WikiLink: An Encyclopedia-Based Semantic Network for Design Creativity.

This study examined the differential impact among semantic word relationships, i.e., superordinate, subordinate, and attribute, in the usefulness of forming a trace for verbal recognition. Besides examining the impact of semantic word relationships, the ability of the subject to use such relationships in a verbal learning task was also studied. A 3 × 3 × 2 factorial design was applied to the three types of word relationships, three types of instructions to use one of the three word relationships deliberately, and the effect of one or two presentations of target items on the word list. Reaction times were used to check differences in the correct response “No” to distractors on a recognition task that varied according to semantic word relationships to the target word in the word list. Reaction times were further analyzed to see if the semantic instructions for encoding to the subjects differed and whether the number of presentations produced differences in reaction time. There was a significant effect for the type of semantic relationship that a distractor had and for semantic encoding instructions to the subject. However, there was no significant difference between one or two presentations. The interaction of type × criterion (practice) seemed to indicate that the encoding preference for certain semantic relationships did shift between practice trials.


 
 
 This paper devoted the problem of identifying a semantic similarity degree or difference of text in cybersecurity field. The paper presents a method for comparing text documents based on the formation and comparison of the corresponding semantic networks. The directed weighted network of terms, where the nodes of such networks are key terms of the text, and edges are semantic relationships between these terms in the text are considered as a semantic network. The algorithm for formation semantic networks as one of the types of ontologies is also presented. Formation of network of term includes pre-processing of text data, extraction of key terms, construction of undirected network of terms (using the algorithm of horizontal visibility graph), determining undirected connections between terms, and further determining the directions of connections and their weight values. The Frobenius norm of the difference of matrices corresponding to the semantic networks is considered to compare the semantic networks. An identifying the critically different texts that can have similar keywords but different semantic between them is important to ensure cybersecurity. Also, the proposed approach can be helpful while solving the problem of accumulating text data semantically similar in content. In general, this approach can also be used in systems of automatic information retrieval to determine the degree of similarity or difference in the structure and semantics of texts and identify the sources of information that have a destructive impact on the information space.
 
 

A novel function-structure concept network construction and analysis method for a smart product design system

In this paper is proposed a method for representing semantic data and knowledge. The method is based two foundational concepts: semantic link network and adjacency model. The method allows graph presentations of semantic data and it preserves the semantic relationships between the concepts of the domain. Furthermore with the methodit is possible construct relational model of the semantically rich data. The method proposed in this paper enables the preservation of semantic relationships between elements of the domain and it produces reasoning rules for the domain knowledge. The visualization can be done by using semantic link network (1) and adjacency relation system based graph (2). Adjacency relation systems enable the restriction of search space and thus limit the search times used by queries. In addition adjacency relation systems can be also converted into relational model. In this paper the emphasis is on the construction of semantic link network and adjacency relation system based on the semantic link network. The paper is organized as follows. In the first section the related research is discussed. In the second section the basic conversion method from semantic link network to adjacency relation system is introduced. In the third section visualization method is introduced by small example. Finally concluding remarks are given.

In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

Multi-criteria linear optimization classifier with semantically weighted kernels for Chinese word formation pattern prediction

Information retrieval systems can be made more effective by providing more expressive query languages for users to specify their information need. This paper argues that this can be achieved through the use of semantic relations as query primitives, and describes a new technique for extracting semantic relations from an online dictionary. In contrast to existing research, this technique involves the composition of basic semantic relations, a process akin to constrained spreading activation in semantic networks. The proposed technique is evaluated in the context of extracting semantic relations that are relevant for retrieval from a corpus of pictures.

Child semantic development includes learning the meaning of words as well as the semantic relations among words. A presumed outcome of semantic development is the formation of a semantic network that reflects this knowledge. We present an algorithm for simultaneously learning word meanings and gradually growing a semantic network, which adheres to the cognitive plausibility requirements of incrementality and limited computations. We demonstrate that the semantic connections among words in addition to their context is necessary in forming a semantic network that resembles an adult’s semantic knowledge.

In this paper, we focus on the visualization of heterogeneous semantic networks obtained from multiple data sources. A semantic network comprising a set of entities and relationships is often used for representing knowledge derived from textual data or database records. Although the semantic networks created for the same domain at different data sources may cover a similar set of entities, these networks could also be very different because of naming conventions, coverage, view points, and other reasons. Since digital libraries often contain data from multiple sources, we propose a visualization tool to integrate and analyze the differences among multiple social networks. Through a case study on two terrorism-related semantic networks derived from Wikipedia and Terrorism Knowledge Base (TKB) respectively, the effectiveness of our proposed visualization tool is demonstrated.

An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for constructing the semantic link network. The basic premise of this work is that the semantics of a web page can be reflected by a set of keywords, and the semantic relationship between two web pages can be determined by the semantic relationship between their keyword sets. The approach adopts the data mining algorithms to discover the semantic relationships between keyword sets, and then uses deductive and analogical reasoning to enrich the semantic relationships. The proposed algorithms have been implemented. Experiment shows that the approach is feasible.

With the development of big data, the heuristic query based on the semantic relationship network has become a hot topic, which attracts much attention. Due to the complex relationship between data records, the traditional query technologies cannot satisfy the requirements of users. To this end, this paper proposes a heuristic query method based on the semantic relationship network, which first constructs the semantic relationship model, and then expands the query based on the constructed semantic relationship network. The experiments demonstrate the reasonableness, high precision of our method.

Few-shot object detection has arisen to address the issue of data scarcity due to the long-tail distribution problem. The goal of few-shot object detection is to allow a pre-trained detector to generalize novel objects with few-shot data samples. However, few-shot object detectors misclassify novel objects into pre-trained base classes frequently. Existing methods applied a large margin classifier to address this problem, but they do not work well in data scarcity settings. In this paper, we propose graph convolution network-based semantic relation network to capture the semantic relations for few-shot object detection. Regardless of the amount of data, the semantic relations among object classes remain constant, which can support the classification of novel objects. Semantic relations are trained by the attention-based module adaptively to training data. Graph convolution network updates the object classifier using semantic relations. The object classifier is integrated and trained with the visual features. Through the comparison experiments with Two-stage Fine-tuning Approach (TFA), we observe that our proposed method achieves higher performance on few-shot settings than TFA.

The paper presents expert systems as a new generation of information storage and retrieval systems which may consider ably improve the processes of scientific and technical informa tion exchange and dissemination. Essential features of the expert systems with databases containing scientific or technical information have been specified. For databases in which a semantic network is used as a knowledge representation scheme, an idea of database organization has been described with some attention paid to the problem of extracting factual information from the texts of scientific and technical publications. The process of information retrieval from the database of a scien tific or technical information expert system has also been briefly described.

In the domain of biomedical and health informatics, ontologies are widely used to capture knowledge ranging from bioinformatics such as gene, protein, protein interactions, etc. to clinical/healthcare informatics knowledge such as diseases, symptoms, treatment, medication, etc. Currently, one medical knowledge source that encapsulates a broad spectrum of medical knowledge is the Unified Medical Language System (UMLS), which can be defined as a compendium of diverse medical ontological standards. The primary components of the UMLS are: Semantic Network (UMLS-SN) – designed by interconnecting well-defined semantic types with semantic relationships, and Metathesaurus (UMLS-META) – the base of UMLS system that is comprised of millions of medical concepts from diverse medical standards. However, within the biomedical and health informatics community, the concepts of software engineering and domain modeling (using meta-models such as ERD, UML, and XML) are very successful in designing and implementing biomedical/health domain application models. In the current status, the UMLS-SN is primarily employed for classification of medical concepts in UMLS-META, but UMLS-SN knowledge can't be viewed or employed as a modeling framework for designing ontological models and is restricted to the UMLS environment. Thus, the impact of the biomedical semantics captured by UMLS-SN might be minimal in medical facilities, research and healthcare organizations that are highly influenced by software engineering, meta-models and domain model-based practices. In order fill this gap, the authors propose a meta-modeling framework for UMLS-SN based on the UML Profile (built using UML meta-model) that will result in a customized domain specific meta-model. This specialized meta-model that encapsulates the medical knowledge semantics of UMLS-SN can then be employed for designing ontological models or relevant healthcare application models and simultaneously be coherent with software meta-models and domain modeling practices.

O texto dos dicionários escolares se utiliza de ilustrações para complementar as definições. Nosso objetivo com este artigo é investigar e descrever os tipos de relações semânticas intersemióticas existentes entre texto e imagem nos verbetes ilustrados dos dicionários escolares tipo 2. Adotamos o modelo proposto por Royce (1998) para o estudo das relações intersemióticas entre texto e imagem. Desenhamos uma pesquisa quati-qualitativa em que verificamos a ocorrência ou não da complementaridade intersemiótica entre o texto do verbete e a ilustração por meio das seguintes relações semânticas: repetição, sinonímia, meronímia, hiponímia, antonímia e colocação. Os resultados deste estudo demostram que a complementaridade intersemiótica ocorre em quase 90% dos verbetes ilustrados, destacando-se a relação semântica de sinonímia.