1589294
A Conceptual-Modeling Approach to Extracting Data from the Web
Electronically available data on the Web is exploding at an ever increasing pace. Much of this data is unstructured, which makes searching hard and traditional database querying impossible. Many Web documents, however, contain an abundance of recognizable constants that together describe the essence of a document’s content. For these kinds of data-rich documents (e.g., advertisements, movie reviews, weather reports, travel information, sports summaries, financial statements, obituaries, and many others) we can apply a conceptual-modeling approach to extract and structure data. The approach is based on an ontology – a conceptual model instance – that describes the data of interest, including relationships, lexical appearance, and context keywords. By parsing the ontology, we can automatically produce a database scheme and recognizers for constants and keywords, and then invoke routines to recognize and extract data from unstructured documents and structure it according to the generated database scheme. Experiments show that it is possible to achieve good recall and precision ratios for documents that are rich in recognizable constants and narrow in ontological breadth.
3217704
Conceptual-Model-Based Data Extraction from Multiple-Record Web Pages
Abstract Electronically available data on the Web is exploding at an ever increasing pace. Much of this data is unstructured, which makes searching hard and traditional database querying impossible. Many Web documents, however, contain an abundance of recognizable constants that together describe the essence of a document's content. For these kinds of data-rich, multiple-record documents (e.g., advertisements, movie reviews, weather reports, travel information, sports summaries, financial statements, obituaries, and many others) we can apply a conceptual-modeling approach to extract and structure data automatically. The approach is based on an ontology – a conceptual model instance – that describes the data of interest, including relationships, lexical appearance, and context keywords. By parsing the ontology, we can automatically produce a database scheme and recognizers for constants and keywords, and then invoke routines to recognize and extract data from unstructured documents and structure it according to the generated database scheme. Experiments show that it is possible to achieve good recall and precision ratios for documents that are rich in recognizable constants and narrow in ontological breadth. Our approach is less labor-intensive than other approaches that manually or semiautomatically generate wrappers, and it is generally insensitive to changes in Web-page format.
7346462
Ontology-based extraction and structuring of information from data-rich unstructured documents
We present a new approach to extracting information from unstructured documents based on an application ontology that describes a domain of interest. Starting with such an ontology, we formulate rules to extract constants and context keywords from unstructured documents. For each unstructured document of interest, we extract its constants and keywords and apply a recognizer to organize extracted constants as attribute values of tuples in a generated database schema. To make our approach general, we fix all the processes and change only the ontological description for a different application domain. In experiments we conducted on two different types of unstructured documents taken from the Web, our approach attained recall ratios in the 80% and 90% range and precision ratios near 98%.
17812918
Acquiring and delivering lessons learned for NASA scientists and engineers: a dynamic approach
Web forms are a common mechanism for collecting information online. They pose some limitations which negatively affect ease and flexibility of user interaction. These limitations are more pronounced when collecting information that can not be broken down into well defined and agreed upon structures. Lessons Learned or any kind of 'knowledge' is an example of such information. In this paper we introduce a concept of 'Dynamic Forms' which is aimed at enhancing user interaction while collecting unstructured information, 'Lessons Learned' in particular.
15635960
Improved variable and value ranking techniques for mining categorical traffic accident data
The ever increasing size of datasets used for data mining and machine learning applications has placed a renewed emphasis on algorithm performance and processing strategies. This paper addresses algorithms for ranking variables in a dataset, as well as for ranking values of a specific variable. We propose two new techniques, called Max Gain (MG) and Sum Max Gain Ratio (SMGR), which are well-correlated with existing techniques, yet are much more intuitive. MG and SMGR were developed for the public safety domain using categorical traffic accident data. Unlike the typical abstract statistical techniques for ranking variables and values, the proposed techniques can be motivated as useful intuitive metrics for non-statistician practitioners in a particular domain. Additionally, the proposed techniques are generally more efficient than the more traditional statistical approaches.
16499745
The Self-4.0 user interface: manifesting a system-wide vision of concreteness, uniformity, and flexibility
Manipulating programs is hard, while manipulating objects in the physical world is often easy. Several attributes of the physical world help make it comprehensible and manipulable: concreteness, uniformity, and flexibility. The Self programming system attempts to apply these attributes to the world within the computer. The semantics of the language, the efficiency and fidelity of its implementation, and the architecture of its user interface conspire to make the experience of constructing programs in Self immediate and tangible. We describe the mechanisms used to achieve this goal, and illustrate those mechanisms within the context of an extended programming task.
52856801
Subjectivity in object-oriented systems
2984394
Augmented Reality Chinese Checkers
This paper presents an application, Augmented Reality Chinese Checkers that we created to investigate user interface issues for table top projected augmented reality entertainment applications. A new tangible interaction device, the wireless button enhanced fiducial, is introduced to support selection tasks in mixed reality environments. The Augmented Reality Chinese Checkers game is built on a framework which can be used to create other computer supported collaborative games. The system is built using the Passive Detection Framework to track the 6 degrees of freedom position in real time of marked objects in the environment. The game supports up to six players at a time.