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This paper fuses the techniques such as semantic network, the individuality service and agent, and references various research achievements of semantics Web on knowledge expression, RDF data manipulation and semantic retrieval, to propose an information retrieval model by combination of semantic with keyword based on
Textual web pages dominate web search engines nowadays. However, there is also a striking increase of structured data on the web. Efficient keyword query processing on structured data has attracted enough attention, but effective query understanding has yet to be investigated. In this paper, we focus on the problem of
A user profile on an online social network is characterized by its profile entries (keywords). In this paper, we study the relationship between semantic similarity of user keywords and the social network topology. First, we present a 'forest' model to categorize keywords and define the notion of distance between
conceptual model is well defined, a set of rules for keyword searching is created to verify preciseness of output produced. The rules created in this paper will be executed on Herbal Research E-Centre prototype.
Text chance discovery is the process of extracting author's potential hidden issue from a large number of texts. For the main question keyword (i.e. Chance) extracting, we propose a framework of text chance discovery system based on immune and multi-agent in this paper. By immunization and agent self-learning, this
The continued exponential growth in volume of literature data is giving birth to a new challenge to the bibliographic analysis service and the traditional features such as keyword search, author search and statistics services could not satisfy researchers for in-depth analysis. The emerging of community analysis in
The Web has the potential to become the world's largest knowledge base. In order to unleash this potential, the wealth of information available on the Web needs to be extracted and organized. There is a need for new querying techniques that are simple and yet more expressive than those provided by standard keyword
approach, personal data are uniformly represented in a single data model proposed in this paper, and stored in a data warehousing system based on a storage model corresponding to the data model. Then, users are enabled to easily retrieve all their personal information by using keywords or a semi-structured query language.
utilized, simple keyword analysis has not been sufficient to identify radical sites on Web 1.0 – pro-extremist, anti-extremist, and news sites, for example, may use the same keywords to discuss the same event but have a very different motivation. In an effort to explore this problem, we completed an exercise involving
this paper we are provide the new architecture that help to find the blood donor information using Voice with some keyword and find the donor information when they required the blood. The objective of this paper to deal with the Vague Voice based donor information filter and make a particular blood group cluster with
relationships that may assist strategic decision making. We use a 64MB open meta big dataset developed by summarizing terrorist activity as keyword frequencies collected from trillions of public news articles published during a 43 year period from 1970-2013 and readily available statistical software, SPSS, to visually summarize
Latent Dirichlet Allocation, which is a non-supervised learning method, can be used for topic detection, automatic text categorization, keyword extraction and so on. It only focuses on the text itself, not considering other external correlation properties. External association property refers to some structured
special data record and new record model on fuzzy set are given. By calculating the membership of keyword, new fuzzy closeness functions are proposed to classify the information. Finally, examples prove that this algorithm can effectively and automatically classify input information of database, the accuracy and intelligence
In the processing of source retrieval in plagiarism detection, rationale for keywords extraction is to select only those phrases or words which maximize the chance of retrieving source documents matching the suspicious document. TF-IDF (term frequency-inverse document frequency), weighted TF-IDF (the weighted term
document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a
a series of analysis, including the distribution of core authors, journal, countries and institutions, co-reference and keywords, and obtained corresponding relults by cluster techniques. The findings are as follows. First, the number of papers shows a stable slope rate in recent years. Second, the USA, England and
Excel as the unit of encryption and decryption and analyzes the file structure. Then, the model automatically identifies the types of data, verifies the legitimacy of data and filters the keywords which will not be encrypted and decrypted. At last, we invoke special algorithms to encrypt or decrypt the data. The
the obtained domain dictionary, is used to segment the short text questions. From the segmentation results, the keywords are extracted to obtain query target and query requirement of the question and to generate a SQL statement for data query. The method proposed in this paper can be applied to question-answering system
model in the data set of flickr. The final ranked pictures are the combination of keywords and users' preference matching. The experiment proves that our method is better than both non-personalization method and common personalization method.
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