Determining the Statistical Significance of Rules for Rule-based Knowledge-extraction Algorithms

Domain specific knowledge bases are often built from domain-specific texts using rule-based knowledge-retrieval algorithms. These algorithms are based on semantic extraction rules that process text using a parser, looking at the resulting parse trees & dependency graphs and then applying those rules to identify possible constructs for triple extraction. The performance of such algorithms critically depends on how capable these rules are in extracting the knowledge (in the form of triples) as a fraction of the total knowledge present in the text fragment. In this paper, we propose a way to statistically analyze the significance of these rules based on the fraction of knowledge that they extract out of given text corpora

Easy Authoring of Intelligent Tutoring Systems for Synthetic Environments

ABSTRACT: We describe how the Extensible Problem Specifi

POPcorn: An Online Resource Providing Access to Distributed and Diverse Maize Project Data

The purpose of the online resource presented here, POPcorn (Project Portal for corn), is to enhance accessibility of maize genetic and genomic resources for plant biologists. Currently, many online locations are difficult to find, some are best searched independently, and individual project websites often degrade over time—sometimes disappearing entirely. The POPcorn site makes available (1) a centralized, web-accessible resource to search and browse descriptions of ongoing maize genomics projects, (2) a single, stand-alone tool that uses web Services and minimal data warehousing to search for sequence matches in online resources of diverse offsite projects, and (3) a set of tools that enables researchers to migrate their data to the long-term model organism database for maize genetic and genomic information: MaizeGDB. Examples demonstrating POPcorn's utility are provided herein

Extensible Problem Specific Tutor (xPST) : Easy authoring of intelligent tutoring systems

An Intelligent Tutoring System (ITS) is an artificially intelligent educational software application that teaches a user skills by giving personalized feedback as the user completes tasks within a problem domain. Despite their popularity, authoring these systems is a labor-intensive process, requiring many different skill sets. A major component of an ITS is the cognitive model. Historically its implementation has required not only cognitive science knowledge, but also programming knowledge as well. To address this challenge, the Extensible Problem Specific Tutor (xPST) was developed for easy authoring of ITSs for existing software and websites. This work develops an xPST authoring tool to simplify the process of xPST authoring by the end user and to help conduct research experiments. It also evaluates the xPST system in terms of the time taken by the users to author successful models. This work also extends xPST framework to enable the creation of generalized tutors in addition to problem specific tutors. To help non-technical military trainers create xPST tutors in game scenarios, this work develops a Torque xPST Driver plugin to enable xPST authoring in Torque 3D game and evaluates authoring in spatial environment scenarios like 3D games using the authoring tool. Finally, this work compares xPST and Cognitive Tutor SDK (another authoring framework) using a fraction addition study and shows that the ratio of training development time to training experience time using xPST is approximately 50% less that that of using Cognitive Tutor SDK. This thesis also shows that there is no significant difference between the “beginner programmer” and “experienced programmer” groups in terms of the time taken to author the tasks using xPST.</p

Determining the Statistical Significance of Rules for Rule-based Knowledge-extraction Algorithms

Domain specific knowledge bases are often built from domain-specific texts using rule-based knowledge-retrieval algorithms. These algorithms are based on semantic extraction rules that process text using a parser, looking at the resulting parse trees & dependency graphs and then applying those rules to identify possible constructs for triple extraction. The performance of such algorithms critically depends on how capable these rules are in extracting the knowledge (in the form of triples) as a fraction of the total knowledge present in the text fragment. In this paper, we propose a way to statistically analyze the significance of these rules based on the fraction of knowledge that they extract out of given text corpora.</p

Expansion of the xPST Framework to Enable Non-programmers to Create Intelligent Tutoring Systems in 3D Game Environments

Our previous work has demonstrated that the Extensible Problem Specific Tutor (xPST) framework lowers the bar for non-programmers to author model tracing intelligent tutoring systems (ITSs) on top of existing software and websites. In this work we extend xPST to enable authoring of tutors in 3D games. This process differs substantially from authoring tutors for traditional GUI software in terms of the inherent domain complexity involved, different types of feedback required and interactions generated by various entities apart from the student. A tutor for a village evacuation task has been constructed in order to demonstrate the capabilities of using the extended xPST system to create a game-based tutor.This is a post-peer-review, pre-copyedit version of a proceeding published as Kodavali, S.K., Gilbert, S., Blessing, S.B. (2010). Expansion of the xPST Framework to Enable Non-programmers to Create Intelligent Tutoring Systems in 3D Game Environments. In: Aleven, V., Kay, J., Mostow, J. (eds) Intelligent Tutoring Systems. ITS 2010. Lecture Notes in Computer Science, vol 6095. Springer, Berlin, Heidelberg. The final authenticated version is available online at DOI: 10.1007/978-3-642-13437-1_72. Copyright 2010 Springer-Verlag Berlin Heidelberg. Posted with permission