Knowledge acquisition from text in a complex domain

Complex real world domains can be characterized by a large amount of data, their interactions and that the knowledge must often be related to concrete problems. Therefore, the available descriptions of real world domains do not easily lend themselves to an adequate representation. The knowledge which is relevant for solving a given problem must be extracted from such descriptions with the help of the knowledge acquisition process. Such a process must adequately relate the acquired knowledge to the given problem. An integrated knowledge acquisition framework is developed to relate the acquired knowledge to real world problems. The interactive knowledge acquisition tool COKAM+ is one of three acquisition tools within this integrated framework. It extracts the knowledge from text, provides a documentation of the knowledge and structures it with respect to problems. All these preparations can serve to represent the obtained knowledge adequately

Knowledge Authoring and Question Answering via Controlled Natural Language

Knowledge acquisition from text is the process of automatically acquiring, organizing and structuring knowledge from text which can be used to perform question answering or complex reasoning. However, current state-of-the-art systems are limited by the fact that they are not able to construct the knowledge base with high quality as knowledge representation and reasoning (KRR) has a keen requirement for the accuracy of data. Controlled Natural Languages (CNLs) emerged as a technology to author knowledge using a restricted subset of English. However, they still fail to do so as sentences that express the same information may be represented by different forms. Current CNL systems have limited power to standardize sentences that express the same meaning into the same logical form. We solved this problem by building the Knowledge Authoring Logic Machine (KALM), which is a technology for domain experts who are not familiar with logic to author knowledge using CNL. The system performs semantic analysis of English sentences and achieves superior accuracy of standardizing sentences that express the same meaning to the same logical representation. Besides, we developed the query part of KALM to perform question answering, which also achieves very high accuracy in query understanding

Automatic Document Summarization Using Knowledge Based System

This dissertation describes a knowledge-based system to create abstractive summaries of documents by generalizing new concepts, detecting main topics and creating new sentences. The proposed system is built on the Cyc development platform that consists of the world’s largest knowledge base and one of the most powerful inference engines. The system is unsupervised and domain independent. Its domain knowledge is provided by the comprehensive ontology of common sense knowledge contained in the Cyc knowledge base. The system described in this dissertation generates coherent and topically related new sentences as a summary for a given document. It uses syntactic structure and semantic features of the given documents to fuse information. It makes use of the knowledge base as a source of domain knowledge. Furthermore, it uses the reasoning engine to generalize novel information. The proposed system consists of three main parts: knowledge acquisition, knowledge discovery, and knowledge representation. Knowledge acquisition derives syntactic structure of each sentence in the document and maps words and their syntactic relationships into Cyc knowledge base. Knowledge discovery abstracts novel concepts, not explicitly mentioned in the document by exploring the ontology of mapped concepts and derives main topics described in the document by clustering the concepts. Knowledge representation creates new English sentences to summarize main concepts and their relationships. The syntactic structure of the newly created sentences is extended beyond simple subject-predicate-object triplets by incorporating adjective and adverb modifiers. This structure allows the system to create sentences that are more complex. The proposed system was implemented and tested. Test results show that the system is capable of creating new sentences that include abstracted concepts not mentioned in the original document and is capable of combining information from different parts of the document text to compose a summary

Assessing schematic knowledge of introductory probability theory

[Abstract]: The ability to identify schematic knowledge is an important goal for both assessment and instruction. In the current paper, schematic knowledge of statistical probability theory is explored from the declarative-procedural framework using multiple methods of assessment. A sample of 90 undergraduate introductory statistics students was required to classify 10 pairs of probability problems as similar or different; to identify whether 15 problems contained sufficient, irrelevant, or missing information (text-edit); and to solve 10 additional problems. The complexity of the schema on which the problems were based was also manipulated. Detailed analyses compared text-editing and solution accuracy as a function of text-editing category and schema complexity. Results showed that text-editing tends to be easier than solution and differentially sensitive to schema complexity. While text-editing and classification were correlated with solution, only text-editing problems with missing information uniquely predicted success. In light of previous research these results suggest that text-editing is suitable for supplementing the assessment of schematic knowledge in development

A conceptual architecture for interactive educational multimedia

Learning is more than knowledge acquisition; it often involves the active participation of the learner in a variety of knowledge- and skills-based learning and training activities. Interactive multimedia technology can support the variety of interaction channels and languages required to facilitate interactive learning and teaching. A conceptual architecture for interactive educational multimedia can support the development of such multimedia systems. Such an architecture needs to embed multimedia technology into a coherent educational context. A framework based on an integrated interaction model is needed to capture learning and training activities in an online setting from an educational perspective, to describe them in the human-computer context, and to integrate them with mechanisms and principles of multimedia interaction

Acquiring Correct Knowledge for Natural Language Generation

Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems, like most AI systems, need substantial amounts of knowledge. However, our experience in two NLG projects suggests that it is difficult to acquire correct knowledge for NLG systems; indeed, every knowledge acquisition (KA) technique we tried had significant problems. In general terms, these problems were due to the complexity, novelty, and poorly understood nature of the tasks our systems attempted, and were worsened by the fact that people write so differently. This meant in particular that corpus-based KA approaches suffered because it was impossible to assemble a sizable corpus of high-quality consistent manually written texts in our domains; and structured expert-oriented KA techniques suffered because experts disagreed and because we could not get enough information about special and unusual cases to build robust systems. We believe that such problems are likely to affect many other NLG systems as well. In the long term, we hope that new KA techniques may emerge to help NLG system builders. In the shorter term, we believe that understanding how individual KA techniques can fail, and using a mixture of different KA techniques with different strengths and weaknesses, can help developers acquire NLG knowledge that is mostly correct

Facilitating argumentative knowledge construction with computer-supported collaboration scripts

Online discussions provide opportunities for learners to engage in argumentative debate, but learners rarely formulate well-grounded arguments or benefit individually from participating in online discussions. Learners often do not explicitly warrant their arguments and fail to construct counterarguments (incomplete formal argumentation structure), which is hypothesized to impede individual knowledge acquisition. Computer-supported scripts have been found to support learners during online discussions. Such scripts can support specific discourse activities, such as the construction of single arguments, by supporting learners in explicitly warranting their claims or in constructing specific argumentation sequences, e.g., argument–counterargument sequences, during online discussions. Participation in argumentative discourse is seen to promote both knowledge on argumentation and domain-specific knowledge. However, there have been few empirical investigations regarding the extent to which computer-supported collaboration scripts can foster the formal quality of argumentation and thereby facilitate the individual acquisition of knowledge. One hundred and twenty (120) students of Educational Science participated in the study with a 2×2-factorial design (with vs. without script for the construction of single arguments and with vs. without script for the construction of argumentation sequences) and were randomly divided into groups of three. Results indicated that the collaboration scripts could improve the formal quality of single arguments and the formal quality of argumentation sequences in online discussions. Scripts also facilitated the acquisition of knowledge on argumentation, without affecting the acquisition of domainspecific knowledge

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE