Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Industrial symbiosis implementation by leveraging on process efficiency methodologies

Resource efficiency is a crucial step for manufacturing companies to improve their operations performance and to reduce waste generation. However, there is no guarantee of a zero waste scenario and companies need to look for new strategies to complement their resource efficiency vision. Therefore, it is important to enroll in an industrial symbiosis strategy as a means to maximize industrial value capturing through the exchange of resources (waste, energy, water and by-products) between different processes and companies. Within this, it is crucial to quantify and characterize the waste, e.g. to have clear understanding of the potential industrial symbiosis hot spots among the processes. For such characterization, it is proposed to use an innovative process efficiency assessment approach. This empowers a clear understanding and quantification of efficiency that identifies industrial symbiosis hot spots (donors) in low efficiency process steps, and enables a plausible definition of potential cold spots (receivers), in order to promote the symbiotic exchanges

Mediating and catalysing innovation: A framework for anticipating the standardisation needs of emerging technologies

The development of technology strategies are often supported by strategic frameworks. Although standards can be critical in fostering technological innovation, particularly by supporting knowledge diffusion, their importance is often neglected by commonly used strategic frameworks. This paper presents a framework which uses the knowledge that needs to transition between key anticipated innovation activities to anticipate potential standardisation needs for emerging technologies. The framework draws attention to strategic considerations and dimensions that might otherwise be overlooked, including different types of standards; standardisation stakeholders; the alignment, coordination, and sequencing of standards; and how these all change over time. A technology roadmapping based framework was used because it explicitly characterises the alignment, coordination, and sequencing of innovation activities (over time) and can be configured to draw out information against the other above strategic considerations and dimensions. The principles and utility of the framework are demonstrated in three contrasting case studies: synthetic biology, additive manufacturing, and smart grid. These show how standards mediate between innovation actors by codifying and diffusing knowledge and can enhance and catalyse innovation. The proposed framework can be used to reveal where standards might be used to support innovation, better characterise the types of standards needed, identify the stakeholders needed to develop them, and highlight any potential alignment, coordination, and sequencing issues related to standardisation activities.Thanks are due to colleagues in BIS, BSI, TSB and Innovate UK for insights, useful conversations and advice on technological domains, and to BIS, BSI and The Gatsby Charitable Foundation, United Kingdom (GA3230) for their financial support. Thanks also due to two anonymous reviewers who helped to refine and more clearly articulate the messages in the article.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.technovation.2015.11.00

A novel framework to improve motion planning of robotic systems through semantic knowledge-based reasoning

The need to improve motion planning techniques for manipulator robots, and new effective strategies to manipulate different objects to perform more complex tasks, is crucial for various real-world applications where robots cooperate with humans. This paper proposes a novel framework that aims to improve the motion planning of a robotic agent (a manipulator robot) through semantic knowledge-based reasoning. The Semantic Web Rule Language (SWRL) was used to infer new knowledge based on the known environment and the robotic system. Ontological knowledge, e.g., semantic maps, were generated through a deep neural network, trained to detect and classify objects in the environment where the robotic agent performs. Manipulation constraints were deduced, and the environment corresponding to the agent’s manipulation workspace was created so the planner could interpret it to generate a collision-free path. For reasoning with the ontology, different SPARQL queries were used. The proposed framework was implemented and validated in a real experimental setup, using the planning framework ROSPlan to perform the planning tasks. The proposed framework proved to be a promising strategy to improve motion planning of robotics systems, showing the benefits of artificial intelligence, for knowledge representation and reasoning in robotics.info:eu-repo/semantics/publishedVersio