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In recent years, the concept of human-in-the-loop has been utilized to support environment sensing. Along with IoT (Internet of Things) and wearable computing technologies, connecting people and devices to the internet provides a significant advantage for real-time emergency management. For development of context-aware applications, it is important to utilize higher-level semantic information, such as human activity, social emotions, and human behaviors for event monitoring. Therefore, human users may become part of sensor networks by using mobile devices and social media to report local information around them. In this work, we mainly focus on the use of social messages spreading by human users to model the real-world events, in order to incorporate human sensors into event contexts for situational awareness. First, our algorithm computes the energy of each collected event messages, and then encapsulates ranked temporal, spatial and topical keywords into a structured node, which could reinforce the alert collected from physical nodes. The experimental results show that the proposed approach is able to extract essential entities of events for incorporating human sensors into event contexts for event prevention and risk management.

In this work we utilize the social messages to construct an extensible event ontology model for learning the experiences and knowledge to cope with emerging real-world events. We develop a platform combining several text mining and social analysis algorithms to cooperate with our stream mining approach to detecting large-scale disastrous events from social messages, in order to achieve the aim of automatically constructing event ontology for emergency response First, we employ the developed event detection technique on Twitter social-messages to monitor the occurrence of emerging events, and record the development and evolution of detected events. Furthermore, we store the messages associated with the detected events in a repository. Through the developed algorithms for analyzing the content of social messages and ontology construction the event ontology can be established, allowing for developing relevant applications for prediction of possible evolution and impact evaluation of the events in the future immediately, in order to achieve the goals for early warning of disasters and risk management.

Due to the explosive growth of social-media applications, enabling event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past microblogs for tackling emerging real-world events, in this work we utilize the social-media messages to characterize events through their contents and spatio-temporal features for relatedness analysis. Several essential features of each detected event dataset have been extracted for event formulation by performing content analysis, spatial analysis, and temporal analysis. This allows our approach compare the new event vector with existing event vectors stored in the event-data repository for evaluation of event relatednesss, by means of validating spatio-temporal feature factors involved in the event evolution. Through the developed algorithms for computing event relatedness, in our system the ranking of related events can be computed, allowing for predicting possible evolution and impacts of the event. The developed system platform is able to immediately evaluate the significantly emergent events, in order to achieve real-time knowledge discovery of disastrous events.

Online microblogging services such as Twitter allow users to post very short messages related to everything ranging from mundane daily life routines to breaking news events. This phenomenon has changed the way for information acquisition. In this paper, we present an instinctive method with a time-decay function which corresponds to the natural propagation of social networks for clustering real-time text streams collected from Twitter. Compared to most previous studies, we follow natural cascading behaviors of event lifecycle to develop a self-adaptive clustering model for online event detection. Also, we construct an expandable similarity matrix which is capable of evaluating microblogging posts with incomplete semantic features. Experimental results show that the proposed method is a sensible solution to monitoring momentous real-time events and utilizing the text streams to facilitate the management of social networking data.

One of the basic human needs is to exchange information and socialize with each other. Online microblogging services such as Twitter allow users to post very short messages related to everything ranging from mundane daily life routines to breaking news events. A key challenging issue of mining such social messages is how to analyze the real-time distributed messages and extract significant features of them in a dynamic environment. In this work, we propose a novel term weighting method, called BursT, using sliding window techniques for weighting message streams. The experimental results show that our weighting technique has an outstanding performance to reflect the shifts of concept drift. The result of this work can be extended to perform a periodic feature extraction, and also be able to integrate other sophisticated clustering methods to enhance the efficiency for real-time event mining in social networks.

With the increasing needs of location information, applications of geospatial information have gained a lot of attention in both research and commercial organizations Extraction of geospatial knowledge from the information content has been thus becoming a important process Among theses applications, a typical example is to discover relationships between various geospatial texts/data and specific locations In this paper, we describe a location based text mining approach using Artificial Neural Networks (ANN) to classify texts into various categories based on their geospatial features, with the aims to discovering relationships between documents and zones First, the collected documents were mapped to corresponding zones by the Adaptive Affinity Propagation (Adaptive AP) clustering techniques, then we performed framed maximize zones by means of Fuzzy ARTMAP (FAM) and Support Vector Machines (SVM) methods, allowing the results of relationships between documents and zones to be well presented Eventually, we compared our experimental results with that of baseline models using Self-organizing maps (SOM) and Learning Vector Quantization (LVQ) methods The preliminary results show that our platform framework has potential for geospatial knowledge discovery.

Traditional content-based image retrieval (CBIR) systems often fail to fulfill a user’s need due to the ‘semantic gap’ existed between the extracted features of the systems and the user’s query. In this paper we propose a novel approach to bridge the semantic gap which is the major deficiency of CBIR systems. We conquer the deficiency by extracting semantics of an image from the environmental texts around it. We apply a text mining process, which adopts the self-organizing map (SOM) learning algorithm as a kernel, on the environmental texts of an image to extract the semantic information from this image. Some implicit semantic information of the images can be discovered after the text mining process. We also define a semantic relevance measure to achieve the semantic-based image retrieval task. We performed experiments on a set of images which are collected from web pages and obtained promising results.

Topic maps standard (ISO-13250) has been gradually recognized as an emerging standard for information exploration and knowledge organization in the web era. One advantage of topic maps is that they enable a user to navigate and access the documents he wants in an organized manner, rather than browsing through hyperlinks that are generally unstructured and often misleading. Nowadays, the topic maps are generally manually constructed by domain experts or users since the functionality and feasibility of automatically generated topic maps still remain unclear. In this work we propose a semi-automatic scheme to construct topic maps. We first apply a text mining process on a corpus of information resources to identify the topics and discover the relations among these topics. Necessary components of topic maps such as topics, topic types, topic occurrences, and topic associations may be automatically revealed by our method.

In this paper we propose a novel method to discover the semantics of an image within a web page and bridge the semantic gap between the image features and a user’s query. Since an image is always accompanied with some text segments which are closely related to the image, we propose that the semantics of the image may be discovered from such text segments through a data mining process on these texts. Based on such recognition, we applied a text mining process on the accompany texts of an image to discover the themes of this image, which constitute the semantics of this image. The self-organizing map algorithm is first applied to cluster a set of preprocessed web pages. Based on the clustering result, we design a theme identification process to identify a set of words which constitute the semantics of an image. We performed experiments on a small set of images and obtained promising results.

In this work we developed a new automatic hypertext construction method based on a proposed text mining approach. Our method applies the self-organizing map algorithm to cluster some flat text documents in a training corpus and generate two maps. We then use these maps to identify the sources and destinations of some important hyperlinks within these training documents. The constructed hyperlinks are then inserted into the training documents to translate them into hypertext form. Such translated documents form the new corpus. Incoming documents can also be translated into hypertext form and added to the corpus through the same approach. Our method had been tested on a set of flat text documents collecting from several newswire sites. Although we only used Chinese text documents, our approach can be applied to any document that can be transformed to a set of indexed terms.

Recently knowledge discovery and data mining in unstructured or semi-structured texts(text mining) has been attracted lots of attention from both commercial and research fields. One aspect of text mining is on automatic text categorization, which assigns a text document to some predefined category according to the correlation between the document and the category. Traditionally the categories are arranged in hierarchical manner to achieve effective searching and indexing as well as easy comprehension for human. The determination of categories and their hierarchical structures were most done by human experts. In this work, we developed an approach to automatically generate categories and reveal the hierarchical structure among them. We also used the generated structure to categorize text documents. The document collection is trained by a self-organizing map to form two feature maps. We then analyzed the two maps to obtain the categories and the structure among them. Although the corpus contains documents written in Chinese, the proposed approach can be applied to documents written in any language and such documents can be transformed into a list of separated terms.

The maintenance cost is a critical issue for the success of integrating database and information retrieval systems (IRS). For a robust integration of search engines, the signature file filter can effectively eliminate the maintenance cost and offer a more natural fit between the database and text retrieval systems. Extending the usability of merged database and signature based text-retrieval systems by building on an object-oriented database management system (OODBMS) provides better and complementary advantages to both databases and information retrieval systems (IRSs). In this paper, we present a new approach for integrating OODBMSs and IRSs that maintains the flexibility and avoids overheads of mapping process, by means of encapsulating the documents and signature based IR methods into storable objects which are being stored in the database. In addition, we develop a novel signature file approach based on a statistical corpus extraction technique, which can effectively reduce false drop probability for text retrieval from the underneath document database.

Extending the usability of merged database and text-retrieval systems by building on an object-oriented database management system (OODBMS) can offer better and complementary advantages to both database and information retrieval systems (IRS). In this paper, we present a new approach for integrating OODBMSs and IRSs that maintains the flexibility and avoids overheads of mapping process, by means of wrapping the documents and encapsulating signature based IR methods into storable objects which are being stored in the database. In particular, we demonstrate how to apply such an integration approach to a special problem domain: a multi-lingual information retrieval system. In our framework, a sound platform and query facilities offered by the underlying object-oriented database can further enhance the original IR functions and be applicable to the construction of multimedia-document based digital libraries.