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Current keyword search by Google, Yahoo, and so on gives enormous unsuitable results. A solution to this perhaps is to annotate semantics to textual web data to enable semantic search, rather than keyword search. However, pure manual annotation is very time-consuming. Further, searching high level concept such as
keyword search, meaning that the user needs to know the correct keywords before being able to retrieve the content of al-Quran. In this paper, we propose a system that supports the end user in querying and exploring the Quran ontology. The system comprises user query reformulation against the Quran ontology stored and
in a keyword-based photo retrieval process.We use metadata about the photo shot context (address location, nearby objects, season, light status...) to generate a bag of words for indexing each photo. We extend the Vector Space Model in order to transform these shot context words into document-vector terms. In addition
Web services like conceptual search, i.e., search based on meaning rather than just character strings, has been the motivation of a large body of research in the IR field.
, we translate relation extraction to a problem of keyword extraction, and analyze the characteristics of word (part-of-speech, position and distance to entity) by means of bootstrapping. Secondly, calculate the weight of each word in one sentence with a pair of geo-entities based on the statistic results of
paper proposed a new architecture (a keyword based approach) to recognize emotions from text. In case of recognizing emotion from a piece of text document or a blog, any human can do this better than a machine only problem is he/she takes time. Proposed emotion detector system takes a text document and the emotion word
Concept-based information retrieval offers a higher abstraction level than the classical keyword-based approach. A multilingual terminology makes possible language-independent indexing and querying. The conceptual structure with its associated terminology is called an ontology. This paper describes a methodology to
a smaller, more informative domain ontology. In particular, we show that fully automated techniques based on keywords or topics have quite poor performance, while a semi-automated approach, requiring limited user involvement, can highly improve the filtering of domain concepts.
ontologies.The web interface allows video browse, query by annotated concepts or by keywords, compressed video preview, media download and upload. The repository contains metadata annotations, which can be either manually created as ground truth or automatically generated by video surveillance systems. Their automatic annotations
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.