Merging uncertain information with semantic heterogeneity in XML

Semistructured information can be merged in a logic-based framework [6, 7]. This framework has been extended to deal with uncertainty, in the form of probability values, degrees of beliefs, or necessity measures, associated with leaves (i.e. textentries) in the XML documents [3]. In this paper we further extend this approach to modelling and merging uncertain information that is defined at different levels of granularity of XML textentries, and to modelling and reasoning with XML documents that contain semantically heterogeneous uncertain information on more complex elements in XML subtrees. We present the formal definitions for modelling, propagating and merging semantically heterogeneous uncertain information and explain how they can be handled using logic-based fusion techniques. Anthony Hunter received a B.Sc. (1984) from the University of Bristol and an M.Sc. (1987) and Ph.D. (1992) from Imperial College, London. He is currently a reader in the Department of Computer Science at University College London. His main research interests are: Knowledge representation and reasoning, Analysing inconsistency, Argumentation, Default reasoning and Knowledge Fusion. Weiru Liu is a senior lecturer at the School of Computer Science, Queen's University Belfast. She received her B.Sc. and M.Sc. degrees in Computer Science from Jilin University, P.R China, and her Ph.D. degree in Artificial Intelligence from the University of Edinburgh. Her main research interests include reasoning under uncertainty, knowledge representation and reasoning, uncertain knowledge and information fusion, and knowledge discovery in databases. She has published over 50 journal and conference papers in these areas.     

A knowledge-based approach to manage information systems interoperability

Interoperability is a key property of enterprise applications, which is hard to achieve due to the large number of interoperating components and semantic heterogeneity. The inherent complexity of interoperability problems implies that there exists no silver bullet to solve them. Rather, the knowledge about how to solve wicked interoperability problems is hidden in the application cases that expose those problems. The paper addresses the question of how to organise and use method knowledge to resolve interoperability problems. We propose the structure of a knowledge-based system that can deliver situation-specific solutions, called method chunks. Situational Method Engineering promotes modularisation and formalisation of method knowledge in the form of reusable method chunks, which can be combined to compose a situation-specific method. The method chunks are stored in a method chunk repository. In order to cater for management and retrieval, we introduce an Interoperability Classification Framework, which is used to classify and tag method chunks and to assess the project situation in which they are to be used. The classification framework incorporates technical as well as business and organisational aspects of interoperability. This is an important feature as interoperability problems typically are multifaceted spanning multiple aspects. We have applied the approach to analyse an industry case from the insurance sector to identify and classify a set of method chunks.     

A motivational approach to information providing: A resource exchange perspective

Although the use of information resources is a competitive advantage for many firms, empirical research on individuals’ motivations for information providing is complex. We therefore assess both intrapersonal and interpersonal motivations and relevant work conditions that moderate the effects of motivation on information providing. The results of this study of employees from 51 firms in Korea show that altruistic traits, social rewards, and reciprocity all positively influence willingness to provide information. These effects are moderated by individuals’ tenure in the organization. The results of this study help shed light on intrapersonal motivations and the dynamic role of interpersonal motivations in information providing.     

Towards knowledge-based infolog specifications A case study of information engineering

A radically new approach to computing, the so-called knowledge-based information processing is achieving striking success in supporting activities needing logical power, human judgement, reasoning and expertise. It offers universal applicability for problem-solving, in particular in more complex tasks than those presently handled by computer systems. The current state of the art and future prospects for the development of a computer system which either performs expert tasks automatically or is used interactively by experts to increase their productivity are reviewed. The weak points we need to look at, namely the lack of guidelines for building such systems, and some dead ends are indicated. Some new results are expected by applying this key technology to our case study, the construction of a Systems Specification Support System (S4). Its knowledge base written in PROLOG captures and encodes human expertise about the INFOLOG model and INFOLOG specifications, which is made available via consultation to formulate specifications and, possibly, advice. The architecture of the knowledge base is presented by discussing its abstraction levels. This investigation provides also a methodology in structuring a systems analyst's knowledge about an application. This means how to find out the main kinds of objects, including their relationships, in some problem domain.     

A split-combination approach to merging knowledge bases in possibilistic logic

We propose an adaptive approach to merging possibilistic knowledge bases that deploys multiple operators instead of a single operator in the merging process. The merging approach consists of two steps: the splitting step and the combination step. The splitting step splits each knowledge base into two subbases and then in the second step, different classes of subbases are combined using different operators. Our merging approach is applied to knowledge bases which are self-consistent and results in a knowledge base which is also consistent. Two operators are proposed based on two different splitting methods. Both operators result in a possibilistic knowledge base which contains more information than that obtained by the t-conorm (such as the maximum) based merging methods. In the flat case, one of the operators provides a good alternative to syntax-based merging operators in classical logic. This paper is a revised and extended version of [36].     

New approach to information fusion steady-state Kalman filtering

By the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model, a unified and general information fusion steady-state Kalman filtering approach is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the filtering, smoothing, and prediction fusion problems for state or signal. The optimal fusion rule weighted by matrices is re-derived as a weighted least squares (WLS) fuser, and is reviewed. An optimal fusion rule weighted by diagonal matrices is presented, which is equivalent to the optimal fusion rule weighted by scalars for components, and it realizes a decoupled fusion. The new algorithms of the steady-state Kalman estimator gains are presented. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors by Lyapunov equations are presented. The exponential convergence of the iterative solution of Lyapunov equation is proved. It is proved that the optimal fusion estimators under three weighted fusion rules are locally optimal, but are globally suboptimal. The proposed steady-state Kalman fusers can reduce the on-line computational burden, and are suitable for real-time applications. A simulation example for the 3-sensor steady-state Kalman tracking fusion estimators shows their effectiveness and correctness, and gives the accuracy comparison of the fusion rules.     

aDApTA: Adaptive approach to information integration in dynamic environments

In complex environments, decision making process faces several challenges due to the dynamic of unpredicted events. There is a highly dynamic flow of information in a situation where most of the relevant data is heterogeneous, cannot be predicted and loaded previously. Even though many efforts have been made in data integration research, choosing the appropriate approach to an unknown (in advance) semantic level of information is still an open issue. The traditional relational data integration methods are not suitable to support complex environments due to the rigidity of their schema. As an alternative, Graph Data models can handle structural heterogeneity complemented by semantic representations like Linked Open Data on the Web. Based on these alternatives, this research proposes aDApTA, an adaptive approach to information integration and its associated architecture. An application example in a real scenario is presented. In this scenario, the logistics of relief goods are managed by a dynamic and multi-perspective view.     

Web information fusion: A review of the state of the art

In this paper, we introduce and overview advances in the field of Web information fusion and integration. As it is such a broad and diverse topic that is researched in many different fields, we choose to provide a unified view by focusing on selected survey articles that extensively cover earlier research contributions. Given the important role that ontologies are playing in Web information fusion and the emergence and fast development of the Semantic Web and Web 3.0 technologies, a separate section is devoted to the topic of ontology research and the Semantic Web. Then, in the section on Web-based support systems, several applications that are enabled as the result of advances in Web information fusion are discussed.     

A content-search information retrieval process based on conceptual graphs

An intelligent information retrieval system is presented in this paper. In our approach, which complies with the logical view of information retrieval, queries, document contents and other knowledge are represented by expressions in a knowledge representation language based on the conceptual graphs introduced by Sowa. In order to take the intrinsic vagueness of information retrieval into account, i.e. to search documents imprecisely and incompletely represented in order to answer a vague query, different kinds of probabilistic logic are often used. The search process described in this paper uses graph transformations instead of probabilistic notions. This paper is focused on the content-based retrieval process, and the cognitive facet of information retrieval is not directly addressed. However, our approach, involving the use of a knowledge representation language for representing data and a search process based on a combinatorial implementation of van Rijsbergen’s logical uncertainty principle, also allows the representation of retrieval situations. Hence, we believe that it could be implemented at the core of an operational information retrieval system. Two applications, one dealing with academic libraries and the other concerning audiovisual documents, are briefly presented.     

A hybrid ontology approach for integration of obsolescence information

Information sharing among distributed obsolescence management systems is a challenge because of the heterogeneity of data (data with different forms and representations). Indeed, this is the main hurdle that exists for current tools managing product obsolescence. This paper presents a hybrid ontology approach for the integration of obsolescence information that combines a global ontology that provides a shared vocabulary for the specification of the semantics of obsolescence domain knowledge, along with local ontologies that describe structures of multiple data sources distributed in various obsolescence management tools. A procedure is provided for mapping local ontologies to the global ontology by quantifying relationships between classes and identifying groups of classes with a clustering method. Ontologies and rules of identifying relationships are realized with OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language). With the application of the hybrid ontology approach, a unified view of data is provided to support decision making for efficient obsolescence management and a structure where new sources of information can be easily added with little modification in the future.     

A multilevel information fusion approach for visual quality inspection

For visual quality inspection systems to be applicable in industrial settings, it is mandatory that they are highly flexible, robust and accurate. In order to improve these characteristics a multilevel information fusion approach is presented. A first fusion step at the feature-level enables the system to learn from an undefined number of potential defects which might be segmented from the images. This allows for the quality control operators to label the data at the image-level and the sub-image-level, and use this information during the learning process. Additionally, the operators are allowed to provide a confidence measure for their labelling. The additional information obtained from the increased flexibility of the operator inputs allows to build more accurate classifiers. A second fusion step at the decision-level combines the classifications of different classifiers, making the system more accurate and more robust with respect to the classification method chosen. The experimental results, using various artificial and real-world visual quality inspection data sets, show that each of these fusion approaches can significantly improve the classification accuracy. If both information fusion approaches are combined the accuracy increases even further, significantly outperforming each of the fusion approaches on their own.     

Modifying inconsistent comparison matrix in analytic hierarchy process: A heuristic approach

Test of consistency is a critical step in the AHP methodology. When a pairwise comparison matrix fails to satisfy the consistency requirement, a decision maker needs to make revisions. To aid the decision maker's revising process, several approaches identify changes to the consistency requirement with respect to changes to a single entry in the original inconsistent matrix [P.T. Harker, Derivatives of the Perron root of a positive reciprocal matrix: with application to the analytic hierarchy process, Applied Mathematics and Computation, 22, 217–232 (1987); T.L. Saaty, Decision-making with the AHP: Why is the principal eigenvector necessary, European Journal of Operational Research, 145, 85–91 (2003)]. Instead of revising single entries, Xu and Wei [Z. Xu and C. Wei, A consistency improving method in the analytic hierarchy process, European Journal of Operational Research, V.116, 443–449 (1999)] derived a consistent matrix by an auto-adaptive process based on the original inconsistent matrix. In this paper, we develop a heuristic approach that auto-generates a consistent matrix based on the original inconsistent matrix. Expressing the inconsistent matrix in terms of a deviation matrix, an iterative process adjusts the deviation matrix to improve the consistency ratio, while preserving most of the original comparison information. We show that the proposed method is able to preserve more original comparison information than Xu and Wei [Z. Xu and C. Wei, A consistency improving method in the analytic hierarchy process, European Journal of Operational Research, V.116, 443–449 (1999)]. It is also shown that the heuristic approach can be used to examine the effects of revising a sub-bloc as well as revising a single entry of the original matrix.     

Target-driven merging of taxonomies with Atom

The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies. We propose a new taxonomy merging algorithm called Atom that, given as input two taxonomies and a match mapping between them, can generate an integrated taxonomy in a largely automatic manner. The approach is target-driven, i.e. we merge a source taxonomy into the target taxonomy and preserve the target ontology as much as possible. In contrast to previous approaches, Atom does not aim at fully preserving all input concepts and relationships but strives to reduce the semantic heterogeneity of the merge results for improved understandability. Atom can also exploit advanced match mappings containing is-a relationships in addition to equivalence relationships between concepts of the input taxonomies. We evaluate Atom for synthetic and real-world scenarios and compare it with a full merge solution.     

一种自适应信息集成方法

检测相似重复记录是信息集成中的关键任务之一,尽管已经提出了各种检测相似重复记录的方法,但字符串匹配算法是这些检测方法中的核心。在提出的自适应信息集成算法中,用一个综合了编辑距离和标记距离的混合相似度去度量字符串之间的相似度。为了避免由于表达方式的差异而造成的字符串之间的不匹配,字符串被分割成独立的单词后按单词的第一个字符进行排序。在单词的匹配中,对拼写错误和缩写有一定的容错功能。实验结果表明,自适应信息集成方法比用Smith Waterman和Jaro距离有更高的正确率。     

Solving conflicts in information merging by a flexible interpretation of atomic propositions

Although many techniques for merging conflicting propositional knowledge bases have already been proposed, most existing work is based on the idea that inconsistency results from the presence of incorrect pieces of information, which should be identified and removed. In contrast, we take the view in this paper that conflicts are often caused by statements that are inaccurate rather than completely false, suggesting to restore consistency by interpreting certain statements in a flexible way, rather than ignoring them completely. In accordance with this view, we propose a novel approach to merging which exploits extra-logical background information about the semantic relatedness of atomic propositions. Several merging operators are presented, which are based on different formalizations of this background knowledge, ranging from purely qualitative approaches, related to possibilistic logic, to quantitative approaches with a probabilistic flavor. Both syntactic and semantic characterizations are provided for each merging operator, and the computational complexity is analyzed.     

A knowledge-based approach to design

A framework is presented for constructing knowledge-based aids for design problems. In particular, we describe the organization of an interactive knowledge-based consultant for VLSI design (called VEXED?an acronym for VLSI expert editor), and a prototype implementation of VEXED. The paper focuses on the principles underlying the design of VEXED, and on several lessons and research issues that have arisen from implementing and experimenting with this prototype.     

Towards an algebraic theory of information integration

Information integration systems provide uniform interfaces to varieties of heterogeneous information sources. For query answering in such systems, the current generation of query answering algorithms in local-as-view (source-centric) information integration systems all produce what has been thought of as “the best obtainable” answer, given the circumstances that the source-centric approach introduces incomplete information into the virtual global relations. However, this “best obtainable” answer does not include all information that can be extracted from the sources because it does not allow partial information. Neither does the “best obtainable” answer allow for composition of queries, meaning that querying a result of a previous query will not be equivalentto the composition of the two queries. In this paper, we provide a foundation for information integration, based on the algebraic theory of incomplete information. Our framework allows us to define the semantics of partial facts and introduce the notion of the exact answer—that is the answer that includes partial facts. We show that querying under the exact answer semantics is compositional. We also present two methods for actually computing the exact answer. The first method is tableau-based, and it is a generalization of the “inverse-rules” approach. The second, much more efficient method, is a generalization of the rewriting approach, and it is based on partial containment mappings introduced in the paper.     

A simple algorithm for in-place merging

The early algorithms for in-place merging were mainly focused on the time complexity, whereas their structures themselves were ignored. Most of them therefore are elusive and of only theoretical significance. For this reason, the paper simplifies the unstable in-place merge by Geffert et al. [V. Geffert, J. Katajainen, T. Pasanen, Asymptotically efficient in-place merging, Theoret. Comput. Sci. 237 (2000) 159-181]. The simplified algorithm is simple yet practical, and has a small time complexity.