Semantic Link Network Research Articles

Extracting semantic link network of words from text for semantics-based applications

Extracting semantic link network of words from text for semantics-based applications

An Analysis on Needs for National Parks by Age Group using Semantic Network Analysis

Background and objective: National parks are places people from all walks of life can utilize. However, many existing studies are limited in suggesting management plans that fit the characteristics of different age groups of people visiting national parks. Therefore, this study aims to identify the requirements for national parks and set the direction for national park services by age group.Methods: This study collected the requirements for national parks from 902 people of different ages through an online self-administered questionnaire and analyzed them through semantic link network analysis.Results: As a result of the analysis, there were management requirements in two areas related to the environment (rubbish and ecological environment, facilities environment such as safety and forest fires) and one area related to the economy, and a monitoring system for rubbish and ecological environment and a private land management policy for the economy were identified as alternatives. The younger generation was interested in environmental issues, the older generation was interested in economic issues, and the elderly was interested in the facilities environment for safety and forest fires. Therefore, it was found that environmental campaigns should be conducted for the younger generation, admission fees and parking rates should be improved for the older generation, and a safe tour environment should be provided for the elderly.Conclusion: The needs analysis found that it is necessary to establish a monitoring system for all national parks from an environmental perspective and improve the management policy about private land from an economic perspective. Furthermore, environmental campaigns and events were required for the younger generation, benefits related to entrance fees for the elderly, and policies to create a safe facilities environment for the elderly. Establishing such national park policies will improve the satisfaction of users.

Automatic evaluation of summary on fidelity, conciseness and coherence for text summarization based on semantic link network

Automatic evaluation of summary is one of the basic problems of automatic text summarization research. The commonly used approaches evaluate the informativeness, conciseness and coherence of summary separately based on different representations with weak semantics such as n-gram and vector of words. By transforming text into semantic link network, this paper proposes a general framework for automatically evaluating summary from the following aspects: (1) fidelity, the extent of a summary that conveys the core of its source text; (2) conciseness, the extent of non-redundancy within summary and, (3) coherence, the extent of relatedness between representations within summary, which are based on a uniform semantic representation of text. Experiments on the evaluation of the summarization of scientific papers and news show that the proposed framework achieves comparable or higher correlations with human judgments than the popular evaluation models. This research also verifies the role of the semantic link network in representing and analysing texts.

Grouping sentences as better language unit for extractive text summarization

Most existing methods for extractive text summarization aim to extract important sentences with statistical or linguistic techniques and concatenate these sentences as a summary. However, the extracted sentences are usually incoherent. The problem becomes worse when the source text and the summary are long and based on logical reasoning. The motivation of this paper is to answer the following two related questions: What is the best language unit for constructing a summary that is coherent and understandable? How is the extractive summarization process based on the language unit? Extracting larger language units such as a group of sentences or a paragraph is a natural way to improve the readability of summary as it is rational to assume that the original sentences within a larger language unit are coherent. This paper proposes a framework for group-based text summarization that clusters semantically related sentences into groups based on Semantic Link Network (SLN) and then ranks the groups and concatenates the top-ranked ones into a summary. A two-layer SLN model is used to generate and rank groups with semantic links including the is-part-of link, sequential link, similar-to link, and cause–effect link. The experimental results show that summaries composed by group or paragraph tend to contain more key words or phrases than summaries composed by sentences and summaries composed by groups contain more key words or phrases than those composed by paragraphs especially when the average length of source texts is from 7000 words to 17,000 words which is the usual length of scientific papers. Further, we compare seven clustering algorithms for generating groups and propose five strategies for generating groups with the four types of semantic links.

The influence of semantic link network on the ability of question-answering system

Semantic Link Network plays an important role in representing and understanding text. This paper investigates the influence of semantic links on the basic abilities of a type of QA system that extracts answers from a range of texts (answer range). Research concerns how semantic links influence the answer range and the performance of this type of QA system. Research also concerns the ability to answering different types of questions and supporting different patterns of answering questions. Based on the semantic link network extracted from Wikipedia, an experimental QA system is developed to answer questions according to a range of pages in Wikipedia. Research reached the following results: (1) the answer range and the semantic link network influence each other: keeping a certain range of performance, increase one can decrease the request of the other; and, (2) the semantic link network can enhance the ability of QA system in answering questions and supporting patterns of answering questions covered by semantic link network.

Probabilistic inference on uncertain semantic link network and its application in event identification

The Probabilistic Semantic Link Network (P-SLN) is a model for enhancing the ability of Semantic Link Network in representing uncertainty. Probabilistic inference over uncertain semantic links can process the likelihood and consistency of uncertain semantic links. This work develops the P-SLN model by incorporating probabilistic inference rules and consistency constraints. Two probabilistic inference mechanisms are incorporated into the model. The application of probabilistic inference on SLN of events for joint event identification verifies the effectiveness of the proposed model.

Abstractive Multi-Document Summarization Based on Semantic Link Network

The key to realize advanced document summarization is semantic representation of documents. This paper investigates the role of Semantic Link Network in representing and understanding documents for multi-document summarization. It proposes a novel abstractive multi-document summarization framework by first transforming documents into a Semantic Link Network of concepts and events and then transforming the Semantic Link Network into the summary of the documents based on the selection of important concepts and events while keeping semantics coherence. Experiments on benchmark datasets show that the proposed summarization approach significantly outperforms relevant state-of-the-art baselines and the Semantic Link Network plays an important role in representing and understanding documents.

Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research

The paper presents an approach to knowledge management in learning and scientific research that allows increasing the availability of expert knowledge and reducing semantic noise during knowledge transfer. The availability of expert knowledge is ensured by transforming them from implicit into explicit form (in the form of Semantic Link Network). The reduction of semantic noise is achieved through the integration of knowledge in different forms and their personalization for different groups of knowledge recipients. In the paper, the tasks of decision-making support are formulated which should be performed by the knowledge management system on the base of the proposed approach.

Event-centric Machine Strategic Reading Approach for Scientific Literature

Massive scientific literature not only have provided plentiful information resources for science and technology information research, but also formulate heavy burdens for researchers and managers of science and technology. It is valuable and urgent to help users understand the knowledge and intelligence implied in scientific literature quickly by using machine reading method. In this paper, we propose an event-centric machine reading approach to construct the semantic link network of scientific research events. Scientific research event network has provide a novel perspective for scientific literature based information search, information analysis and intelligence studies.

The Joint Framework for Dynamic Topic Semantic Link Network Prediction

To explore the maximum potential of textual data, a well-organized dynamic semantic structure of the topics is in fact of great importance for effectively supporting the advanced intelligent application. The proposed framework joints the Gaussian mixture model and the Bayesian network to conduct inference and prediction of topic relationships of a dynamic topic semantic link network. The approach is to identify the relationships between the topics and to infer the condition-dependent topic relationships for predicting the topic semantic link network structure, which not only describes the relationships between the topics under changing-dependent conditions but also provides a broader understanding of the relationships between the topics in dynamic evolution processes. The results of the evaluation and experimental analysis indicate that the proposed approach is effective, feasible, and well-suited to predict the dynamic and multi-dimensional relationship structure of topics.

The contribution of cause-effect link to representing the core of scientific paper-The role of Semantic Link Network.

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing.

Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network

The semantic link network is a semantics modeling method for effective information services. This paper proposes a new text summarization approach that extracts semantic link network from scientific paper consisting of language units of different granularities as nodes and semantic links between the nodes, and then ranks the nodes to select Top-k sentences to compose summary. A set of assumptions for reinforcing representative nodes is set to reflect the core of paper. Then, semantic link networks with different types of node and links are constructed with different combinations of the assumptions. Finally, an iterative ranking algorithm is designed for calculating the weight vectors of the nodes in a converged iteration process. The iteration approximately approaches a stable weight vector of sentence nodes, which is ranked to select Top-k high-rank nodes for composing summary. We designed six types of ranking models on semantic link networks for evaluation. Both objective assessment and intuitive assessment show that ranking semantic link network of language units can significantly help identify the representative sentences. This paper not only provides a new approach to summarizing text based on the extraction of semantic links from text but also verifies the effectiveness of adopting the semantic link network in rendering the core of text. The proposed approach can be applied to implementing other summarization applications such as generating an extended abstract, the mind map, and the bulletin points for making the slides of a given paper. It can be easily extended by incorporating more semantic links to improve text summarization and other information services.

Semantically linking events for massive scientific literature research

PurposeThis paper aims to semantically linking scientific research events implied by scientific and technical literature to support information analysis and information service applications. Literature research is an important method to acquire scientific and technical information which is important for research, development and innovation of science and technology. It is difficult but urgently required to acquire accurate, timely, rapid, short and comprehensive information from the large-scale and fast-growing literature, especially in the big data era. Existing literature-based information retrieval systems focus on basic data organization, and they are far from meeting the needs of information analytics. It becomes urgent to organize and analyze scientific research events related to scientific and technical literature for forecasting development trend of science and technology.Design/methodology/approachScientific literature such as a paper or a patent is represented as a scientific research event, which contains elements including when, where, who, what, how and why. Metadata of literature is used to formulate scientific research events that are implied in introduction and related work sections of literature. Named entities and research objects such as methods, materials and algorithms can be extracted from texts of literature by using text analysis. The authors semantically link scientific research events, entities and objects, and then, they construct the event space for supporting scientific and technical information analysis.FindingsThis paper represents scientific literature as events, which are coarse-grained units comparing with entities and relations in current information organizations. Events and semantic relations among them together formulate a semantic link network, which could support event-centric information browsing, search and recommendation.Research limitations/implicationsThe proposed model is a theoretical model, and it needs to verify the efficiency in further experimental application research. The evaluation and applications of semantic link network of scientific research events are further research issues.Originality/valueThis paper regards scientific literature as scientific research events and proposes an approach to semantically link events into a network with multiple-typed entities and relations. According to the needs of scientific and technical information analysis, scientific research events are organized into event cubes which are distributed in a three-dimensioned space for easy-to-understand and information visualization.

Logic and Event Based Semantic Relationship Evolution in Service Semantic Link Network

To facilitate reasoning and predicting service evolution relationships with incomplete, partial or uncertain knowledge for supporting an intelligent service application and to preserve the relationship consistency in the whole service networks during Web service evolution processes over time. An approach was presented for event-based relationships evolution detection and reasoning among web services by combining first-order logic and probabilistic graphical models in a single representation. The Web service relationship evolution model was constructed by using available probability information and knowledge based on the related event and their inherent service relationship dependencies. And S-MLN was taken as a logical framework for Web service relationships evolution reasoning with uncertainty to discover and predict evolutionary service relationship classification. The events and evolutionary measures of the research were found to be helpful for evaluating Web service evolution relationships. And it is effective to evaluate the quality of service set classification by SMLN based evolution relationships prediction. The study identifies the theoretical foundations of web service evolution discovery in the context of SLN. This study, based on an established theoretical foundation, will help the research community to gain a deeper understanding of the dynamic relationship in the semantic context of the web services network.

대학 학사행정 기록물의 토픽맵 기반 검색시스템 설계에 관한 연구

A topic map was designed as an efficient information retrieval method that is optimized for classification, organization, and navigation through the use of a semantic link network above information resources. With this, this study aims to design a topic map-based university archives retrieval system to provide the relevant information retrieval. For this study, electronic records that relate to the academic administration within two years of D university were collected, and topic map editing was carried out with Ontopia Omnigator. Topics were classified according to their functional analysis of academic administration. In the end, the number of topics was finalized as 626, with 6 types in general: academic work, staff, college register, student, university, etc. Association was separated into six types as well, which were formed with consideration to the relationships among topics. In addition, there are seven occurrence types: register class, register number, register date, receiver, title, creator, and identifier. It is expected that the associative nature of the designed topic map-based retrieval system in this study will make navigation of large records easy and allow incidental discovery of knowledge.

Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Measuring graph similarity is a primary issue for graph-related applications. Many works have been proposed on simple topology-based structural similarity measuring for networks. It is not enough for semantic-rich networks like semantic networks, semantic link networks, and event-linked networks where semantic-based structural similarity measuring is more important than topology-based structure similarity measuring. In this paper, the authors introduce a semantic-based structural similarity for the first time and then propose an approach to measure the semantic-based structural similarity between networks with the computing theory for semantic relations as the foundation. A case study in semantic link network of the scientific research is also presented to show the feasibility of the proposed approach.

Semantics, knowledge and grids

Summary Semantics, knowledge and grids represent three streams of studying humans and cyberspace that humans create and interact. Relevant research concerns all areas of computing, especially web semantics, artificial intelligence, and advanced computing infrastructures. This special issue selected eight papers reporting relevant research and applications from the 9th International Conference on Semantics, Knowledge and Grids. Copyright © 2015 John Wiley & Sons, Ltd.

Designing a novel linear‐time graph kernel for semantic link network

SummaryGraph is an efficient tool for representing structured data such as proteins, molecular compounds, and social networks. Graph kernel is a technique to measure the similarity between graphs. However, existing graph kernels still have several limitations: (1) semantics on link is ignored; (2) node is associated with single label; (3) most graph kernels require more than cubic time, which is still computationally expensive; and, (4) there is seldom consideration of handling the graph comparison when the number of node types becomes huge. In this paper, we utilize semantic link network (SLN) to represent complex structured data with richer semantic information. Topic model is employed for dimension reduction and tagging each node with multiple labels. And a novel linear‐time graph kernel for SLN is designed to calculate the similarity between two SLNs. This work remedies the limitations of the conventional graph kernels. The effectiveness and efficiency of this approach are evaluated by the document classification task on public corpora. Empirical results demonstrate that the proposed method can achieve better performance than the traditional topic model‐based classification approach. Copyright © 2015 John Wiley & Sons, Ltd.

Towards semantically linked multilingual corpus

Multilingual information processing gains more and more attention in recent years with the development of information globalization. Multilingual corpus is a key challenge for multilingual information extraction, analysis, management and service in a wide range of systems. This work addresses on the study and analysis of semantic associations among elements in a multilingual corpus. A solution is proposed in this paper to optimize the semantic organization of multilingual corpus by linking the corpus elements into a semantic link network. This enhances the text-basd applications of multilingual corpus such as corpus linguistics study, dictionary search, machine translation and cross-lingual information retrieval.

Semantic Link Network-Based Model for Organizing Multimedia Big Data

Recent research shows that multimedia resources in the wild are growing at a staggering rate. The rapid increase number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. In this paper, the semantic link network model is used for organizing multimedia resources. A whole model for generating the association relation between multimedia resources using semantic link network model is proposed. The definitions, modules, and mechanisms of the semantic link network are used in the proposed method. The integration between the semantic link network and multimedia resources provides a new prospect for organizing them with their semantics. The tags and the surrounding texts of multimedia resources are used to measure their semantic association. The hierarchical semantic of multimedia resources is defined by their annotated tags and surrounding texts. The semantics of tags and surrounding texts are different in the proposed framework. The modules of semantic link network model are implemented to measure association relations. A real data set including 100 thousand images with social tags from Flickr is used in our experiments. Two evaluation methods, including clustering and retrieval, are performed, which shows the proposed method can measure the semantic relatedness between Flickr images accurately and robustly.