A new implementation of OntoSTEP: Toward flexible generation of ontology and knowledge graphs of EXPRESS-driven data

Barbau et al. (2012) proposed OntoSTEP that translates the STandard for the Exchange of Product Model Data (STEP) schema and its instances to an ontology and knowledge graphs represented in the Web Ontology Language (OWL). OntoSTEP models can be integrated with any OWL models to enrich their semantics. However, the current implementation has several limitations, mainly in (1) supporting the latest ISO 10303 schemas and (2) generating various representation types depending on the purpose of use. We present an improved implementation of OntoSTEP to overcome these limitations. In this paper, we demonstrate that the new implementation can successfully translate STEP schemas and instances in a faster and more flexible way, thus furthering the adoption of the full capabilities of ISO 10303. By encoding STEP entities in OWL, we facilitate integration with other standards through knowledge graphs.

Three Representation Types for Systems of Forms and Linear Maps

We consider systems of bilinear forms and linear maps as representations of a graph with undirected and directed edges. Its vertices represent vector spaces; its undirected and directed edges represent bilinear forms and linear maps, respectively. We prove that if the problem of classifying representations of a graph has not been solved, then it is equivalent to the problem of classifying representations of pairs of linear maps or pairs consisting of a bilinear form and a linear map. Thus, there are only two essentially different unsolved classification problems for systems of forms and linear maps.

On matrix representations of oversemigroups of semigroups generated by mutually annihilating 2-potent and 2-nilpotent elements

Among the old results, there are only some results on the representation type of semigroups, namely, for a finite quite simple semigroup (I. S. Ponizovsky) and some semigroups of all transformations of a finite set (I. S. Ponizovsky, C. Ringel); these papers were discussed on finite representation type. If we talk about new results, and even for semigroup classes, then it should be noted works on representations of the semigroups generated by idempotents with partial zero multiplication (V. M. Bondarenko, O. M. Tertychna), semigroups generated by the potential elements (V. M. Bondarenko, O. V. Zubaruk) and representations of direct products of the symmetric second-order semigroup (V. M. Bondarenko, E. M. Kostyshyn). Such semigroups can have both a finite and infinite representation type. V. M. Bondarenko and Ja. V. Zatsikha described representation types of the third-order semigroups over a field, and indicate the canonical form of the matrix representations for any semigroup of finite representation type. This article is devoted to the study of similar problems for oversemigroups of commutative semigroups.

Data Pre-Processing and Customized Onto-Graph Construction for Knowledge Extraction in Healthcare Domain of Semantic Web

Present electronic world produces enormous amount of data every second in various formats, especially in healthcare units. To efficiently utilize the available data by representing it in the machine readable form, the concept of Semantic web stepped in progressing towards automated knowledge discovery process. In this paper, comprehensive pre-processing techniques have been proposed for preparing the raw data to be presentable in structured format so as to construct the onto-graph for selected features in a health care domain. Cluster based Missing Value Imputation Algorithm (CMVI) has been proposed to enhance the quality of the imputed data which is the most important step during data pre-processing. Missing values were randomly induced into the Pima Indian Diabetic dataset with the missing ratio of 1%, 3% and 5% for each attribute up to 50% of the attributes in the original diabetic dataset. The experimental observations reveal that the quality of the pre-processed data is better compared to raw, unprocessed data in terms of imputation accuracy measured against coefficient of determination (R2 ), Index of agreement (d2 ) and Root Mean Square Error (RMSE).Documented results proved that the proposed techniques are comparatively superior than the traditional approaches with increased R2 & d2 and decreased RMSE scores. Further, importance of knowledge graph and various ontological representation types are discussed in short as construction of .owl file is the first step towards automation in semantic web.

SUMMARIZING INDONESIAN NEWS ARTICLES USING GRAPH CONVOLUTIONAL NETWORK

Multi-document summarization transforms a set of related documents into one concise summary. Existing Indonesian news articles summarizations do not take relationships between sentences into account and heavily depends on Indonesian language tools and resources. In this paper, we employ Graph Convolutional Network (GCN) which accepts word embedding sequence and sentence relationship graph as input for Indonesian news articles summarization. Our system is comprised of four main components, which are preprocess, graph construction, sentence scoring, and sentence selection components. Sentence scoring component is a neural network that uses Recurrent Neural Network (RNN) and GCN to produce the scores of all sentences. We use three different representation types for the sentence relationship graph. Sentence selection component then generates summary with two different techniques, which are by greedily choosing sentences with the highest scores and by using Maximum Marginal Relevance (MMR) technique. The evaluation shows that GCN summarizer with Personalized Discourse Graph (PDG) graph representation system achieves the best results with average ROUGE-2 recall score of 0.370 for 100-word summary and 0.378 for 200-word summary. Sentence selection using greedy technique gives better results for generating 100-word summary, while MMR performs better for generating 200-word summary.

Which properties of the visual stimuli predict the type of representation used in mental rotation?

Two modes of internal representation, holistic and piecemeal transformation, have been reported as a means to perform mental rotation (MR) tasks. The stimulus complexity effect has been proposed as an indicator to disentangle between these two representation types. However, the complexity effect has not been fully confirmed owing to the fact that different performances could result from different types of stimuli. Moreover, whether the non-mirror foils play a role in forcing participants to encode all the information from the stimuli in MR tasks is still under debate. This study aims at testing the association between these two common types of representation with different stimuli in MR tasks. First, the numbers of segments and vertices in polygon stimuli were manipulated to test which property of the visual stimuli is more likely to influence the representation in MR tasks. Second, the role of non-mirror foils was examined by comparing the stimulus complexity effect in both with- and without-non-mirror foils conditions. The results revealed that the segment number affected the slope of the linear function relating response times to rotation angle, but the vertex number in the polygons did not. This suggests that a holistic representation was more likely to be adopted in processing integrated objects, whereas a piecemeal transformation was at play in processing multi-part objects. In addition, the stimulus complexity effect was observed in the with-non-mirror foils condition but not in the without-non-mirror foils one, providing a direct evidence to support the role of non-mirror foils in MR tasks.

Quiver Gauge Theories: Finitude and Trichotomoty

D-brane probes, Hanany-Witten setups and geometrical engineering stand as a trichotomy of standard techniques of constructing gauge theories from string theory. Meanwhile, asymptotic freedom, conformality and IR freedom pose as a trichotomy of the beta-function behaviour in quantum field theories. Parallel thereto is a trichotomy in set theory of finite, tame and wild representation types. At the intersection of the above lies the theory of quivers. We briefly review some of the terminology standard to the physics and to the mathematics. Then, we utilise certain results from graph theory and axiomatic representation theory of path algebras to address physical issues such as the implication of graph additivity to finiteness of gauge theories, the impossibility of constructing completely IR free string orbifold theories and the unclassifiability of N < 2 Yang-Mills theories in four dimensions.