11,122 research outputs found
Public or private economies of knowledge: The economics of diffusion and appropriation of bioinformatics tools
The past three decades have witnessed a period of great turbulence in the economies of biological knowledge, during which there has been great uncertainty as to how and where boundaries could be drawn between public or private knowledge especially with regard to the explosive growth in biological databases and their related bioinformatic tools. This paper will focus on some of the key software tools developed in relation to bio-databases. It will argue that bioinformatic tools are particularly economically unstable, and that there is a continuing tension and competition between their public and private modes of production, appropriation, distribution, and use. The paper adopts an ?instituted economic process? approach, and in this paper will elaborate on processes of making knowledge public in the creation of ?public goods?. The question is one of continuously creating and sustaining new institutions of the commons. We believe this critical to an understanding of the division and interdependency between public and private economies of knowledge
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Connecting Researchers with Companies for University-Industry Collaboration
Nowadays, companies are spending more time and money to enhance their innovation ability to respond to the increasing market competition. The pressure makes companies seek help from external knowledge, especially those from academia. Unfortunately, there is a gap between knowledge seekers (companies) and suppliers (researchers) due to the scattered and asymmetric information. To facilitate shared economy, various platforms are designed to connect the two parties. In this context, we design a researcher recommendation system to promote their collaboration (e.g. patent license, collaborative research, contract research and consultancy) based on a research social network with complete information about both researchers and companies. In the recommendation system, we evaluate researchers from three aspects, including expertise relevance, quality and trustworthiness. The experiment result shows that our system performs well in recommending suitable researchers for companies. The recommendation system has been implemented on an innovation platform, InnoCity.
PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
This study provides an efficient approach for using text data to calculate
patent-to-patent (p2p) technological similarity, and presents a hybrid
framework for leveraging the resulting p2p similarity for applications such as
semantic search and automated patent classification. We create embeddings using
Sentence-BERT (SBERT) based on patent claims. We leverage SBERTs efficiency in
creating embedding distance measures to map p2p similarity in large sets of
patent data. We deploy our framework for classification with a simple Nearest
Neighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of
a patent based on the class assignment of the K patents with the highest p2p
similarity. We thereby validate that the p2p similarity captures their
technological features in terms of CPC overlap, and at the same demonstrate the
usefulness of this approach for automatic patent classification based on text
data. Furthermore, the presented classification framework is simple and the
results easy to interpret and evaluate by end-users. In the out-of-sample model
validation, we are able to perform a multi-label prediction of all assigned CPC
classes on the subclass (663) level on 1,492,294 patents with an accuracy of
54% and F1 score > 66%, which suggests that our model outperforms the current
state-of-the-art in text-based multi-label and multi-class patent
classification. We furthermore discuss the applicability of the presented
framework for semantic IP search, patent landscaping, and technology
intelligence. We finally point towards a future research agenda for leveraging
multi-source patent embeddings, their appropriateness across applications, as
well as to improve and validate patent embeddings by creating domain-expert
curated Semantic Textual Similarity (STS) benchmark datasets.Comment: 18 pages, 7 figures and 4 Table
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
Information retrieval and text mining technologies for chemistry
Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European
Community’s Horizon 2020 Program (project reference:
654021 - OpenMinted). M.K. additionally acknowledges the
Encomienda MINETAD-CNIO as part of the Plan for the
Advancement of Language Technology. O.R. and J.O. thank
the Foundation for Applied Medical Research (FIMA),
University of Navarra (Pamplona, Spain). This work was
partially funded by Consellería
de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic
funding of UID/BIO/04469/2013 unit and COMPETE 2020
(POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi
for useful feedback and discussions during the preparation of
the manuscript.info:eu-repo/semantics/publishedVersio
Patent Database: Their Importance in Prior Art Documentation and Patent Search
In knowledge based economies the nation’s economic status depends on the production, distribution and use of knowledge
and information. The recent trend in the economic growth of nations is mainly determined by innovative technological knowhow
of the individuals. Intellectual property has gained attention in this era of knowledge. The vast amount of data generated
through the application of intellectual assets is managed with the help of various in- silico tools. In recent days, the patent
databases have gained importance due to the detailed information available on the granted patent and other details, such as,
legal status of the patent applications, which are not available through any other literature search. This review paper attempts to
describe different types of patent databases available, their unique features, strengths, weakness and their major purpose. This
paper details the information on how to access a patent database, the relevance of patent information obtained from these
databases in prior art search, patent analysis, and the drawbacks present in these patent databases
AI-assisted patent prior art searching - feasibility study
This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy
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