Jaimie Murdock

Evaluating Dynamic Ontologies

Ontology evaluation poses a number of difficult challenges requiring different evaluation methodologies, particularly for a “dynamic ontology” generated by a combination of automatic and semi-automatic methods. We review evaluation methods that focus solely on syntactic (formal) correctness, on the preservation of semantic structure, or on pragmatic utility. We propose two novel methods for dynamic ontology evaluation and describe the use of these methods for evaluating the different taxonomic representations that are generated at different times or with different amounts of expert feedback. These methods are then applied to the Indiana Philosophy Ontology (InPhO), and used to guide the ontology enrichment process.

Exploration and exploitation of Victorian science in Darwin’s reading notebooks

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwins behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwins own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwins consumption more exploratory than the cultures production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.

InPhO: a system for collaboratively populating and extending a dynamic ontology

InPhO is a system that combines statistical text processing, information extraction, human expert feedback, and logic programming to populate and extend a dynamic ontology for the field of philosophy. Integrated in the editorial workflow of the Stanford Encyclopedia of Philosophy (SEP), it will provide important metadata features such as automated generation of cross-references, semantic search, and ontology driven conceptual navigation.

Topic Exploration with the HTRC Data Capsule for Non-Consumptive Research

In this half-day tutorial, we will show 1) how the HathiTrust Research Center (HTRC) Data Capsule can be used for non-consumptive research over collection of texts and 2) how integrated tools for LDA topic modeling and visualization can be used to drive formulation of new research questions. Participants will be given an account in the HTRC Data Capsule and taught how to use the workset manager to create a corpus, and then use the VMs secure mode to download texts and analyze their contents.

LODE: linking digital humanities content to the web of data

Numerous digital libraries projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data, including JCDL. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of our Linked Open Data Enhancer Lode is to work “out of the box”, enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With Lode we enable non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. Lode links and enhances the local RDF repository without reducing the quality of the data. In particular, we support the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high quality linking candidates using state of the art matching algorithms. We hope that the Lode framework will be useful to digital humanities scholars complementing other digital humanities tools.

Genetic clustering for the identification of species

Artificial life simulations can yield distinct populations of agents representing different adaptations to a common environment or specialized adaptations to different environments. Here we apply a standard clustering algorithm to the genomes of such agents to discover and characterize these subpopulations. As evolution proceeds new subpopulations are produced, which show up as new clusters. Cluster centroids allow us to characterize these different subpopulations and identify their distinct adaptation mechanisms. We suggest these subpopulations may reasonably be thought of as species, even if the simulation software allows interbreeding between members of the different subpopulations. Our results indicate both sympatric and allopatric speciation are present in the Polyworld artificial life system. Our analysis suggests that intra- and inter-cluster fecundity differences may be sufficient to foster sympatric speciation in artificial and biological ecosystems.

Mapping the intersection of science and philosophy

This poster presents what we believe to be the first attempt to empirically measure and visualize the cross-pollination of science and philosophy through citation patterns. Using the Stanford Encyclopedia of Philosophy as a proxy for the philosophical literature, we plot SEP citations onto the UCSD Map of Science to highlight areas of science which overlap with philosophical discussion. An outline of further studies is also discussed.

Towards publishing secure capsule-based analysis

Computational engagement with the HathiTrust Digital Library (HTDL) is confounded by the in- copyright status and licensing restrictions on the majority of the content. Because of these limitations, computational analysis on the HTDL must either be carried out in a secure environment or on derivative datasets. The HathiTrust Research Center (HTRC) Data Capsule service provides researchers with a secure environment through which they invoke tools that create, analyze, and export non-consumptive datasets. These derivative datasets, so long as they do not reproduce the full-text of the original work, are a transformative work protected by Fair Use provisions of United States Copyright Law, and can be published for reuse by other researchers, as the HTRC Extracted Features Dataset has been. Secure environments and derivative datasets enable researchers to engage with restricted data from focused studies of a few dozen volumes to large- scale experiments on millions of volumes. This paper describes advances in the Capsule service through a case study of how the HTRC Data Capsule service has advanced our activities on provenance, workflows, worksets, and non-consumptive exports through a topic modeling example. We also discuss the potential applications of this Capsule-based model to other digital libraries wrestling with research access and copyright restrictions.

Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library

We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of “drill-down” topic modeling—simultaneously reducing both the size of the corpus and the unit of analysis—we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of “close reading” needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted.