Substantial work in knowledge engineering has focused on eliciting knowledge and representing it in a computational form. However, before elicited knowledge can be represented, it must be integrated and transformed so the knowledge engineer can understand it. This research identifies the need to separate knowledge representation into human comprehension and computational reasoning and shows that this will lead to better knowledge representation. Modeling of human comprehension is called conceptual knowledge representation. The Conceptual Knowledge Representation Scheme is developed and validated by conducting a combined qualitative/quantitative repeated-measures experiment comparing the Conceptual Knowledge Representation Scheme to two computation-oriented ones. The results demonstrate that the Conceptual Knowledge Representation Scheme better facilitates human comprehension than existing representation schemes. Four principles of the Conceptual Knowledge Representation Scheme emerge that help to attain effective knowledge representation. These are: (1) a focus on human comprehension only, (2) design around natural language, (3) addition of constructs common in the domain, and (4) constructs for representing abstract versions of detailed concepts.

Community Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this research, we address this collaborative QA task by drawing knowledge from the crowds in community QA portals such as Yahoo! Answers. Despite their popularity, it is well known that answers in community QA portals have unequal quality. We therefore propose a quality-aware framework to design methods that select answers from a community QA portal considering answer quality in addition to answer relevance. Besides using answer features for determining answer quality, we introduce several other quality-aware QA methods using answer quality derived from the expertise of answerers. Such expertise can be question independent or question dependent. We evaluate our proposed methods using a database of 95K questions and 537K answers obtained from Yahoo! Answers. Our experiments have shown that answer quality can improve QA performance significantly. Furthermore, question dependent expertise based methods are shown to outperform methods using answer features only. It is also found that there are also good answers not among the best answers identified by Yahoo! Answers users.

Abstract Web pages from a Web site can often be associated with concepts in an ontology, and pairs of Web pages also can be associated with relationships between concepts. With such associations, the Web site can be searched, browsed, or even reorganized based on the concept and relationship labels of its Web pages. In this article, we study the link chain extraction problem that is critical to the extraction of Web pages that are related. A link chain is an ordered list of anchor elements linking two Web pages related by some semantic relationship. We propose a link chain extraction method that derives extraction rules for identifying the anchor elements forming the link chains. We applied the proposed method to two well‐structured Web sites and found that its performance in terms of precision and recall is good, even with a small number of training examples.

In today’s dynamic and changing environment, companies have a strong need to create or sustain their competitive advantages. In order to be competitive, companies need to be responsive and closer to the customers, and deliver value‐added products and services as quickly as possible. Companies also need to be able to support organizational information needs faster and better than their competitors. These goals can be realized by applying two emerging information technologies: enterprise resource planning (ERP) supporting business process integration; and data warehousing supporting data integration. Companies with the further integration of ERP and data warehousing will have great advantages in the competitive environment. Two cases have been studied and presented to illustrate its values.

While much research has been devoted to data modeling, little attention has been paid to developing constructs for modeling micro-objects, i.e. constructs for modeling low dimension objects such as the attribute Birth_Date and the data type Boolean. Most data models consider attributes as functions of macro-objects (i.e. objects formed from constructs such as entity sets, relations, and object sets). For example, the attribute Birth_Date is often modeled as a function of the entity set Person. This research proposes a Conceptual Micro-Object Model (CMoM), which considers the attribute as the foundation of data modeling. Other constructs such as the Conceptual Data Type Primitive (CDTP) and Attribute Group (AG) are developed to model the constituent components of attributes and objects that can be formed from attributes respectively. CMoM is useful for modeling intricate micro-objects such as dates. It also allows one to identify and resolve redundancy between conceptual macro-objects such as those modeled as entity sets and relations. For example, redundancies between attributes in different relations can be detected. Finally, it facilitates a formal foundation for defining object-oriented concepts such as inheritance, aggregation, and encapsulation.

Terms such as `data processing system', `management information system', `decision support system', `expert system' and `executive support system' are widely used. However, there appears to be no generally accepted way to distinguish among these classes of information systems due to the vagueness that has existed in traditional classification schemes of information systems. The purpose of this research is to establish an analytical framework based on a theoretical foundation, called Semiotics (the theory of signs) to understand, classify and compare information systems of various generations. Such a framework should simplify subsequent discussions and considerations in building new generations of information systems with complicated functions and capabilities. The proposed semiotics framework consists of 10 features that can be used to characterize the relationships between an information system and its users, and represent and organize the system's contents. The framework is applied to analyze and compare the semiotics features of information systems. Its relevance to system development, particularly system planning and requirements determination, is also discussed.

Automated database design systems embody knowledge about the database design process. However, their lack of knowledge about the domains for which databases are being developed significantly limits their usefulness. A methodology for acquiring and using general world knowledge about business for database design has been developed and implemented in a system called the Common Sense Business Reasoner, which acquires facts about application domains and organizes them into a a hierarchical, context-dependent knowledge base. This knowledge is used to make intelligent suggestions to a user about the entities, attributes, and relationships to include in a database design. A distance function approach is employed for integrating specific facts, obtained from individual design sessions, into the knowledge base (learning) and for applying the knowledge to subsequent design problems (reasoning).

It is often difficult to obtain a good conceptual understanding of a legacy database, especially when there is a lack of documentation. Database reverse engineering attempts to provide solutions for this problem. It is the part of system maintenance work that produces a sufficient understanding of a legacy database and its application domain to allow appropriate changes to be made. However, research on database reverse engineering has largely ignored design and evaluation issues of their methods (i.e., foundations and processes). This research proposes a framework for the design and evaluation of reverse engineering methods of relational databases. This framework consists of eight criteria: 1) the situation chosen as the basis for reverse engineering, 2) the conceptual model chosen to represent the reverse engineering results, 3) the prerequisites of the database to be reverse engineered, 4) the thoroughness of domain semantics acquisition, 5) rules and heuristics employed by the reverse engineering process, 6) performance efficiency of the reverse engineering process, 7) completeness and robustness and 8) validation. These criteria are important to be considered in designing reverse engineering methods, so that they can perform reverse engineering for a broad range of legacy databases at a high level of automation and provide a conceptual schema that is semantically rich and correct.