Ken Barker

IJCAI Conference 2022 Conference Paper

Knowledge-Based News Event Analysis and Forecasting Toolkit

• Oktie Hassanzadeh
• Parul Awasthy
• Ken Barker
• Onkar Bhardwaj
• Debarun Bhattacharjya
• Mark Feblowitz
• Lee Martie
• Jian Ni

We present a toolkit for knowledge-based news event analysis and forecasting. The toolkit is powered by a Knowledge Graph (KG) of events curated from structured and unstructured sources of event-related knowledge. The toolkit provides functions for 1) mapping ongoing news headlines to concepts in the KG, 2) retrieval, reasoning, and visualization for causal analysis and forecasting, and 3) extraction of causal knowledge from text documents to augment the KG with additional domain knowledge. Each function has a number of implementations using a wide range of state-of-the-art neuro-symbolic techniques. We show how the toolkit enables building a human-in-the-loop explainable solution for event analysis and forecasting.

AIJ Journal 2009 Journal Article

Automatic interpretation of loosely encoded input

• James Fan
• Ken Barker
• Bruce Porter

Knowledge-based systems are often brittle when given unanticipated input, i. e. assertions or queries that misalign with the ontology of the knowledge base. We call such misalignments “loose speak”. We found that loose speak occurs frequently in interactions with knowledge-based systems, but with such regularity that it often can be interpreted and corrected algorithmically. We also found that the common types of loose speak, such as metonymy and noun-noun compounds, have a common root cause. We created a Loose-Speak Interpreter and evaluated it with a variety of empirical studies in different domains and tasks. We found that a single, parsimonious algorithm successfully interpreted numerous manifestations of loose speak with an average precision of 98% and an average recall of 90%.

AAAI Conference 2007 Conference Paper

Learning by Reading: A Prototype System, Performance Baseline and Lessons Learned

• Ken Barker
• S. Chaw
• N. Friedland
• J. Hobbs
• D. Israel
• D.S. Kim
• B. Porter

KR Conference 2004 Conference Paper

A Question-Answering System for AP Chemistry: Assessing KR&R Technologies

• Ken Barker
• Vinay Chaudhri
• Jason Chaw
• Peter Clark
• James Fan
• David Israel
• Sunil Mishra
• Bruce Porter

Basic research in knowledge representation and reasoning (KR&R) has steadily advanced over the years, but it has been difficult to assess the capability of fielded systems derived from this research. In this paper, we present a knowledge-based question-answering system that we developed as part of a broader effort by Vulcan Inc. to assess KR&R technologies, and the result of its assessment. The challenge problem presented significant new challenges for knowledge representation, compared with earlier such assessments, due to the wide variability of question types that the system was expected to answer. Our solution integrated several modern KR&R technologies, in particular semantically well-defined frame systems, automatic classification methods, reusable ontologies, a methodology for knowledge base construction, and a novel extension of methods for explanation generation. The resulting system exhibited high performance, achieving scores for both accuracy and explanation which were comparable to human performance on similar tests. While there are qualifications to this result, it is a significant achievement and an informative data point about the state of the art in KR&R, and reflects significant progress by the field.

KR Conference 2004 Conference Paper

Towards a Quantitative, Platform-Independent Analysis of Knowledge Systems

• Noah S. Friedland
• Paul G. Allen
• Michael Witbrock
• Gavin Matthews
• Nancy Salay
• Pierluigi Miraglia
• Jurgen Angele
• Steffen Staab

The Halo Pilot, a six-month effort to evaluate the state-ofthe- art in applied Knowledge Representation and Reasoning (KRR) systems, collaboratively developed a taxonomy of failures with the goal of creating a common framework of metrics against which we could measure inter- and intra- system failure characteristics of each of the three Halo knowledge applications. This platform independent taxonomy was designed with the intent of maximizing its coverage of potential failure types; providing the necessary granularity and precision to enable clear categorization of failure types; and providing a productive framework for short and longer term corrective action. Examining the failure analysis and initial empirical use of the taxonomy provides quantitative insights into the strengths and weaknesses of individual systems and raises some issues shared by all three. These results are particularly interesting when considered against the long history of assumed reasons for knowledge system failure. Our study has also uncovered some shortcomings in the taxonomy itself, implying the need to improve both its granularity and precision. It is the hope of Project Halo to eventually produce a failure taxonomy and associated methodology that will be of general use in the fine-grained analysis of knowledge systems.

IJCAI Conference 2003 Conference Paper

The Knowledge Required to Interpret Noun Compounds

• James Fan
• Ken Barker
• Bruce Porter

Noun compound interpretation is the task of determining the semantic relations among the constituents of a noun compound. For example, "concrete floor" means a floor made of concrete, while "gymnasium floor" is the floor region of a gymnasium. We would like to enable knowledge acquisition systems to interpret noun compounds, as part of their overall task of translating imprecise and incomplete information into formal representations that support automated reasoning. However, if interpreting noun compounds requires detailed knowledge of the constituent nouns, then it may not be worth doing: the cost of acquiring this knowledge may outweigh the potential benefit. This paper describes an empirical investigation of the knowledge required to interpret noun compounds. It concludes that the axioms and ontological distinctions important for this task are derived from the top levels of a hierarchical knowledge base (KB); detailed knowledge of specific nouns is less important. This is good news, not only for our work on knowledge acquisition systems, but also for research on text understanding, where noun compound interpretation has a long history. A more detailed version of this paper can be found in [Fan et al, 2003].