7135959
On original generation of structure in legal documents
This position paper advocates a vision in the development of automated legal reasoning and presents evidence supporting the plausibility of that vision. The paper observes that original creation of documents of legal import in either fully formal or semistructured form offers the prospect of greatly reducing the cost and expanding the scope of knowledge engineering for legal reasoning. This, it is claimed, is most likely to be achieved via formalization of various sublanguages of legal discourse. SeaSpeak is an example of such a sublanguage and it appears to be amenable to full formalization. Short of that, much can be done with partial formalization and semistructured documents. The paper presents a tabular format for message expression, motivated by a formal agent communication language.
1878850
Information Brokering on the World Wide Web
Information Brokering is the process of collecting and re-distributing information. As the rich but unstructured sea of data that comprises the World Wide Web continues to grow, so too will the demand for information brokering services. This paper outlines a methodology and an architecture to support information brokering on the Web. Specifically, the two innovations which are described facilitate the construction and distribution of customized views that integrate data from a number of Web sources. An original technique is presented for the extraction of data from the Web and a solution based on JavaScript and HTML is presented to support the embedding of SQL queries within Web documents.
16822197
Generating Original Structure in Regulatory Documents
As technology and society continue to evolve, the size of the corpus of government policies and procedures continues to more than keep pace. The U.S. Federal Tax Code today consumes over 2.8 million words or 6000 pages. There are more than 20,000 cross-references both within the code itself and to external regulations. Navigating the sea of information is a daunting task for the IRS let alone a well-intentioned tax payer, or policy-maker seeking to eliminate redundancies, inconsistencies, or loopholes. While tools for tasks such as compliance checking or query answering have long held promise, automated reasoning, however intelligent, needs something to reason upon, a formalized knowledge base of some kind. In other domains people may be the primary targets of knowledge engineering; in the policy realm, much of the requisite knowledge resides in legal and regulatory documents.
45512564
Constraint-driven hierarchical information extraction
This abstract summarizes a propsal to use semistructured constraints as a general framework for information extraction. The proposal builds on the intuition that written documents tend to contain a great deal of redundancy: Tell them what you are going to tell them, tell them, tell them what you told them. While current methods overlook or eliminate redundancy, we seek to explicitly represent and exploit both repetition and other natural patterns in semistructured text. We summarize our early research on verification, outline current and future directions, and identify related work. Our initial work assumes knowledge of both document structure and its attendant constraints for the purpose of extraction verification. Specifically, semantic and structural constraints are used to check wrapper accuracy. For example, a structural constraint might ensure that the parties to a contract are introduced at the beginning of the document. A foreign-key constraint checks whether the parties are included on a list of approved signers. That signatories are repeated at the end of a contract is represented by an inverse constraint. A wrapper to extract seven attributes from legal arbitration decisions was applied to 200 cases. Constraint-based verification produced only ten false negatives, the majority of which were due to errors in wrapper delimiters. Further results were reported earlier [Hunter, et al., 2002]. We are currently experimenting with semistructured constraints in hierarchical extraction. Documents with the same semantic and structural constraints are placed in the same class. Class-specific routines fragment the document, extract from each fragment, and recurse on selected components. We propose the use of semistructured constraints as a framework for both fragmentation and extraction. For example, in addition to simple features like HTML-nesting, constraints can dictate whether certain values or structural properties functionally determine other fragments. A dissenting opinion must correspond to a legal decision with more than one judge.
44753947
Constraint-based wrapper specification and verification for cooperative information systems
In this paper, we propose the use of semistructured constraints in wrappers to mitigate the impact of poor extraction accuracy on Cooperative Information System (CIS) data quality. Wrappers are a critical element of CISs whenever the constituent information systems publish semistructured text such as forms, reports, and memos rather than structured databases. The accuracy of CIS data that stem from text depends upon the wrappers as well as the accuracy of the underlying sources. Wrapper specification is the process of defining patterns (i.e. regular expressions) to extract information from semistructured text. Wrapper verification is the process of ensuring extraction accuracy--that the extracted information faithfully reflects the underlying source. We focus on the problem of extraction accuracy. We use constraints on semistructured data for both wrapper specification and verification. Consequently, we perform extraction and verification simultaneously. We apply the concept to wrappers for a Uniform Domain Name Dispute Resolution Policy (UDRP) CIS of arbitration decisions. UDRP decisions are currently distributed across arbitration authorities on three continents. The accuracy of data extracted using constraint-based specification and verification is measured by Type I and Type II errors.
8163714
Scaling Laws in Natural Scenes and the Inference of 3D Shape
This paper explores the statistical relationship between natural images and their underlying range (depth) images. We look at how this relationship changes over scale, and how this information can be used to enhance low resolution range data using a full resolution intensity image. Based on our findings, we propose an extension to an existing technique known as shape recipes [3], and the success of the two methods are compared using images and laser scans of real scenes. Our extension is shown to provide a two-fold improvement over the current method. Furthermore, we demonstrate that ideal linear shape-from-shading filters, when learned from natural scenes, may derive even more strength from shadow cues than from the traditional linear-Lambertian shading cues.
17821880
Indentation and Protrusion Detection and Its Applications
In this paper, we investigated the mechanism of dividing a 2D-object border into a set of local and global indentation and protrusion segments by extending the classic curvature scale-space filtering method. The resultant segments, arranged in hierarchical structures, can represent the object shape. Applying this technique, we derived a border irregularity measure for pigmented skin lesions. The measure correlated well with experienced dermatologists' evaluations and may be useful for measuring the malignancy of the lesion. Furthermore, we can use the method to discover all the bays in an aerial map.
5750510
Online Query Relaxation via Bayesian Causal Structures Discovery
We introduce a novel algorithm, TOQR, for relaxing failed queries over databases, that is, over-constrained DNF queries that return an empty result. TOQR uses a small dataset to discover the implicit relationships among the domain attributes, and then it exploits this domain knowledge to relax the failed query. TOQR starts with a relaxed query that does not include any constraint, and it tries to add to it as many as possible of the original constraints or their relaxations. The order in which the constraints are added is derived from the domain's causal structure, which is learned by applying the TAN algorithm to the small training dataset. Our experiments show that TOQR clearly outperforms other approaches: even when trained on a handful of examples, it successfully relaxes more that 97% of the failed queries; furthermore, TOQR'S relaxed queries are highly similar to the original failed query.
35157
A cooperative coevolutionary algorithm for multiobjective optimization
This paper presents a kind of cooperative coevolutionary algorithm (CCEA) for multi-objective optimization (MOO). In this algorithm, solutions evolve in the form of cooperative subpopulations. An archive stores non-dominated solutions and helps to evaluate individuals in the subpopulations. The mechanism of niching is applied to maintain the diversity of solutions in the archive. Meanwhile, an extending operator is designed to mine information on solution distribution from the archive and guide the search to regions that are not explored enough. Extensive simulations are performed on different benchmark problems for various multi-objective evolutionary algorithms (MOEAs) and indicate that CCEA is strongly competitive with five recent well-known MOEAs in finding a good non-dominated solution set.
15212662
A 100-MIPS GaAs asynchronous microprocessor
The authors describe how they ported an asynchronous microprocessor previously implemented in CMOS to gallium arsenide, using a technology-independent asynchronous design technique. They introduce new circuits including a sense-amplifier, a completion detection circuit, and a general circuit structure for operators specified by production rules. The authors used and tested these circuits in a variety of designs.<<ETX>>