1,721,051 research outputs found
Issues with meta-knowledge
No full textAt the SEKE'99 conference, knowledge engineering researchers held a panel on the merits of meta-knowledge(i.e. problem solving methods and ontologies) for the development of knowledge-based systems. The original panel was framed as a debate on the merits of meta-knowledge for knowledge maintenance[21]. However, the debate quickly expanded. In the end, we were really discussing the merits of different technologies for the specification of reusable components for KBS. In this brief article we record some of the lively debate from that panel and the email exchanges it generated
Meta-Knowledge in systems design: panacea ...or undelivered promise?
No full textIn this study we present a review of the emerging field of meta-knowledge components as practised over the past decade among a variety of practitioners. We use the artificially-defined term 'meta-knowledge' to encompass all those different but overlapping notions used by the Artificial Intelligence and Software Engineering communities to represent reusable modelling frameworks: ontologies, problem-solving methods, experience factories and experience bases, patterns, to name a few. We then elaborate on how meta-knowledge is deployed in the context of system's design to improve its reliability by consistency checking, enhance its reuse potential, and manage its knowledge sharing. We speculate on its usefulness and explore technologies for supporting deployment of meta-knowledge. We argue that, despite the different approaches being followed in systems design by divergent communities, meta-knowledge is present in all cases, in a tacit or explicit form, and its utilisation depends on pragmatic aspects which we try to identify and critically review on criteria of effectiveness
Sharing Experiments Using Open Source Software
No full textAssociated research group: Critical Systems Research GroupWhen researchers want to repeat, improve or refute prior conclusions, it is useful to have a complete and operational description of prior experiments. If those descriptions are overly long or complex, then sharing their details may not be informative. OURMINE is a scripting environment for the development and deployment of data mining experiments. Using OURMINE, data mining novices can specify and execute intricate experiments, while researchers can publish their complete experimental rig alongside their conclusions. This is achievable because of OURMINE's succinctness. For example, this paper presents two experiments documented in the OURMINE syntax. Thus, the brevity and simplicity of OURMINE recommends it as a better tool for documenting, executing, and sharing data mining experiments.Nelson, Adam; Menzies, Tim; Gay, Gregory. (2011). Sharing Experiments Using Open Source Software. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/217397
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Finding Robust Solutions in Requirements Models
No full textAssociated research group: Critical Systems Research GroupSolutions to non-linear requirements engineering problems may be "brittle"; i.e. small changes may dramatically alter solution effectiveness. Hence, it is not enough to just generate solutions to requirements problems- we must also assess solution robustness. The KEYS2 algorithm can generate decision ordering diagrams. Once generated, these diagrams can assess solution robustness in linear time. In experiments with real-world requirements engineering models, we show that KEYS2 can generate decision ordering diagrams in O(N 2). When assessed in terms of terms of (a) reducing inference times, (b) increasing solution quality, and (c) decreasing the variance of the generated solution, KEYS2 out-performs other search algorithms (simulated annealing, ASTAR, MaxWalkSat).Gay, Gregory; Menzies, Tim; Jalali, Omid; Mundy, Gregory; Gilkerson, Beau; Feather, Martin; Kiper, James. (2010). Finding Robust Solutions in Requirements Models. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/217410
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
On the Use of Relevance Feedback in IR-based Concept Location
No full textAssociated research group: Critical Systems Research GroupConcept location is a critical activity during software evolution as it produces the location where a change is to start in response to a modification request, such as, a bug report or a new feature request. Lexical-based concept location techniques rely on matching the text embedded in the source code to queries formulated by the developers. The efficiency of such techniques is strongly dependent on the ability of the developer to write good queries. We propose an approach to augment information retrieval (IR) based concept location via an explicit relevance feedback (RF) mechanism. RF is a two-part process in which the developer judges existing results returned by a search and the IR system uses this information to perform a new search, returning more relevant information to the user. A set of case studies performed on open source software systems reveals the impact of RF on IR based concept location.Gay, Gregory; Haiduc, Sonia; Marcus, Andrian; Menzies, Tim. (2009). On the Use of Relevance Feedback in IR-based Concept Location. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/217423
How to Build Repeatable Experiments
No full textAssociated research group: Critical Systems Research GroupThe mantra of the PROMISE series is "repeatable, improvable, maybe refutable" software engineering experiments. This community has successfully created a library of reusable software engineering data sets. The next challenge in the PROMISE community will be to not only share data, but to share experiments. Our experience with existing data mining environments is that these tools are not suitable for publishing or sharing repeatable experiments.
OURMINE is an environment for the development of data mining experiments. OURMINE offers a succinct notation for describing experiments. Adding new tools to OURMINE, in a variety of languages, is a rapid and simple process. This makes it a useful research tool. Complicated graphical interfaces have been eschewed for simple command-line prompts. This simplifies the learning curve for data mining novices. The simplicity also encourages large scale modification and experimentation with the code.
In this paper, we show the OURMINE code required to reproduce a recent experiment checking how defect predictors learned from one site apply to another. This is an important result for the PROMISE community since it shows that our shared repository is not just a useful academic resource. Rather, it is a valuable resource industry: companies that lack the local data required to build those predictors can use PROMISE data to build defect predictors.Gay, Gregory; Menzies, Tim; Cukic, Bojan; Turhan, Burak. (2008). How to Build Repeatable Experiments. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/217362
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