Information Access in a Multilingual World: Transitioning from Research to Real-World Applications

Multilingual Information Access (MLIA) is at a turning point wherein substantial real-world applications are being introduced after fifteen years of research into cross-language information retrieval, question answering, statistical machine translation and named entity recognition. Previous workshops on this topic have focused on research and small- scale applications. The focus of this workshop was on technology transfer from research to applications and on what future research needs to be done which facilitates MLIA in an increasingly connected multilingual world

The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data

Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies. Recently, there have been substantial developments in the field of patent analytics, which describes the science of analysing large amounts of patent information to discover trends. We define Intellectual Property Analytics (IPA) as the data science of analysing large amount of IP information, to discover relationships, trends and patterns for decision making. In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse intellectual property data. This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles. The bibliographic information of the articles are analysed, followed by a discussion of the articles divided in four main categories: knowledge management, technology management, economic value, and extraction and effective management of information. We hope research scholars and industrial users, may find this review helpful when searching for the latest research efforts pertaining to intellectual property analytics.The authors would like to acknowledge support of the Engineering and Physical Sciences Research Council (EPSRC)

Simulation in manufacturing and business: A review

Copyright @ 2009 Elsevier B.V.This paper reports the results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 to provide an up-to-date picture of the role of simulation techniques within manufacturing and business. The review is characterised by three factors: wide coverage, broad scope of the simulation techniques, and a focus on real-world applications. A structured methodology was followed to narrow down the search from around 20,000 papers to 281. Results include interesting trends and patterns. For instance, although discrete event simulation is the most popular technique, it has lower stakeholder engagement than other techniques, such as system dynamics or gaming. This is highly correlated with modelling lead time and purpose. Considering application areas, modelling is mostly used in scheduling. Finally, this review shows an increasing interest in hybrid modelling as an approach to cope with complex enterprise-wide systems

DERIVING TECHNOLOGY ROADMAPS WITH TECH MINING TECHNIQUES

Technology monitoring has been a knowledge intensive and time-consuming task for IT managers or domain experts. Tech mining techniques can be used to mitigate these efforts. This paper proposes a technology monitoring framework based on tech mining techniques to facilitate the derivative of information and communication technology (ICT) roadmaps. With this framework, a tech mining engine is able to allocate the most relevant documents which describe a category of technologies. Domain experts were participated in a scan meeting to verify the generated roadmaps based on the selected cluster of documents. The draft roadmaps can be further articulated with domain experts\u27 judgment for technology forecasting and assessment

The Power of Patents: Leveraging Text Mining and Social Network Analysis to Forecast IoT Trends

Technology has become an indispensable competitive tool as science and technology have progressed throughout history. Organizations can compete on an equal footing by implementing technology appropriately. Technology trends or technology lifecycles begin during the initiation phase. Finally, it reaches saturation after entering the maturity phase. As technology reaches saturation, it will be removed or replaced by another. This makes investing in technologies during this phase unjustifiable. Technology forecasting is a critical tool for research and development to determine the future direction of technology. Based on registered patents, this study examined the trends of IOT technologies. A total of 3697 patents related to the Internet of Things from the last six years of patenting have been gathered using lens.org for this purpose. The main people and companies were identified through the creation of the IOT patent registration cooperation network, and the main groups active in patent registration were identified by the community detection technique. The patents were then divided into six technology categories: Safety and Security, Information Services, Public Safety and Environment Monitoring, Collaborative Aware Systems, Smart Homes/Buildings, and Smart Grid. And their technical maturity was identified and examined using the Sigma Plot program. Based on the findings, information services technologies are in the saturation stage, while both smart homes/buildings, and smart grid technologies are in the saturation stage. Three technologies, Safety and Security, Public Safety and Environment Monitoring, and Collaborative Aware Systems are in the maturity stage

Graph Representation Learning Towards Patents Network Analysis

Patent analysis has recently been recognized as a powerful technique for large companies worldwide to lend them insight into the age of competition among various industries. This technique is considered a shortcut for developing countries since it can significantly accelerate their technology development. Therefore, as an inevitable process, patent analysis can be utilized to monitor rival companies and diverse industries. This research employed a graph representation learning approach to create, analyze, and find similarities in the patent data registered in the Iranian Official Gazette. The patent records were scrapped and wrangled through the Iranian Official Gazette portal. Afterward, the key entities were extracted from the scrapped patents dataset to create the Iranian patents graph from scratch based on novel natural language processing and entity resolution techniques. Finally, thanks to the utilization of novel graph algorithms and text mining methods, we identified new areas of industry and research from Iranian patent data, which can be used extensively to prevent duplicate patents, familiarity with similar and connected inventions, Awareness of legal entities supporting patents and knowledge of researchers and linked stakeholders in a particular research field.Comment: 7 Pages, 12 Figures, 7 Table

The scholarly impact of TRECVid (2003-2009)

This paper reports on an investigation into the scholarly impact of the TRECVid (TREC Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis a vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams ‘scored’ highly against the evaluation criteria and the relationship between ‘top scoring’ teams at TRECVid and the ‘top scoring’ papers in terms of citations is analysed. A strong relationship was found between ‘success’ at TRECVid and ‘success’ at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed

Facilitating Technology Transfer by Patent Knowledge Graph

Technologies are one of the most important driving forces of our societal development and realizing the value of technologies heavily depends on the transfer of technologies. Given the importance of technologies and technology transfer, an increasingly large amount of money has been invested to encourage technological innovation and technology transfer worldwide. However, while numerous innovative technologies are invented, most of them remain latent and un-transferred. The comprehension of technical documents and the identification of appropriate technologies for given needs are challenging problems in technology transfer due to information asymmetry and information overload problems. There is a lack of common knowledge base that can reveal the technical details of technical documents and assist with the identification of suitable technologies. To bridge this gap, this research proposes to construct knowledge graph for facilitating technology transfer. A case study is conducted to show the construction of a patent knowledge graph and to illustrate its benefit to finding relevant patents, the most common and important form of technologies

Gamification in Proprietary Innovation: Identifying a Technical Framework Based on Patent Data.

This paper reveals dominant patterns of gamification in proprietary innovation and develops a technical framework. In recent years, a rash increase in securitizing gamification-related inventions has taken place. By analyzing the content of 134 unique patents from USPTO and EPO with an in-depth raw data text analysis, the technical background is explored holistically. To discover meaningful patterns and thus to derive implications from the patent data they are visually summarized. Especially predominant are the topics of device, data, user and game. Based on the nature of the data, being evidence-based and future directed, our technical framework integrates these patterns and sets it into relation. An additional analysis provides further insights into fundamental game elements. As patents serve as a decisive indicator of future product introductions, the information gathered in this paper represents essential strategic information to guide practitioners and researchers in the area of gamification

Technology Forecasting Using Data Mining and Semantics: First Annual Report

The planning and management of research and development is a challenging process which is compounded by the large amounts of information which is available. The goal of this project is to mine science and technology databases for patterns and trends which facilitate the formation of research strategies. Examples of the types of information sources which we exploit are diverse and include academic journals, patents, blogs and news stories. The intended outputs of the project include growth forecasts for various technological sectors (with an emphasis on sustainable energy), an improved understanding of the underlying research landscape, as well as the identification of influential researchers or research groups. This paper focuses on the development of techniques to both organize and visualize the data in a way which reflects the semantic relationships between keywords. We studied the use of the joint term frequencies of pairs of keywords, as a means of characterizing this semantic relationship – this is based on the intuition that terms which frequently appear together are more likely to be closely related. Some of the results reported herein describe: (1) Using appropriate tools and methods, exploitable patterns and information can certainly be extracted from publicly available databases, (2) Adaptation of the Normalized Google Distance (NGD) formalism can provide measures of keyword distances that facilitate keyword clustering and hierarchical visualization, (3) Further adaptation of the NGD formalism can be used to provide an asymmetric measure of keyword distances to allow the automatic creation of a keyword taxonomy, and (4) Adaptation of the Latent Semantic Approach (LSA) can be used to identify concepts underlying collections of keywords