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

Domain knowledge specification for energy tuning

To overcome the challenges of energy consumption of HPC systems, the European Union Horizon 2020 READEX (Runtime Exploitation of Application Dynamism for Energy-efficient Exascale computing) project uses an online auto-tuning approach to improve energy efficiency of HPC applications. The READEX methodology pre-computes optimal system configurations at design-time, such as the CPU frequency, for instances of program regions and switches at runtime to the configuration given in the tuning model when the region is executed. READEX goes beyond previous approaches by exploiting dynamic changes of a region's characteristics by leveraging region and characteristic specific system configurations. While the tool suite supports an automatic approach, specifying domain knowledge such as the structure and characteristics of the application and application tuning parameters can significantly help to create a more refined tuning model. This paper presents the means available for an application expert to provide domain knowledge and presents tuning results for some benchmarks.Web of Science316art. no. E465

Exploiting Domain Knowledge in Making Delegation Decisions

@inproceedings{conf/admi/EmeleNSP11, added-at = {2011-12-19T00:00:00.000+0100}, author = {Emele, Chukwuemeka David and Norman, Timothy J. and Sensoy, Murat and Parsons, Simon}, biburl = {http://www.bibsonomy.org/bibtex/20a08b683088443f1fd36d6ef28bf6615/dblp}, booktitle = {ADMI}, crossref = {conf/admi/2011}, editor = {Cao, Longbing and Bazzan, Ana L. C. and Symeonidis, Andreas L. and Gorodetsky, Vladimir and Weiss, Gerhard and Yu, Philip S.}, ee = {http://dx.doi.org/10.1007/978-3-642-27609-5_9}, interhash = {1d7e7f8554e8bdb3d43c32e02aeabcec}, intrahash = {0a08b683088443f1fd36d6ef28bf6615}, isbn = {978-3-642-27608-8}, keywords = {dblp}, pages = {117-131}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2011-12-19T00:00:00.000+0100}, title = {Exploiting Domain Knowledge in Making Delegation Decisions.}, url = {http://dblp.uni-trier.de/db/conf/admi/admi2011.html#EmeleNSP11}, volume = 7103, year = 2011 }Postprin

Composition of Biochemical Networks using Domain Knowledge

Graph composition has applications in a variety of practical applications. In drug development, for instance, in order to understand possible drug interactions, one has to merge known networks and examine topological variants arising from such composition. Similarly, the design of sensor nets may use existing network infrastructures, and the superposition of one network on another can help with network design and optimisation. The problem of network composition has not received much attention in algorithm and database research. Here, we work with biological networks encoded in Systems Biology Markup Language (SBML), based on XML syntax. We focus on XML merging and examine the algorithmic and performance challenges we encountered in our work and the possible solutions to the graph merge problem. We show that our XML graph merge solution performs well in practice and improves on the existing toolsets. This leads us into future work directions and the plan of research which will aim to implement graph merging primitives using domain knowledge to perform composition and decomposition on specific graphs in the biological domain

Deriving item features relevance from collaborative domain knowledge

An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based filtering). Collaborative algorithms have been proven to achieve better recommendation quality then content based algorithms in a variety of scenarios, being more effective in modeling user behaviour. However, they can not be applied when items have no interactions at all, i.e. cold start items. Content based algorithms, which are applicable to cold start items, often require a lot of feature engineering in order to generate useful recommendations. This issue is specifically relevant as the content descriptors become large and heterogeneous. The focus of this paper is on how to use a collaborative models domain-specific knowledge to build a wrapper feature weighting method which embeds collaborative knowledge in a content based algorithm. We present a comparative study for different state of the art algorithms and present a more general model. This machine learning approach to feature weighting shows promising results and high flexibility

The influencing mechanism of manufacturing scene change on process domain knowledge reuse

It is necessary for a enterprise to reuse outside process domain knowledge to develop intelligent manufacturing technology. The key factors influencing knowledge reuse in digital manufacturing scene are manufacturing activities and PPR (Products, Processes and Resources) related to knowledge modeling, enterprise and integrated systems related to knowledge utilizing. How these factors influence knowledge modeling and utilizing is analyzed. Process domain knowledge reuse across the enterprises consists of knowledge reconfiguration and integrated application with CAx systems. The module-based knowledge model and loosely-coupled integration application of process domain knowledge are proposed. The aircraft sheet metal process domain knowledge reuse is taken as an example, and it shows that the knowledge reuse process can be made flexible and rapid

Idea-caution before exploitation:the use of cybersecurity domain knowledge to educate software engineers against software vulnerabilities

The transfer of cybersecurity domain knowledge from security experts (‘Ethical Hackers’) to software engineers is discussed in terms of desirability and feasibility. Possible mechanisms for the transfer are critically examined. Software engineering methodologies do not make use of security domain knowledge in its form of vulnerability databases (e.g. CWE, CVE, Exploit DB), which are therefore not appropriate for this purpose. An approach based upon the improved use of pattern languages that encompasses security domain knowledge is proposed