On the applicability of schema integration techniques to database interoperation

We discuss the applicability of schema integration techniques developed for tightly-coupled database interoperation to interoperation of databases stemming from different modelling contexts. We illustrate that in such an environment, it is typically quite difficult to infer the real-world semantics of remote classes from their definition in remote databases. However, defining relationships between the real-world semantics of schema elements is essential in existing schema integration techniques. We propose to base database interoperation in such environments on instance-level semantic relationships, to be defined using what we call object comparison rules. Both the local and the remote classifications of the appropriately merged instances are maintained, allowing for the derivation of a global class hierarchy if desired

The state-of-the-art in web-scale semantic information processing for cloud computing

Based on integrated infrastructure of resource sharing and computing in distributed environment, cloud computing involves the provision of dynamically scalable and provides virtualized resources as services over the Internet. These applications also bring a large scale heterogeneous and distributed information which pose a great challenge in terms of the semantic ambiguity. It is critical for application services in cloud computing environment to provide users intelligent service and precise information. Semantic information processing can help users deal with semantic ambiguity and information overload efficiently through appropriate semantic models and semantic information processing technology. The semantic information processing have been successfully employed in many fields such as the knowledge representation, natural language understanding, intelligent web search, etc. The purpose of this report is to give an overview of existing technologies for semantic information processing in cloud computing environment, to propose a research direction for addressing distributed semantic reasoning and parallel semantic computing by exploiting semantic information newly available in cloud computing environment.Comment: 20 page

Automated schema matching techniques: an exploratory study

Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made

Information Integration and Computational Logic

Information Integration is a young and exciting field with enormous research and commercial significance in the new world of the Information Society. It stands at the crossroad of Databases and Artificial Intelligence requiring novel techniques that bring together different methods from these fields. Information from disparate heterogeneous sources often with no a-priori common schema needs to be synthesized in a flexible, transparent and intelligent way in order to respond to the demands of a query thus enabling a more informed decision by the user or application program. The field although relatively young has already found many practical applications particularly for integrating information over the World Wide Web. This paper gives a brief introduction of the field highlighting some of the main current and future research issues and application areas. It attempts to evaluate the current and potential role of Computational Logic in this and suggests some of the problems where logic-based techniques could be used.Comment: 53 Page

Literature Review Of Attribute Level And Structure Level Data Linkage Techniques

Data Linkage is an important step that can provide valuable insights for evidence-based decision making, especially for crucial events. Performing sensible queries across heterogeneous databases containing millions of records is a complex task that requires a complete understanding of each contributing databases schema to define the structure of its information. The key aim is to approximate the structure and content of the induced data into a concise synopsis in order to extract and link meaningful data-driven facts. We identify such problems as four major research issues in Data Linkage: associated costs in pair-wise matching, record matching overheads, semantic flow of information restrictions, and single order classification limitations. In this paper, we give a literature review of research in Data Linkage. The purpose for this review is to establish a basic understanding of Data Linkage, and to discuss the background in the Data Linkage research domain. Particularly, we focus on the literature related to the recent advancements in Approximate Matching algorithms at Attribute Level and Structure Level. Their efficiency, functionality and limitations are critically analysed and open-ended problems have been exposed.Comment: 20 page

Using Methods of Declarative Logic Programming for Intelligent Information Agents

The search for information on the web is faced with several problems, which arise on the one hand from the vast number of available sources, and on the other hand from their heterogeneity. A promising approach is the use of multi-agent systems of information agents, which cooperatively solve advanced information-retrieval problems. This requires capabilities to address complex tasks, such as search and assessment of sources, query planning, information merging and fusion, dealing with incomplete information, and handling of inconsistency. In this paper, our interest is in the role which some methods from the field of declarative logic programming can play in the realization of reasoning capabilities for information agents. In particular, we are interested in how they can be used and further developed for the specific needs of this application domain. We review some existing systems and current projects, which address information-integration problems. We then focus on declarative knowledge-representation methods, and review and evaluate approaches from logic programming and nonmonotonic reasoning for information agents. We discuss advantages and drawbacks, and point out possible extensions and open issues.Comment: 66 pages, 1 figure, to be published in "Theory and Practice of Logic Programming

Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence

Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications. Since the overall human knowledge is innumerable that still grows explosively and changes frequently, knowledge construction and update inevitably involve automatic mechanisms with less human supervision, which usually bring in plenty of noises and conflicts to KGs. However, most conventional knowledge representation learning methods assume that all triple facts in existing KGs share the same significance without any noises. To address this problem, we propose a novel confidence-aware knowledge representation learning framework (CKRL), which detects possible noises in KGs while learning knowledge representations with confidence simultaneously. Specifically, we introduce the triple confidence to conventional translation-based methods for knowledge representation learning. To make triple confidence more flexible and universal, we only utilize the internal structural information in KGs, and propose three kinds of triple confidences considering both local and global structural information. In experiments, We evaluate our models on knowledge graph noise detection, knowledge graph completion and triple classification. Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.Comment: 8 page

Specifying global behaviour in database federations

We discuss the impact of locally implemented behaviour on global behaviour specification in a federation of object-oriented databases. In particular, given a specification of an integrated view of a number of component databases, we discuss the process of determining the global methods that are implicitly implemented by a given set of local methods on these component databases. To this end, we develop the notions of objectivity and subjectivity of local methods, indicating whether the execution of a local method affects the global view exactly as it affects the local database, behaviour equivalences between local methods, indicating whether local methods of different components have similar effect, and behaviour concurrences, indicating whether local methods respond to the same event. These notions can be used as a basis for tools supporting the engineering activity of specifying global behaviour in database federations