Ken Kaneiwa

An upper ontology for event classifications and relations

For knowledge representation and reasoning, there is a need to consider the nature of events because event data describe various features and behaviors of the occurrences of actions and changes in the real world. In this paper, we propose to establish an upper event-ontology in order-sorted logic as an infrastructure for event knowledge bases. Our event ontology contains a classification of event entities (e.g., natural events and artificial events) and event relationships (e.g., causal relations and next-event relations). These ontological characterizations are needed for a theoretical basis of applications such as implementation of event databases, detection of event relationships, and annotation of event data.

A sequential pattern mining algorithm using rough set theory

Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns in time series data. This task becomes difficult when valuable patterns are locally or implicitly involved in noisy data. In this paper, we propose a method for mining such local patterns from sequences. Using rough set theory, we describe an algorithm for generating decision rules that take into account local patterns for arriving at a particular decision. To apply sequential data to rough set theory, the size of local patterns is specified, allowing a set of sequences to be transformed into a sequential information system. We use the discernibility of decision classes to establish evaluation criteria for the decision rules in the sequential information system.

Order-sorted logic programming with predicate hierarchy

Order-sorted logic has been formalized as first-order logic with sorted terms where sorts are ordered to build a hierarchy (called a sort-hierarchy). These sorted logics lead to useful expressions and inference methods for structural knowledge that ordinary first-order logic lacks. Nitta et al. pointed out that for legal reasoning a sort-hierarchy (or a sorted term) is not sufficient to describe structural knowledge for event assertions, which express facts caused at some particular time and place. The event assertions are represented by predicates with n arguments (i.e., n-ary predicates), and then a particular kind of hierarchy (called a predicate hierarchy) is built by a relationship among the predicates. To deal with such a predicate hierarchy, which is more intricate than a sort-hierarchy, Nitta et al. implemented a typed (sorted) logic programming language extended to include a hierarchy of verbal concepts (corresponding to predicates). However, the inference system lacks a theoretical foundation because its hierarchical expressions exceed the formalization of order-sorted logic. In this paper, we formalize a logic programming language with not only a sort-hierarchy but also a predicate hierarchy. This language can derive general and concrete expressions in the two kinds of hierarchies. For the hierarchical reasoning of predicates, we propose a manipulation of arguments in which surplus and missing arguments in derived predicates are eliminated and supplemented. As discussed by Allen, McDermott and Shoham in research on temporal logic and as applied by Nitta et al. to legal reasoning, if each predicate is interpreted as an event or action (not as a static property), then missing arguments should be supplemented by existential terms in the argument manipulation. Based on this, we develop a Horn clause resolution system extended to add inference rules of predicate hierarchies. With a semantic model restricted by interpreting a predicate hierarchy, the soundness and completeness of the Horn-clause resolution is proven.

A rough set approach to multiple dataset analysis

Abstract: In the area of data mining, the discovery of valuable changes and connections (e.g., causality) from multiple data sets has been recognized as an important issue. This issue essentially differs from finding statistical associations in a single data set because it is complicated by the different data behaviors and relationships across multiple data sets. Using rough set theory, this paper proposes a change and connection mining algorithm for discovering a time delay between the quantitative changes in the data of two temporal information systems and for generating the association rules of changes from their connected decision table. We establish evaluation criteria for the connectedness of two temporal information systems with varying time delays by calculating weight-based accuracy and coverage of the association rules of changes, adjusted by a fuzzy membership function.

On the complexities of consistency checking for restricted UML class diagrams

Automatic debugging of UML class diagrams helps in the visual specification of software systems because users cannot detect errors in logical consistency easily. This study focuses on the tractable consistency checking of UML class diagrams. We accurately identify inconsistencies in these diagrams by translating them into first-order predicate logic that is generalized by counting quantifiers and classify their expressivities by eliminating certain components. We introduce optimized algorithms that compute the respective consistencies of class diagrams of different expressive powers in P, NP, PSPACE, or EXPTIME with respect to the size of the class diagrams. In particular, owing to the restrictions imposed on attribute value types, the complexities of consistency checking of class diagrams decrease from EXPTIME to P and PSPACE in two cases: (i) when the class diagrams contain disjointness constraints and overwriting/multiple inheritances and (ii) when the class diagrams contain both these components along with completeness constraints. Additionally, we confirm the existence of a restriction of class diagrams that prevents any logical inconsistency.

Extended Full Computation-Tree Logic with Sequence Modal Operator: Representing Hierarchical Tree Structures

An extended full computation-tree logic, CTLS*, is introduced as a Kripke semantics with a sequence modal operator. This logic can appropriately represent hierarchical tree structures where sequence modal operators in CTLS* are applied to tree structures. An embedding theorem of CTLS* into CTL* is proved. The validity, satisfiability and model-checking problems of CTLS* are shown to be decidable. An illustrative example of biological taxonomy is presented using CTLS* formulas.

Evaluation data and prototype system WISDOM for information credibility analysis

Purpose – The purpose of this paper is to describe evaluation data and a prototype system named WISDOM used for analyzing information credibility based on natural language processing.Design/methodology/approach – The authors started the Information Credibility Criteria project in April, 2007, mainly to analyze the credibility of information (text) on the web. The project proposes to capture information credibility based on four criteria (content, sender, appearance, and social valuation) and aims to analyze and organize them logically using natural language processing based on predicate argument structure.Findings – The evaluation data described in this paper were developed as learning and verifying data for these various analysis modules and are composed of manually‐annotated data based on each evaluation criteria about several pre‐selected topics such as current events and medical issues. The prototype system WISDOM was developed to provide information credibility from different perspectives.Orginality/...

An Order-Sorted Resolution with Implicitly Negative Sorts

We usually use natural language vocabulary for sort names in order-sorted logics, and some sort names may contradict other sort names in the sort-hierarchy. These implicit negations, called lexical negations in linguistics, are not explicitly prefixed by the negation connective. In this paper, we propose the notions of structured sorts, sort relations, and the contradiction in the sort-hierarchy. These notions specify the properties of these implicit negations and the classical negation, and thus, we can declare the exclusivity and the totality between two sorts, one of which is affirmative while the other is negative. We regard the negative affix as a strong negation operator, and the negative lexicon as an antonymous sort that is exclusive to its counterpart in the hierarchy. In order to infer from these negations, we integrate a structured sort constraint system into a clausal inference system.

An order-sorted quantified modal logic for meta-ontology

The notions of meta-ontology enhance the ability to process knowledge in information systems; in particular, ontological property classification deals with the kinds of properties in taxonomic knowledge based on a philosophical analysis. The goal of this paper is to devise a reasoning mechanism to check the ontological and logical consistency of knowledge bases, which is important for reasoning services on taxonomic knowledge. We first consider an ontological property classification that is extended to capture individual existence and time and situation dependencies. To incorporate the notion into logical reasoning, we formalize an order-sorted modal logic that involves rigidity, sortality, and three kinds of modal operators (temporal/situational/any world). The sorted expressions and modalities establish axioms with respect to properties, implying the truth of properties in different kinds of possible worlds and in varying domains in Kripke semantics. We provide a prefixed tableau calculus to test the satisfiability of such sorted modal formulas, which validates the ontological axioms of properties.

Distributed reasoning with ontologies and rules in order-sorted logic programming

Integrating ontologies and rules on the Semantic Web enables software agents to interoperate between them; however, this leads to two problems. First, reasoning services in SWRL (a combination of OWL and RuleML) are not decidable. Second, no studies have focused on distributed reasoning services for integrating ontologies and rules in multiple knowledge bases. In order to address these problems, we consider distributed reasoning services for ontologies and rules with decidable and effective computation. In this paper, we describe multiple order-sorted logic programming that transfers rigid properties from knowledge bases. Our order-sorted logic contains types (rigid sorts), non-rigid sorts, and unary predicates that distinctly express essential sorts, non-essential sorts, and non-sortal properties. We formalize the order-sorted Horn-clause calculus for such properties in a single knowledge base. This calculus is extended by embedding rigid-property derivation for multiple knowledge bases, each of which can transfer rigid-property information from other knowledge bases. In order to enable the reasoning to be effective and decidable, we design a query-answering system that combines order-sorted linear resolution and rigid-property resolution as top-down algorithms.