Effectiveness of Title-Search vs. Full-Text Search in the Web

Search engines sometimes apply the search on the full text of documents or web-pages; but sometimes they can apply the search on selected parts of the documents only, e.g. their titles. Full-text search may consume a lot of computing resources and time. It may be possible to save resources by applying the search on the titles of documents only, assuming that a title of a document provides a concise representation of its content. We tested this assumption using Google search engine. We ran search queries that have been defined by users, distinguishing between two types of queries/users: queries of users who are familiar with the area of the search, and queries of users who are not familiar with the area of the search. We found that searches which use titles provide similar and sometimes even (slightly) better results compared to searches which use the full-text. These results hold for both types of queries/users. Moreover, we found an advantage in title-search when searching in unfamiliar areas because the general terms used in queries in unfamiliar areas match better with general terms which tend to be used in document titles

Functional Analysis and Object-Oriented Design- A Hybrid Methodology

We propose a methodology for information systems analysis and design which is a hybrid of two main streams in software engineering, the functional (or process-oriented) approach and the object-oriented (OO) approach. System analysis, which aims at eliciting and defining user requirements, continues to be carried out in the functional approach, utilizing data flow diagrams (DFD). System design, which aims at designing the software, is carried out in the OO approach, yielding an object model that consists of an object schema and a behavior schema (i.e., methods and messages). The transition from the functional model (in the analysis stage) to the OO model (in the design stage) is enabled by the use of ADISSA methodology, which facilitates design of the object schema from DFD data stores, and design of the behavior schema from transactions, which by themselves are derived from the DFDs

Special Theme of Research in Information Systems Analysis and Design -II. Data Modeling or Functional Modeling - Which Comes First? An Experimental Comparison

The software analysis process consists of two main activities: data modeling and functional modeling. While traditional development methodologies usually emphasize functional modeling via dataflow diagrams (DFDs), object-oriented (OO) methodologies emphasize data modeling via class diagrams. UML includes techniques for both data and functional modeling which are used in different methodologies in different ways and orders. This article is concerned with the ordering of modeling activities in the analysis stage. The main issue we address is whether it is better to create a functional model first and then a data model, or vice versa. We conduct a comparative experiment in which the two opposing orders are examined. We use the FOOM methodology as a platform for the experiment as it enables the creation of both a data model (a class diagram) and a functional model (hierarchical OO-DFDs), which are synchronized. The results of the experiment show that an analysis process that begins with data modeling provides better specifications than one that begins with functional modeling

Ontology-based Classification of News in an Electronic Newspaper

This paper deals with the classification of news items in ePaper, a prototype system of a future personalized newspaper service on a mobile reading device. The ePaper system aggregates news items from various news providers and delivers to each subscribed user (reader) a personalized electronic newspaper, utilizing content-based and collaborative filtering methods. The ePaper can also provide users "standard" (i.e., not personalized) editions of selected newspapers, as well as browsing capabilities in the repository of news items. This paper concentrates on the automatic classification of incoming news using hierarchical news ontology. Based on this classification on one hand, and on the users' profiles on the other hand, the personalization engine of the system is able to provide a personalized paper to each user onto her mobile reading device

An Ontology- Content-based Filtering Method

Traditional content-based filtering methods usually utilize text extraction and classification techniques for building user profiles as well as for representations of contents, i.e. item profiles. These methods have some disadvantages e.g. mismatch between user profile terms and item profile terms, leading to low performance. Some of the disadvantages can be overcome by incorporating a common ontology which enables representing both the users' and the items' profiles with concepts taken from the same vocabulary. We propose a new content-based method for filtering and ranking the relevancy of items for users, which utilizes a hierarchical ontology. The method measures the similarity of the user's profile to the items' profiles, considering the existing of mutual concepts in the two profiles, as well as the existence of "related" concepts, according to their position in the ontology. The proposed filtering algorithm computes the similarity between the users' profiles and the items' profiles, and rank-orders the relevant items according to their relevancy to each user. The method is being implemented in ePaper, a personalized electronic newspaper project, utilizing a hierarchical ontology designed specifically for classification of News items. It can, however, be utilized in other domains and extended to other ontologies

Personalized Knowledge Service Based on Smart Cell-Phone Usage: A Conceptual Framework

Smart cell-phones include many advanced applications and services, which allow their users to achieve various useful goals. However, many users face difficulties when upgrading their cell-phones devices to more advanced ones, partially because their applications include more complex patterns of use for achieving the users’ goals. We present a conceptual framework that aims to help overcoming usage barrier by providing smart cell-phones’ users a personalized knowledge service. The framework is based on the utilization of task models and on the tracking and analyzing the usage of the applications included in the smart cell-phone that enable to construct users’ stereotypes and suggest personalized help according to their usage patterns. It is assumed that the system monitors the usage patterns of the user, thus enabling dynamic update of his/her belonging to a stereotype. The user can override the suggestions and navigate independently in order to find the required knowledge

Information Filtering and Automatic Keyword Identification by Artificial Neural Networks

Information filtering (IF) systems usually filter data items by correlating a vector of terms (keywords) that represent the user profile with similar vectors of terms that represent the data items (e.g. documents). The terms that represent the data items can be determined by (human) experts (e.g. authors of documents) or by automatic indexing methods. In this study we employ an artificial neural-network (ANN) as an alternative method for both filtering and term selection, and compare its effectiveness to “traditional” methods. In an earlier study we developed and examined the performance of an IF system that employed content-based and stereotypic rule-based filtering methods, in the domain of e-mail messages. In this study we train a large-scale ANN-based filter which uses meaningful terms in the same database of email messages as input, and use it to predict the relevancy of those messages. Results of the study reveal that the ANN prediction of relevancy is very good, compared to the prediction of the IF system: correlation between the ANN prediction and the users’ evaluation of message relevancy ranges between 0.76- 0.99, compared to correlation in the range of 0.41-0.77 for the IF system. Moreover, we found very low correlation between the terms in the user profile (which were selected by the users) and the positive causal-index terms of the ANN (which indicate the important terms that appear in the messages). This indicates that the users under-estimate the importance of some terms, failing to include them in their profiles. This may explain the rather low prediction accuracy of the IF system that is based on user-generated profiles

A Meta Knowledge Base and A Search Mechanism for Distributed, Heterogeneous Databases

This paper deals with the issue of accessing relevant information in a network that consists of heterogeneous databases of various types, e.g. structured databases as well as text, audio, and picture files, which reside in locations not necessarily known to the users. The objective is to construct a search mechanism to find the most relevant databases in the network with the help of a meta knowledge base (MKB). This is an ongoing research project, and the current paper provides only a general overview of the problem and the architecture of the proposed solution

Prototyping a microcomputer-based online library catalog

Bibliography: p. 42-47