Conventional tools for automatic metadata creation mostly extract named entities or patterns from texts and annotate them with information about persons, locations, dates, and so on. However, this kind of entity type information is often too primitive for more advanced intelligent applications such as concept-based search. Here, we try to generate semantically-deep metadata with limited human intervention. The main idea behind our approach is to use Web mining and categorization techniques to create thematic metadata. The proposed approach, comprises of three computational modules: feature extraction, HCQF (hier-concept query formulation) and text instance categorization. The feature extraction module sends the name of text instances to Web search engines, and the returned highly-ranked search-result pages are used to describe them.

Many Web information services utilize techniques of information extraction(IE) to collect important facts from the Web. To create more advanced services, one possible method is to discover thematic information from the collected facts through text classification. However, most conventional text classification techniques rely on manual-labelled corpora and are thus ill-suited to cooperate with Web information services with open domains. In this work, we present a system named LiveClassifier that can automatically train classifiersthrough Web corpora based on user-defined topic hierarchies. Due to its flexibility and convenience, LiveClassifier can be easily adapted for various purposes. New Web information services can be created to fully exploit it; human users can use it to create classifiers for their personal applications. The effectiveness of classifiers created by LiveClassifier is well supportedby empirical evidence.

Conventional tools for automatic metadata creation mostly extract named entities or text segments from texts and annotate them with information about persons, locations, dates, and so on. However, this kind of entity type information is often insufficient for machines to understand the facts contained in the texts, thus precluding the possibility of implementing more advanced, intelligent applications, such as concept-based search. In this work, we try to create more refined thematic metadata inherent in texts. Based on Web resource mining, our approach acquires training corpora necessary to describe both the thematic categories and the metadata extracted from the texts. The approach then finds the corresponding relationships among them by means of categorization and thus generates thematic metadata for the textual data. Experimental results confirm the potential and wide adaptability of our approach.