Scientific Knowledge Object Patterns

Web technology is revolutionizing the way diverse scientific knowledge is produced and disseminated. In the past few years, a handful of discourse representation models have been proposed for the externalization of the rhetoric and argumentation captured within scientific publications. However, there hasn’t been a unified interoperable pattern that is commonly used in practice by publishers and individual users yet. In this paper, we introduce the Scientific Knowledge Object Patterns (SKO Patterns) towards a general scientific discourse representation model, especially for managing knowledge in emerging social web and semantic web. © ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is going to be published in "Proceedings of 15th European Conference on Pattern Languages of Programs", (2011) http://portal.acm.org/event.cfm?id=RE197&CFID=8795862&CFTOKEN=1476113

Vulnerability anti-patterns:a timeless way to capture poor software practices (Vulnerabilities)

There is a distinct communication gap between the software engineering and cybersecurity communities when it comes to addressing reoccurring security problems, known as vulnerabilities. Many vulnerabilities are caused by software errors that are created by software developers. Insecure software development practices are common due to a variety of factors, which include inefficiencies within existing knowledge transfer mechanisms based on vulnerability databases (VDBs), software developers perceiving security as an afterthought, and lack of consideration of security as part of the software development lifecycle (SDLC). The resulting communication gap also prevents developers and security experts from successfully sharing essential security knowledge. The cybersecurity community makes their expert knowledge available in forms including vulnerability databases such as CAPEC and CWE, and pattern catalogues such as Security Patterns, Attack Patterns, and Software Fault Patterns. However, these sources are not effective at providing software developers with an understanding of how malicious hackers can exploit vulnerabilities in the software systems they create. As developers are familiar with pattern-based approaches, this paper proposes the use of Vulnerability Anti-Patterns (VAP) to transfer usable vulnerability knowledge to developers, bridging the communication gap between security experts and software developers. The primary contribution of this paper is twofold: (1) it proposes a new pattern template – Vulnerability Anti-Pattern – that uses anti-patterns rather than patterns to capture and communicate knowledge of existing vulnerabilities, and (2) it proposes a catalogue of Vulnerability Anti-Patterns (VAP) based on the most commonly occurring vulnerabilities that software developers can use to learn how malicious hackers can exploit errors in software

Software patterns to improve knowledge transfer: an experiment.

Patterns for software development have been a hot topic for some time within the object-oriented community. Patterns are part of a software engineering problem-solving discipline. It all started with Design Patterns [11], but gradually patterns were used in a larger number of areas of system development. The goal of patterns within the software community is to create a body of literature to help software developers resolve recurring problems encountered throughout all areas of software development. Patterns help to create a shared language for communicating insight and experience about these problems and their solutions [4]. In this research report, first, a definition of software patterns is given, including some history, an overview of the different kinds of software patterns, the elements of a pattern and the different pattern formats. Secondly, as patterns claim to improve transfer of knowledge, we performed an experiment to test this hypothesis. This experiment is described in Section 2. Finally, Section 3 formulates the conclusions about this experiment.

Path Ranking with Attention to Type Hierarchies

The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base. Prior work has demonstrated the effectiveness of path-ranking based methods, which solve the problem by discovering observable patterns in knowledge graphs, consisting of nodes representing entities and edges representing relations. However, these patterns either lack accuracy because they rely solely on relations or cannot easily generalize due to the direct use of specific entity information. We introduce Attentive Path Ranking, a novel path pattern representation that leverages type hierarchies of entities to both avoid ambiguity and maintain generalization. Then, we present an end-to-end trained attention-based RNN model to discover the new path patterns from data. Experiments conducted on benchmark knowledge base completion datasets WN18RR and FB15k-237 demonstrate that the proposed model outperforms existing methods on the fact prediction task by statistically significant margins of 26% and 10%, respectively. Furthermore, quantitative and qualitative analyses show that the path patterns balance between generalization and discrimination.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Visualizing latent domain knowledge

Knowledge discovery and data mining commonly rely on finding salient patterns of association from a vast amount of data. Traditional citation analysis of scientific literature draws insights from strong citation patterns. Latent domain knowledge, in contrast to the mainstream domain knowledge, often consists of highly relevant but relatively infrequently cited scientific works. Visualizing latent domain knowledge presents a significant challenge to knowledge discovery and quantitative studies of science. We build upon a citation-based knowledge visualization procedure and develop an approach that not only captures knowledge structures from prominent and highly cited works, but also traces latent domain knowledge through low-frequency citation chains. We apply this approach to two cases: (1) identifying cross-domain applications of Pathfinder networks (PFNETs) and (2) clarifying the current status of scientific inquiry of a possible link between Bovine spongiform encephalopathy (BSE), also known as mad cow disease, and a new variant Creutzfeldt-Jakob disease (vCJD), a type of brain disease in human

Interface and Interaction Design Patterns for Intercultural Collaboration

This paper reports about on-going research into interaction design patterns in intercultural remote collaboration. It proposes that interaction and interface design patterns help to communicate and generate new design knowledge in supporting intercultural teamwork. It describes methods used to observe and develop design patterns in collocated, remote and blended collaborative learning and design contexts, and reports preliminary findings of interface and interaction design patterns, which support intercultural remote collaboration

Successful Patterns of Scientific Knowledge Sourcing: Mix and Match

Valuable knowledge emerges increasingly outside of firm boundaries, in particular in public research institutions and universities. The question is how firms organize their interactions with universities effectively to acquire knowledge and apply it successfully. Literature has so far largely ignored that firms may combine different types of interactions with universities for optimizing their collaboration strategies. We argue conceptually that firms need diverse (broad) and highly developed (deep) combinations of various interactions with universities to maximize returns from these collaborations. Our empirical investigation rests upon a survey of more than 800 firms in Germany. We find that both the diversity and intensity of collaborative engagements with universities propel innovation success. However, broadening the spectrum of interactions is more beneficial with regard to innovation success. Applying latent class cluster analysis we identify four distinct patterns of interaction. Our findings show that formal forms of interaction (joint/contract) research provide the best balance between joint knowledge development and value capture. --Technology transfer,industry-science links,open innovation,university knowledge