Establishing Nomological Networks for Behavioral Science: a Natural Language Processing Based Approach

As the accumulated research base of the behavioral sciences have grown, the amount of actual knowledge discovery has not kept pace as evidenced by an increasing number of disconnected theories and the related problem of construct proliferation. Therefore, integrating social and behavioral sciences across research areas or even disciplines in a meaningful way is imperative. Despite the information systems (IS) discipline’s leadership on creating nomological networks and inter-nomological networks for research integration, a quantitative approach to automatically establish nomological networks from large-scale data is missing. Based on the design science paradigm, we therefore propose a novel natural language processing based approach bringing together these two previous research endeavors. We used a dataset consisting of all the relevant behavioral studies from two tops journal in the IS and psychology fields to evaluate our approach in comparison to human decisions. Finally, the limitations and possible extensions of our approach are critically discussed

HOLMES: A Hybrid Ontology-Learning Materials Engineering System

Designing and discovering novel materials is challenging problem in many domains such as fuel additives, composites, pharmaceuticals, and so on. At the core of all this are models that capture how the different domain-specific data, information, and knowledge regarding the structures and properties of the materials are related to one another. This dissertation explores the difficult task of developing an artificial intelligence-based knowledge modeling environment, called Hybrid Ontology-Learning Materials Engineering System (HOLMES) that can assist humans in populating a materials science and engineering ontology through automatic information extraction from journal article abstracts. While what we propose may be adapted for a generic materials engineering application, our focus in this thesis is on the needs of the pharmaceutical industry. We develop the Columbia Ontology for Pharmaceutical Engineering (COPE), which is a modification of the Purdue Ontology for Pharmaceutical Engineering. COPE serves as the basis for HOLMES. The HOLMES framework starts with journal articles that are in the Portable Document Format (PDF) and ends with the assignment of the entries in the journal articles into ontologies. While this might seem to be a simple task of information extraction, to fully extract the information such that the ontology is filled as completely and correctly as possible is not easy when considering a fully developed ontology. In the development of the information extraction tasks, we note that there are new problems that have not arisen in previous information extraction work in the literature. The first is the necessity to extract auxiliary information in the form of concepts such as actions, ideas, problem specifications, properties, etc. The second problem is in the existence of multiple labels for a single token due to the existence of the aforementioned concepts. These two problems are the focus of this dissertation. In this work, the HOLMES framework is presented as a whole, describing our successful progress as well as unsolved problems, which might help future research on this topic. The ontology is then presented to help in the identification of the relevant information that needs to be retrieved. The annotations are next developed to create the data sets necessary for the machine learning algorithms to perform. Then, the current level of information extraction for these concepts is explored and expanded. This is done through the introduction of entity feature sets that are based on previously extracted entities from the entity recognition task. And finally, the new task of handling multiple labels for tagging a single entity is also explored by the use of multiple-label algorithms used primarily in image processing

Citation Handling: Processing Citation Texts in Scientific Documents

Citation sentences (sentences that cite other papers) play a key role in the summarization of scientific articles. However, a citation-based summarization system that depends on generic natural language processing components, such as parsers or sentence compressors, will perform poorly if those components cannot handle citations correctly. In this thesis, I examine the effect of citation handling on parsing, sentence compression, and multi-document summarization. There are two types of citations that occur in citation sentences: constituent citations and parenthetical citations. I propose an automatic citation classifier based on training data created through Mechanical Turk tasks. I demonstrate that the use of type-specific citation handling as pre-processing improves the performance of a state-of-the-art generic parser, both for quality of the parse trees and running time. Extrinsic evaluations demonstrate that improving the performance of a parser on citation sentences in turn improves the performance of a sentence compressor, Trimmer (Zajic et al., 2007), and a multi-document summarization system, MASCS, according to several summarization measures