcefully paired with internal research and knowledge from interoperable information repositories, back-end databases and legacy systems. Semantified information assets can free human resources so that they can be used to better serve business development, support innovation and increase productivity