Semantic Network Knowledge Research Articles

A workflow model for holistic data management and semantic interoperability in quantitative archival research

Abstract Archival research is a complicated task that involves several diverse activities for the extraction of evidence and knowledge from a set of archival documents. The involved activities are usually unconnected, in terms of data connection and flow, making difficult their recursive revision and execution, as well as the inspection of provenance information at data element level. This article proposes a workflow model for holistic data management in archival research: from transcribing and documenting a set of archival documents, to curating the transcribed data, integrating it to a rich semantic network (knowledge graph), and then exploring the integrated data quantitatively. The workflow is provenance-aware, highly recursive and focuses on semantic interoperability, aiming at the production of sustainable data of high value and long-term validity. We provide implementation details for each step of the workflow and present its application in maritime history research. We also discuss relevant quality aspects and lessons learned from its application in a real context.

Word-to-text integration components in second language (L2) reading comprehension

Recognition of individual words serves as an initial basis for comprehension of a written text; yet there are complex word-to-text (WTT) integration processes underlying the comprehension. This study focused on two components of WTT integration, that is, syntactic parsing and semantic association, and assessed how syntactic and semantic network knowledge differentially predicted two types of text comprehension (literal vs. inferential) in second language readers. Participants were 229 adult learners of English language as a foreign language at a Saudi University. A battery of tasks was administrated to measure their reading comprehension, syntactic knowledge (grammatical error correction), and semantic network knowledge (semantic association), together with working memory and vocabulary knowledge/size. Multiple regression analyses showed that both syntactic and semantic network knowledge significantly predicted reading comprehension (disregarding the type of comprehension), controlling for working memory and vocabulary knowledge. Syntactic knowledge, as opposed to semantic network knowledge, was a significant, unique predictor of literal comprehension, whereas a converse pattern was found for inferential comprehension.

A New Multilabel Recognition Framework for Transmission Lines Bolt Defects Based on the Combination of Semantic Knowledge and Structural Knowledge

Automatic inspection of transmission lines has received extensive attention in recent years. Bolts are widely used as fasteners in transmission lines, and their defect images have the characteristics of large intra-class differences and small inter-class differences. In view of this, we proposed a multilabel image recognition framework for bolt defects (RFBD). The framework consists of a visual feature and semantic knowledge network (VFSKnet) and a visual feature and position knowledge network (VFPKnet). To learn the relationship between bolt labels, the VFSKnet utilizes the relation between bolt defect labels as professional posterior knowledge to guide the model. To capture the structured fine-grained features, the VFPKnet extracts and utilizes the significant structural features of bolts. Finally, these two sub-networks, VFSKnet and VFPKnet, are combined into a framework after weighting, i.e. RFBD, for the final recognition. By using the proposed RFBD, the label-level accuracy reaches 93.91%, and the image-level accuracy reaches 83.29%. Experimental results well demonstrate the superiority of the proposed model for bolt defect recognition.

Semantic knowledge networks in education

The article is devoted to the modeling a semantic knowledge networks. The knowledge network is the basic concept of the problem of knowledge management. This is a new discipline that implements the principles of sustainable development of education. The method of constructing a semantic knowledge network allows us to analyze the connections between educational disciplines: “Economic Cybernetics”, “Algorithms and Programming” and “Calculus”. The paper compares the topological characteristics of the concept graphs related to various disciplines. We develop the algorithm to implement the subject area model in the form of a semantic knowledge network. 125 concepts are analyzed that provide optimal mastering disciplines and establish the connection between them.

Decreased meta-memory is associated with early tauopathy in cognitively unimpaired older adults.

The ability to accurately judge memory efficiency (meta-memory monitoring) for newly learned (episodic) information, is decreased in older adults and even worse in Alzheimer's disease (AD), whereas no differences have been found for semantic meta-memory. The pathological substrates of this phenomenon are poorly understood. Here, we examine the association between meta-memory monitoring for episodic and semantic information to the two major proteinopathies in AD: amyloid (Aβ) and tau pathology in a group of cognitively unimpaired older adults. All participants underwent multi-tracer PET and meta-memory monitoring was assessed using a feeling-of-knowing (FOK) task for non-famous (episodic) and famous (semantic) face-name pairs. Whole brain voxel-wise correlations between meta-memory and PET data were conducted (controlling for memory), as well as confirmatory region-of-interest analyses. Participants had reduced episodic FOK compared to semantic FOK. Decreased episodic FOK was related to tauopathy in the medial temporal lobe regions, including the entorhinal cortex and temporal pole, whereas decreased semantic FOK was related to increased tau in regions associated with the semantic knowledge network. No association was found with Aβ-pathology. Alterations in the ability to accurately judge memory efficiency (in the absence of memory decline) may be a sensitive clinical indicator ofAD pathophysiology in thepre-symptomaticphase.

Semantic knowledge network inference across a range of stakeholders and communities of practice

This paper provides empirical and experimental assessments of thematic knowledge discourses based on two case studies in the US Virgin Islands and Florida. We utilize a latent semantic indexing analysis over natural language corpus to classify and categorize knowledge categories. We computed TF*IDF scores and associated co-occurrence Jaccard similarity scores to construct semantic knowledge networks. Using network analysis, we computed structural metrics over four composite groups: neighbor-based, centrality, equivalence and position. The analysis show that structural network characteristics of environmental knowledge can exponentially predict associations between knowledge categories. We show that connectivity play a critical role on acquisition, representation, and diffusion patterns of knowledge within local communities. We provide evidence of a global prevalence of a shared knowledge core. We show that core social-ecological attributes of knowledge follow scale-free, power law distributions and stable, equilibrium network structures. We identify two distinct models of bidirectional translation: a bottom-up and a top-down.

A collaboration centric approach for building the semantic knowledge network for knowledge advantage machine

Knowledge advantage machine (KaM) is an advanced system for knowledge exploitation. In this paper, we propose a Collaboration Centric Behavior Model, which helps one or more knowledge-workers to discover and link useful knowledge objects dubbed JANs into a semantic knowledge network. The JAN is constructed as a semantic web service to semantically present three categories of service behavior: expected service behavior that presents what requestor expects it to serve; actual service behavior that presents how it offers its service; and quality evaluation that presents whether its service behavior is consistent with requestor’s expectation by checking conformance. On the basis of the KaM architecture, we build a process model to implement to discovery a JAN and link different JANs as a personal knowledge network or a group knowledge network. This is illustrated using an academic research scenario. Experimental results show that the proposed method is feasible and effective.

Research of Semantic Network Knowledge Representation and Query Algorithm Based on Relational Model

Because of the solid foundation, mature technology, rich products of relational database ,it has been the first consideration for studying and storing the massive data. How to build an effective knowledge base based on the powerful storage and processing capability is always the key issue for the research and application of knowledge engineering and intelligent system ,but also the bottleneck knowledge management turns into the application of technology .In this paper ,a relational model of semantic network has been given and discusses with examples the storage of relational data table based on the semantic network knowledge representation. Once semantic network relational model established, the knowledge base that based on semantic network will be set up. In this paper, Semantic network knowledge base query algorithm is studied. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3502 Full Text: PDF

Diagnosing Learning Disabilities in a Special Education By an Intelligent Agent Based System

The presented paper provides an intelligent agent based classification system for diagnosing and evaluation of learning disabilities with special education students. It provides pedagogy psychology profiles for those students and offer solution strategies with the best educational activities. It provides tools that allow class teachers to discuss psycho functions and basic skills for learning skills, then, performs psycho pedagogy evaluation by comprising a series of strategies in a semantic network knowledge base. The system’s agent performs its classification of student’s disabilities based on its past experience that it got from the exemplars that were classified by expert and acquired in its knowledge base.

The neural correlates of naming and fluency deficits in Alzheimer's disease: an FDG‐PET study

To examine the neural processes associated with language deficits in Alzheimer's disease (AD), and in particular to elucidate the correlates of confrontation naming and word retrieval impairments. Sixty patients with probable AD were included. Confrontation naming was assessed using the number of words spontaneously named correctly on the Boston Naming Test. We recorded the number of additional words stated following phonemic cuing. We also assessed phonemic (FAS) and semantic (supermarket items) fluency. We then correlated performance on each measure with resting cortical metabolic activity using FDG-PET images. We found that poorer ability to spontaneously name an object was associated with hypometabolism of bilateral inferior temporal lobes. In contrast, when a phonemic cue was provided, successful naming under this condition was associated with higher metabolic activity in bilateral inferior frontal gyrus (IFG), right superior frontal gyrus (SFG), left temporal, and occipital regions. Consistent with these findings, we found that poorer semantic fluency was associated with hypometabolism in regions including both IFG and temporal regions, and poorer phonemic fluency was associated with hypometabolism in only left IFG. Across analyses, measures that required cued retrieval were associated with metabolism in the left IFG, whereas measures taxing semantic knowledge were associated with metabolic rate of left temporal cortex. Naming deficits in AD reflect compromise to temporal regions involved in the semantic knowledge network, and frontal regions involved in the controlled retrieval of information from that network.

Automatic crosslingual thesaurus generated from the Hong Kong SAR Police Department Web corpus for crime analysis

AbstractFor the sake of national security, very large volumes of data and information are generated and gathered daily. Much of this data and information is written in different languages, stored in different locations, and may be seemingly unconnected. Crosslingual semantic interoperability is a major challenge to generate an overview of this disparate data and information so that it can be analyzed, shared, searched, and summarized. The recent terrorist attacks and the tragic events of September 11, 2001 have prompted increased attention on national security and criminal analysis. Many Asian countries and cities, such as Japan, Taiwan, and Singapore, have been advised that they may become the next targets of terrorist attacks. Semantic interoperability has been a focus in digital library research. Traditional information retrieval (IR) approaches normally require a document to share some common keywords with the query. Generating the associations for the related terms between the two term spaces of users and documents is an important issue. The problem can be viewed as the creation of a thesaurus. Apart from this, terrorists and criminals may communicate through letters, e‐mails, and faxes in languages other than English. The translation ambiguity significantly exacerbates the retrieval problem. The problem is expanded to crosslingual semantic interoperability. In this paper, we focus on the English/Chinese crosslingual semantic interoperability problem. However, the developed techniques are not limited to English and Chinese languages but can be applied to many other languages. English and Chinese are popular languages in the Asian region. Much information about national security or crime is communicated in these languages. An efficient automatically generated thesaurus between these languages is important to crosslingual information retrieval between English and Chinese languages. To facilitate crosslingual information retrieval, a corpus‐based approach uses the term co‐occurrence statistics in parallel or comparable corpora to construct a statistical translation model to cross the language boundary. In this paper, the text‐based approach to align English/Chinese Hong Kong Police press release documents from the Web is first presented. We also introduce an algorithmic approach to generate a robust knowledge base based on statistical correlation analysis of the semantics (knowledge) embedded in the bilingual press release corpus. The research output consisted of a thesaurus‐like, semantic network knowledge base, which can aid in semantics‐based crosslingual information management and retrieval.

Parallel natural language processing on a semantic network array processor

This paper presents a parallel natural language processing system implemented on a marker-passing parallel AI computer, the Semantic Network Array Processor (SNAP). Our system uses a memory-based parsing approach in which parsing is viewed as a memory search process. Linguistic information is stored as phrasal patterns in a semantic network knowledge base distributed over the memory of the parallel computer. Parsing is performed by recognizing and linking phrasal patterns that reflect a sentence interpretation. This is achieved by propagating markers over the distributed network. We have developed a system capable of processing newswire articles from a particular domain. The paper presents the structure of the system, the memory-based parsing method used, and the performance results obtained. >

Combining semantic networks with multi-attribute utility models: An evaluative data base indexing method

This article presents a method for representing evaluative knowledge by combining techniques from two separate but complementary disciplines. A semantic network knowledge representation is merged with a decision-theoretic multiattribute utility (MAU) model to provide an indexing technique for data bases that store information in the form of judgments or evaluations. Such a merger provides reasoning and inference capabilities that allow the data base to estimate an evaluation even if no direct evidence is available for user queries. In addition to the technique, a sample application in international marketing, called the Country Consultant, is described.

Parallel Knowledge Representation on the Connection Machine

Semantic network systems are a common knowledge representation paradigm used in contemporary AI research. Researchers have become increasingly discouraged, however, with the performance of these systems. As networks grow performance suffers, often to an unacceptable extent. The representations for many AI domains are so large that existing systems cannot execute general inferences on them in an acceptable amount of time-serial representation systems are computationally ineffective for large domains. This is one of the reasons that, in the last few years, researchers have been turning their attention, away from traditional symbolic approaches, toward neural networks and parallel connectionist paradigms. We have been developing PARKA, a symbolic, semantic network knowledge representation system that takes advantage of the CM′s massive parallelism. Our primary motivation is to demonstrate that massive parallelism can be used to provide the run-time improvements necessary to bring large AI systems into realistic applications requiring rapid response time. PARKA′s design allows it to evaluate rapidly enough for everyday use many inferences that are computationally ineffective in serial systems. In this paper we describe the design and implementation of PARKA. We explain our motivations for developing a representation system on the Connection Machine and show how PARKA exploits its design to optimize performance. We show that PARKA effects in O( d) time inferences for which serial systems require O( B d ) time, where d is the network depth and B is the average network branchout. Timings of the system on random networks are also presented. We show that PARKA is capable of effecting top-down inheritance inferences on 16,000-node networks in well under a second.

Redex: The ranging equipment diagnostic expert system

This paper describes REDEX, an advanced prototype expert system that diagnoses hardware failures in the Ranging Equipment (RE) at NASA's Ground Network tracking stations. REDEX will help the RE technician identify faulty circuit cards or modules that must be replaced, and thereby reduce troubleshooting time. It features a highly graphical user interface that uses color block diagrams and layout diagrams to illustrate the location of a fault. A semantic network knowledge representation technique was used to model the design structure of the RE. A catalog of generic troubleshooting rules was compiled to represent heuristics that are applied in diagnosing electronic equipment. Specific troubleshooting rules were identified to represent additional diagnostic knowledge that is unique to the RE Over 50 genetic and 250 specific troubleshooting rules have been derived. REDEX is implemented in Prolog on an IBM PC AT-compatible workstation. Block diagram graphics displays are color-coded to identify signals that have been monitored or inferred to have nominal values, signals that are out of tolerance, and circuit cards and functions that are diagnosed as faulty. A hypertext-like scheme is used to allow the user to easily navigate through the space of diagrams and tables. Over 50 graphic and tabular displays have been implemented. REDEX is currently being evaluated in a stand-alone mode using simulated RE fault scenarios. It will soon be interfaced to the RE and tested in an online environment. When completed and fielded, REDEX will be a concrete example of the application of expert systems technology to the problem of improving performance and reducing the lifestyle costs of operating NASA's communications networks in the 1990's.

Generalized augmented transition network grammars for generation from semantic networks

The augmented transition network (ATN) is a formalism for writing parsing grammars that has been much used in Artificial Intelligence and Computational Linguistics. A few researchers have also used ATNs for writing grammars for generating sentences. Previously, however, either generation ATNs did not have the same semantics as parsing ATNs, or they required an auxiliary mechanism to determine the syntactic structure of the sentence to be generated. This paper reports a generalization of the ATN formalism that allows ATN grammars to be written to parse labelled directed graphs. Specifically, an ATN grammar can be written to parse a semantic network and generate a surface string as its analysis. An example is given of a combined parsing-generating grammar that parses surface sentences, builds and queries a semantic network knowledge representation, and generates surface sentences in response.