Browsing by Subject "Machine reading"
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Item Addressing the brittleness of knowledge-based question-answering(2009-12) Chaw, Shaw Yi; Porter, Bruce, 1956-; Barker, Kenneth J.; Mooney, Raymond; Novak, Gordon S.; Markman, ArtKnowledge base systems are brittle when the users of the knowledge base are unfamiliar with its content and structure. Querying a knowledge base requires users to state their questions in precise and complete formal representations that relate the facts in the question with relevant terms and relations in the underlying knowledge base. This requirement places a heavy burden on the users to become deeply familiar with the contents of the knowledge base and prevents novice users to effectively using the knowledge base for problem solving. As a result, the utility of knowledge base systems is often restricted to the developers themselves. The goal of this work is to help users, who may possess little domain expertise, to use unfamiliar knowledge bases for problem solving. Our thesis is that the difficulty in using unfamiliar knowledge bases can be addressed by an approach that funnels natural questions, expressed in English, into formal representations appropriate for automated reasoning. The approach uses a simplified English controlled language, a domain-neutral ontology, a set of mechanisms to handle a handful of well known question types, and a software component, called the Question Mediator, to identify relevant information in the knowledge base for problem solving. With our approach, a knowledge base user can use a variety of unfamiliar knowledge bases by posing their questions with simplified English to retrieve relevant information in the knowledge base for problem solving. We studied the thesis in the context of a system called ASKME. We evaluated ASKME on the task of answering exam questions for college level biology, chemistry, and physics. The evaluation consists of successive experiments to test if ASKME can help novice users employ unfamiliar knowledge bases for problem solving. The initial experiment measures ASKME's level of performance under ideal conditions, where the knowledge base is built and used by the same knowledge engineers. Subsequent experiments measure ASKME's level of performance under increasingly realistic conditions. In the final experiment, we measure ASKME's level of performance under conditions where the knowledge base is independently built by subject matter experts and the users of the knowledge base are a group of novices who are unfamiliar with the knowledge base. Results from the evaluation show that ASKME works well on different knowledge bases and answers a broad range of questions that were posed by novice users in a variety of domains.Item Bayesian Logic Programs for plan recognition and machine reading(2012-12) Vijaya Raghavan, Sindhu; Mooney, Raymond J. (Raymond Joseph); Barker, Kenneth; Ghosh, Joydeep; Ravikumar, Pradeep; Shavlik, JudeSeveral real world tasks involve data that is uncertain and relational in nature. Traditional approaches like first-order logic and probabilistic models either deal with structured data or uncertainty, but not both. To address these limitations, statistical relational learning (SRL), a new area in machine learning integrating both first-order logic and probabilistic graphical models, has emerged in the recent past. The advantage of SRL models is that they can handle both uncertainty and structured/relational data. As a result, they are widely used in domains like social network analysis, biological data analysis, and natural language processing. Bayesian Logic Programs (BLPs), which integrate both first-order logic and Bayesian net- works are a powerful SRL formalism developed in the recent past. In this dissertation, we develop approaches using BLPs to solve two real world tasks – plan recognition and machine reading. Plan recognition is the task of predicting an agent’s top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. In the first part of the dissertation, we develop an approach to abductive plan recognition using BLPs. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for abductive plan recognition as is. Therefore, we extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). In the second part of the dissertation, we apply BLPs to the task of machine reading, which involves automatic extraction of knowledge from natural language text. Most information extraction (IE) systems identify facts that are explicitly stated in text. However, much of the information conveyed in text must be inferred from what is explicitly stated since easily inferable facts are rarely mentioned. Human readers naturally use common sense knowledge and “read between the lines” to infer such implicit information from the explicitly stated facts. Since IE systems do not have access to common sense knowledge, they cannot perform deeper reasoning to infer implicitly stated facts. Here, we first develop an approach using BLPs to infer implicitly stated facts from natural language text. It involves learning uncertain common sense knowledge in the form of probabilistic first-order rules by mining a large corpus of automatically extracted facts using an existing rule learner. These rules are then used to derive additional facts from extracted information using BLP inference. We then develop an online rule learner that handles the concise, incomplete nature of natural-language text and learns first-order rules from noisy IE extractions. Finally, we develop a novel approach to calculate the weights of the rules using a curated lexical ontology like WordNet. Both tasks described above involve inference and learning from partially observed or incomplete data. In plan recognition, the underlying cause or the top-level plan that resulted in the observed actions is not known or observed. Further, only a subset of the executed actions can be observed by the plan recognition system resulting in partially observed data. Similarly, in machine reading, since some information is implicitly stated, they are rarely observed in the data. In this dissertation, we demonstrate the efficacy of BLPs for inference and learning from incomplete data. Experimental comparison on various benchmark data sets on both tasks demonstrate the superior performance of BLPs over state-of-the-art methods.Item Knowledge integration in machine reading(2011-08) Kim, Doo Soon; Porter, Bruce, 1956-; Allen, James F.; Barker, Kenneth J.; Lifschitz, Vladimir; Mooney, Raymond J.Machine reading is the artificial-intelligence task of automatically reading a corpus of texts and, from the contents, building a knowledge base that supports automated reasoning and question answering. Success at this task could fundamentally solve the knowledge acquisition bottleneck – the widely recognized problem that knowledge-based AI systems are difficult and expensive to build because of the difficulty of acquiring knowledge from authoritative sources and building useful knowledge bases. One challenge inherent in machine reading is knowledge integration – the task of correctly and coherently combining knowledge snippets extracted from texts. This dissertation shows that knowledge integration can be automated and that it can significantly improve the performance of machine reading. We specifically focus on two contributions of knowledge integration. The first contribution is for improving the coherence of learned knowledge bases to better support automated reasoning and question answering. Knowledge integration achieves this benefit by aligning knowledge snippets that contain overlapping content. The alignment is difficult because the snippets can use significantly different surface forms. In one common type of variation, two snippets might contain overlapping content that is expressed at different levels of granularity or detail. Our matcher can “see past” this difference to align knowledge snippets drawn from a single document, from multiple documents, or from a document and a background knowledge base. The second contribution is for improving text interpretation. Our approach is to delay ambiguity resolution to enable a machine-reading system to maintain multiple candidate interpretations. This is useful because typically, as the system reads through texts, evidence accumulates to help the knowledge integration system resolve ambiguities correctly. To avoid a combinatorial explosion in the number of candidate interpretations, we propose the packed representation to compactly encode all the candidates. Also, we present an algorithm that prunes interpretations from the packed representation as evidence accumulates. We evaluate our work by building and testing two prototype machine reading systems and measuring the quality of the knowledge bases they construct. The evaluation shows that our knowledge integration algorithms improve the cohesiveness of the knowledge bases, indicating their improved ability to support automated reasoning and question answering. The evaluation also shows that our approach to postponing ambiguity resolution improves the system’s accuracy at text interpretation.