The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Along with the rapid popularity of the Internet, crime information on the web is becoming increasingly rampant, and the majority of them are in the form of text. Because a lot of crime information in documents is described through events, event-based semantic technology can be used to study the patterns and trends of web-oriented crimes. In our research project on cyber crime mining, we construct...
In this paper, a drug knowledge platform based on intelligent information technology is constructed. Unlike the traditional virtual drug compound library which focus on the patents of drug only, the aim of the new platform is to design a system including information retrieval, information extraction, construction of drug compound and drug ontology, structure based virtual screening, and text classification...
Digital libraries have shouldered the mission of preserving and spreading human culture in the era of information. However, knowledge extraction for digital libraries is not well studied, and that holds back the role promotion of digital libraries from information collector to knowledge provider. This paper presents an ontology-based approach, which extracts detailed attributes of Traditional Chinese...
There are a lot of text documents on the Web which contain opinions or sentiments about an object such as software reviews, product reviews, movies reviews, music reviews, and book reviews etc. Opinion mining or sentiment classification aim to extract the features on which the reviewers express their opinions and determine they are positive or negative. In this paper we proposed an ontology based...
In this paper, we describe a location based text mining approach to classify texts into various categories based on their geospatial features, with the aims to discovering relationships between documents and zones. We first mapped documents into corresponding zones by adaptive affinity propagation (adaptive AP) clustering technique, and then framed maximize zones by means of simplified fuzzy ARTMAP...
Ontology learning aims to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing ones. In this paper, we present a complete framework...
Current classification methods are based on the ldquobag of wordsrdquo (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. In this paper, we proposed a system that uses ontologies and natural language processing techniques to index texts. Traditional BOW matrix is replaced by ldquoBag of Conceptsrdquo (BOC)...
Text classification has been widely used to assist users with the discovery of useful information from the Internet. However, current text classification systems are based on the ldquoBag of Wordsrdquo (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. To overcome this problem, previous work attempted to enrich...
Ontology learning has become a major area of research whose goal is to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing...
As much valuable domain knowledge is hidden in enterprises' text repositories (e.g., email archives, digital libraries, etc.), it is desirable to develop effective knowledge management tools to process this unstructured data so as to extract domain knowledge for business decision making. Ontology-based semantic annotation of documents is one of the promising ways for knowledge discovery from text...
Extractive text summarization aims to create a condensed version of one or more source documents by selecting the most informative sentences. Research in text summarization has therefore often focused on measures of the usefulness of sentences for a summary. We present an approach to sentence extraction that maps sentences to nodes of a hierarchical ontology. By considering ontology attributes we...
This paper presents a method of ontology-based sentiment classification to classify and analyse online product reviews of consumers. We implement and experiment with a support vector machines text classification approach based on a lexical variable ontology. After testing, it could be demonstrated that the proposed method can provide more effectiveness for sentiment classification based on text content.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.