Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.
In general, these research works highlight that to achieve microblog summarization, due to the dynamic nature of its content, it is crucial to consider both the chronological order of the posts and their information content. Unlike these microblog summarization approaches that consider the time and meaning of the tweets at two different stages, our solution considers both timestamps and meaning of the tweets at the same time. This work presents the Time Aware Knowledge Extraction (briefly TAKE) methodology, as a new approach to perform conceptual and temporal data analysis of tweets' content for microblog summarization. TAKE extends Fuzzy Formal Concept Analysis @cite_11 introducing time dependencies among objects, in order to provide a summary that follow the evolution of the story over the timeline. Furthermore, the proposed framework reveals good performances in terms of F-Measure, with optimal Recall and comparable values of Precision with respect to the compared approaches. Specifically, the timed fuzzy lattice extracted by TAKE enable us to support user requests providing less or more succinct summary according to the specific needs.
@cite_11
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In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.