Generalizing GAMETH: Inference rule procedure..

In this paper we present a generalisation of GAMETH framework, that play an important role in identifying crucial knowledge. Thus, we have developed a method based on three phases. In the first phase, we have used GAMETH to identify the set of “reference knowledge”. During the second phase, decision rules are inferred, through rough sets theory, from decision assignments provided by the decision maker(s). In the third phase, a multicriteria classification of “potential crucial knowledge” is performed on the basis of the decision rules that have been collectively identified by the decision maker(s).Knowledge Management; Knowledge Capitalizing; Managing knowledge; crucial knowledge;

Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment

Image Quality Assessment (IQA) with reference images have achieved great success by imitating the human vision system, in which the image quality is effectively assessed by comparing the query image with its pristine reference image. However, for the images in the wild, it is quite difficult to access accurate reference images. We argue that it is possible to learn reference knowledge under the No-Reference Image Quality Assessment (NR-IQA) setting, which is effective and efficient empirically. Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images. And then, to achieve fast convergence and avoid overfitting, we further propose an inductive bias regularization. Such a framework not only solves the congenital defects of NR-IQA but also improves the feature extraction framework, enabling it to express more abundant quality information. Surprisingly, our method utilizes less input while obtaining a more significant improvement compared to the teacher models. Extensive experiments on eight standard NR-IQA datasets demonstrate the superior performance to the state-of-the-art NR-IQA methods, i.e., achieving the PLCC values of 0.917 (vs. 0.884 in LIVEC) and 0.686 (vs. 0.661 in LIVEFB)

Challenges in Capitalizing Knowledge in Innovative Product Design Process.

Capitalizing on company’s knowledge is increasingly being recognized in a private organizations environment since managing knowledge productivity is considered a source of competitive advantage. In this paper we present a generalization of GAMETH framework, that play an important role in identifying crucial knowledge used and created in innovative product design process. Thus, we have developed a method based on three phases. In the first phase, we have used GAMETH to identify the set of “reference knowledge”. During the second phase, decision rules are inferred, through rough sets theory, from decision assignments provided by the decision maker(s). In the third phase, a multicriteria classification of “potential crucial knowledge” is performed on the basis of the decision rules that have been collectively identified by the decision maker(s).Dominance Rough set approach; Decision rules; Multi- criteria classification; crucial knowledge; Knowledge Capitalizing;

Solving Meno\u27s Puzzle, Defeating Merlin\u27s Subterfuge: Bodies of Reference Knowledge and Archaeological Inference

THE MIND OF Lewis Binford is nimble and constantly evolving. In part, one can map Binford\u27s prodigious intellectual growth by looking at the research trajectories of his students, who often continue on paths they began under his tutelage. In my case, certainly, this is very true. When I arrived at the University of New Mexico in the late 1970s and early 1980s, Binford was exploring the nature of the archaeological record: how to understand past human organization at a supra-ethnographic scale, what we might learn from bones and site structure, and how to reliably give meaning to the archaeological record. This chapter, harking back to the early 1980s, focuses on the latter and attempts to organize some of the many thoughts that have been offered on the notion initially known as middle-range theory. Fundamental questions in archaeology are: What is it? How old is it? Why did people make it? Why did they stop making it? What do these patterns in artifacts, structures, and so forth mean at a deeper level? When students of archaeology ask and answer these questions, they are confronted with Meno\u27s Puzzle and Merlin\u27s subterfuge. Meno was the imaginary debater with whom Plato puzzled over the detection of Virtue (Evans 1995). If we knew how to recognize Virtue in a person, then we could and would do so. But, if Virtue\u27s distinguishing characteristics are a mystery to us, how will we recognize a virtuous individual when one appears? Mark Twain\u27s Merlin, the resident magician in King Arthur\u27s court, was capable of discerning activities happening at great distances, even 10,000 miles away. Shockingly, however, Merlin could not reckon the contents of the Connecticut Yankee\u27s pocket, though both were located in the same room. In archaeology, three related problems surface. First, without traveling back in time, how can we really know what the past was like? Second, can we learn about the past without imposing the present on the past (Wobst 1978)? That is, can the past somehow speak for itself and tell us something different than we think we already know? Finally, assuming that we can learn about the past, how do we know those knowledge claims are secure

Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty

Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-automated EL systems, while detecting latent factors that affect the EL performance, like corpus-specific features, can provide insights on how to improve a system based on the special characteristics of the underlying corpus. In this paper, we first introduce a consensus-based method to generate difficulty labels for entity mentions on arbitrary corpora. The difficulty labels are then exploited as training data for a supervised classification task able to predict the EL difficulty of entity mentions using a variety of features. Experiments over a corpus of news articles show that EL difficulty can be estimated with high accuracy, revealing also latent features that affect EL performance. Finally, evaluation results demonstrate the effectiveness of the proposed method to inform semi-automated EL pipelines.Comment: Preprint of paper accepted for publication in the 34th ACM/SIGAPP Symposium On Applied Computing (SAC 2019

NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker

Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the reference knowledge base is complete. In practice, however, it is necessary to deal with the case of linking to an entity that is not contained in the knowledge base (NIL entity). Recent works have shown that, instead of focusing only on affinities between mentions and entities, considering inter-mention affinities can be used to represent NIL entities by producing clusters of mentions. At the same time, inter-mention affinities can help to substantially improve linking performance for known entities. With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities. The approach clusters mentions and entities based on dense representations from Transformers and resolves conflicts (if more than one entity is assigned to a cluster) by computing transitive mention-entity affinities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL entities. Further, we apply the presented approach to an actual EL task, namely to knowledge graph population by linking entities in Wikipedia listings, and provide an analysis of the outcome.Comment: Preprint of a paper in the research track of the 20th Extended Semantic Web Conference (ESWC'23

Incorporating stakeholders’ knowledge in group decision-making

International audienc

The Knowledge Base as an Extension of Distance Learning Reference Service

This study explores knowledge bases as extension of reference services for distance learners. Through a survey and follow-up interviews with distance learning librarians, this paper discusses their interest in creating and maintaining a knowledge base as a resource for reference services to distance learners. It also investigates their perceptions about the feasibility and practicality of a reference knowledge base. Primary findings indicate that the majority of participants view a knowledge base as an extension of distance learning reference services positively but see issues related to workload and quality control, in particular, which might hinder the development and maintenance of this type of repository