10 Evolutions in Information Retrieval
Biswanath Dutta
I. Objectives
• To study the evolutions that has taken place in information retrieval field.
• The evolutions are illustrated by describing the standards and protocols and by discussing the global initiatives and the researches.
II. Learning Outcomes
After going through this module the students:
• Will know about the various Information Retrieval (IR) standards and protocols.
• Will know about the intelligent software tools in IR and their applications in LIS.
• Will know about the global initiatives and researches in IR.
• Will know about user interface design, design strategies and issues involved in it.
III. Structure
1. Introduction
2. Information Retrieval Standards and Protocols 2.1 Z39.50
2.1.1 How does Z39.50 Works?
2.2 SRW
2.3 SRU
2.4 CQL
3. Global Digital Library
3.1 Paradigm Shift towards a Global Learned Society
3.2 Challenges
3.3 Obstacles to Universal Access
4. Intelligent Information Retrieval
4.1 Expert System (ES)
4.2 Expert System for Library Professionals
4.2.1 Expert System in Cataloguing
4.2.2 Expert System in Classification
4.2.3 Expert System in Document Delivery
4.2.4 Expert System in Abstracting
5. Hypertext and Hypermedia Systems
5.1 Hypertext
5.2 Hypermedia
5.3 Information Retrieval Based on Hypertext and Hypermedia
6. User Interface
6.1 IR as a Problem Solving Process
6.2 Issue of Vocabulary
6.3 Interfaces for Retrieval Systems
6.4 Interface Design Strategies
7. Summary
8. References
1. Introduction
The growth of Information and Communication Technology (ICT) has influenced the way information is being searched and retrieved. It has brought revolution in IR. There are several advancements that have taken place in this area over the period of time.
In this module, we discuss some of the IR techniques and technologies that evolved in the recent past. We discuss some of the significant IR standards and protocols such as Z39.50, SRW/SRU, and CQL. We also report the state-of-the-art research in IR field, for instance, the initiative of the global digital library, application of intelligent systems like an expert system in library cataloguing, classification and abstracting, the application and the issues of intelligent hypertext and hypermedia systems, and the research on human-computer interaction.
2. Information Retrieval Standards and Protocols
A standard means an agreement by what way to perform a task or carry out some activity to obtain a predictable result [6]. All standards available by the National Information Standards Organization (NISO), U.S.A are established by an agreement, which is based on the expertise of designers, application developers and vendors, and product users. All the standards available by NISO are approved by the American National Standards Institute (ANSI). There are various standards and protocols exist today for IR systems. In the following sections some of the very popular search and retrieval standards and protocols, such as, Z39.50 [1], CQL [4], SRW [3], and SRU [2] are discussed.
2.1 Z39.50
Z39.50 is used both at the national and international level as a standard protocol that defines computer-to-computer information retrieval technique. It is a non-proprietary and vendor- independent. Z39.50 was originally approved by the National Information Standards Organization (NISO) in 1988. In 1998, International Organization for Standardization (ISO) adopted Z39.50 and issued ISO 23950 Information and documentation – Information retrieval (Z39.50) [6]. Using Z39.50 a user through his/her system can search and retrieve information from other Z39.50 compliant computer systems without having the prior idea about the syntax of search that is used by the other systems. The primary goal of Z39.50 is to reduce the complexity and difficulties involved in searching and retrieving electronic information [6]. Z39.50 makes the life of the end-users simple to search and use the wealth of information available on the Internet. In Z39.50 enabled system environment, when a user of one system search for an information in another system, he does not need to know how the other system works.
Z39.50 operates in a client/server architecture. The protocol acts as a common language that all Z39.50-enabled systems can understand. It is like the Esperanto language, which bridges several “languages and dialects” that various information systems “speak” [6]. The communication and interoperation for Z39.50 take place both at the client and the server, hence, they must be able to speak the same Z39.50 language. TCP/IP [57] Internet communications protocol is used as a standard by almost all Z39.50 implementations to connect the systems and compliant software of Z39.50 to translate between them for searching and retrieval of information.
Since all the associated technical activities occur behind the scenes, the users only see their familiar search and display interface. Z39.50 standardizes the messages used by clients and servers for communication, regardless of what software, platform or systems are used for achieving the interoperability [6]. A Z39.50 enabled client system can communicate with diverse servers. Similarly, a Z39.50 enabled server is searchable by client systems developed by different vendors.
2.1.1 How does Z39.50 Works?
As stated above, a user on a client system can search through Z39.50 enabled interface without knowing how a server system works. Z39.50 governs the entire process of how a client translates the query into a standard format to send to a server [6]. After receiving the query, the server applies the Z39.50 rules to translate the query into a format that the local database understands, performs the search and sends the result to the client system. After receiving the result, the interface software at the client processes the results returned through Z39.50 with the goal of displaying them as close as possible to the way records are displayed in the user’s native system [6].
2.2 SRW
SRW stands for Search/Retrieve Web Service protocol [3]. Its aim is to minimize the cross- language problems. The goal is to allow access to several networked resources and support interoperability among distributed databases, using a common utilization framework [3]. It is developed by collective implementers with more than 20 years of experience of the Z39.50 Information Retrieval protocol with nascent developments in the technological arena of the web.
SRW provides both Simple Object Access Protocol (SOAP) [36] and URL-based access mechanisms to a wide range of possible clients from Microsoft’s .Net initiative to simple Extensible Stylesheet Language Transformations (XSLT) [35] and JavaScript transformations. This influences the Contextual Query Language (CQL) (discussed below) that provides an expressive but intuitive ways to search formulation [3]. SRW directs the usage of open and industry-supported standards like eXtensible Markup Language (XML) and XML Schema, and where desired, SOAP and XPath [3].
SRW provides semantics in search of databases having metadata and objects, both text and non- text [3]. As SRW has been developed on Z39.50 semantics, this makes easier for existing Z39.50 systems decreasing the barriers to new information providers, to enable their contents available via a standard search and retrieve mechanism. SRW defines web service incorporating several Z39.50 features, most notably, the Search, Present and Sort Services [3].
2.3 SRU
SRU stands for Search/Retrieve via URL. It is a standard XML-based protocol for search by utilizing CQL (http://www.loc.gov/cql/), a standard syntax for query representation [3]. The prime difference between SRU and SRW is that the former uses HTTP as the transport mechanism and the latter is based on SOAP protocol and uses XML streams for both the query and the results. This depicts that the query is communicated as a URL and the XML is received as if it were a web page. POST method an alternate way for using the HTPP transport technique is not acceptable in SRU protocol. The advantage is that a wide variety of transport mechanisms can be used in this case for instance e-mail.
2.4 CQL
CQL stands for Contextual Query Language (formerly known as, Common Query Language, http://www.loc.gov/cql/)). It is designed for use with SRW which is a search protocol successor to Z39.50 (as discussed in the previous section). CQL is an abstract and extensible query language for maximum interoperability amongst the connected systems. The goal is to reduce the difficulty to learn and use while retaining the capability to allow complex searches. Primarily CQL is used in the bibliographic domain, but it is not restricted to this context alone. CQL provides standards and tested mechanism to specify a query to select records from a database that may be used either internally or remotely [3]. The bibliographic database of Library of Congress has an SRW/CQL interface available for all 28 million records. CQL can also be used in OpenOffice to identify, locate and integrate a huge amount of data within the application [3].
3. Global Digital Library
With the rise in the worldwide use of the open systems such as the Internet and World Wide Web (WWW) has enabled us to experience several real world entities in cyberspace like “virtual libraries” [42]. It is the role of librarians to take active participation and work together for reducing the issues and problems related to the information framework, which has a much global presence.
Global Digital Library (GDL) [42] is a prototype which aims to connect several national libraries and some major libraries, museums, archives, and information organizations with each other. Undoubtedly, there exists a need for cooperation globally in the field of building “digital” knowledge and sharing them in this digital information age.
3.1 Paradigm Shift towards a Global Learned Society
The growth of information and communication technologies, there is an increasing need to have access to information globally in order to have a much finer and better picture of the society in which we are living [42]. We are more curious about knowing our culture, our surroundings, our history, our economy, our growth in science and technology, etc. Nowadays, information has become a key to productivity and with the progress in technology, economic progress, and societal change, libraries have new challenges to face. This change wants our libraries to not only provide or satisfy the information needs of users who visit library but also to provide access to services and information resources to the users not present physically in the library i.e. user at home, at work, in school, or in any place where they need them [12].
3.2 Challenges
In the last few years, there is a rapid growth in the use of Internet for uploading digital contents on the World Wide Web (WWW) needed for non-commercial and commercial purposes. Nowadays it is a matter of a few seconds for us to write, talk, confer with, or send textual, audio and visual content to desired person in any part of the world [42]. The pattern for information seeking and use of a library and other information services and their delivery has changed dramatically. With the physical library, digital library has also become a reality. Now it is essential to share information physically as well as over the cyberspace to satisfy the need for both types of users.
3.3 Obstacles to Universal Access
There exist a lot of hurdles and issues related to the information infrastructure. Some of these difficulties are [7][40][41][42]:
• Several legal issues may arise related to intellectual property, copyright, confidentiality and privacy, security, personal, business equity, etc.;
• Difference in culture may influence the way of information communication;
• The presence of generational gaps;
• The sheer complexity of information architecture both at the global and national level;
• To have an effective and adequate inventory of available resources comprising the knowledge of information;
• The ability to locate, identify and retrieve relevant and quality information;
• Due to the huge amount of information, the complexity arises related to “undesirable” “indecent” information.
Irrespective of these difficulties and unsolved issues, it is expected that the relevant technologies will soon be available which will enable us to link all the global information to form “The Global Library” and delivery of multimedia information [12]. Note that in the recent time we have seen various initiatives of digital libraries (mostly in terms of institutional repositories), but they are mostly dispersed in nature. What is still missing, or what should be done is the global initiative by the major libraries and information organizations and works together towards finding the solutions for the above difficulties and issues. Hence, to build a true “The Global Library”, an effective, substantive and higher level of cooperation between the library and information leaders is needed both at the global and national level [42].
4. Intelligent Information Retrieval
Intelligent information retrieval, as defined by Sparck Jones [43], is a computer system having the capability to infer knowledge with the help of its previous knowledge for establishing a link between the requirement of its user and a set of candidate document. This is a system which can perform intelligent retrieval. The realization of researchers to use knowledge in the information retrieval system has led them to think about the artificial intelligent system which also has the similar purpose, and one among these classes is an expert system.
4.1 Expert System(ES)
As Peter [58] stated in artificial intelligence, an expert system is “a computer system which emulates the decision-making ability of human experts.” The expert systems are designed to solve complex problems by reasoning over knowledge stored in a knowledge base. The knowledge in the knowledge base is primarily represented as IF-THEN rules rather than conventional procedural code [59]. The first expert systems were invented in the 1970s and then proliferated in the 1980s [44] [45] [46]. The expert systems were the first among the true realization of Artificial Intelligence software.
An expert system has two primary components: inference engine and knowledge base [47] [44]. A knowledge base consisted of rules and facts, i.e., the knowledge about the real world objects. Inference engine applies these rules to the known facts for deducing new facts or knowledge. Inference engines can debug and can also provide the explanations for the deduced knowledge. The knowledge bases are designed in a similar fashion like the object oriented programming and stores the knowledge in a structured form. The knowledge in a knowledge base is structured in a form of classes, subclasses, and instance.
As expert systems evolved, several new techniques were adopted into various types of inference, engines. Some of the most important of these, as mentioned by Mettrey are [48]:
• Truth Maintenance: These systems record the dependencies in a knowledge base, so that when facts are changed the dependent knowledge also get altered accordingly. For example, if the system learns that Aristotle is no longer known to be a man, it will repeal the assertion that Aristotle is mortal.
• Hypothetical Reasoning: In hypothetical reasoning, the knowledge base can be sub- divided up into several possible views, or worlds. This enables the inference engine to explore several possibilities in parallel. In case of the previous example, the system may want to find the consequences of both the assertions, what will be true if Aristotle is a Man and what will be true if he is not?
• Fuzzy Logic: This was one of the first extension of the simply using rules for representing knowledge. The main idea behind fuzzy logic is to associate the probability with each rule. Hence, not to assert that Aristotle is mortal, instead to assert that Aristotle may be mortal with some probability value.
• Ontology Classification: With the introduction of object classes in the knowledge bases, a new kind of reasoning is enabled. With this addition of object classes, it is possible now to reason about the structure of the objects, instead of just simply reasoning the value of the objects. In the case of the above example, Man can be represented as an object class and R1 can be redefined as a rule which defines the class of all men. These types of specific inference engines are known as classifiers. As Mettrey stated [48], classifiers are very effective for unstructured volatile domains and a key technology for the Internet and the emerging Semantic Web [60].
4.2 Expert System for Library Professionals
Expert systems can be used to emulate the jobs of library professionals in a library. They can be applied where the intelligent activities are involved and generally carried by the library experts. Note that primarily the expert systems have a more specific goal to achieve than an information system. Expert systems are to make decisions, and not just to produce reports [16, p.91]. In this section, we discuss some of the significant expert systems specifically built for libraries.
4.2.1 Expert System in Cataloguing
A system called AUTOCAT [20] was produced in Germany. The system was designed to generate bibliographic records of physical sciences periodicals available in machine-readable form. Another significant work was done by Weibel, Oskins and Vizine-Goetz [21] at OCLC. They built a prototype based on rules known as “the OCLC Automated Title Page Cataloguing Project.” The tool was designed to prepare descriptive cataloguing from the title pages. Another important project, namely, Qualcat (Quality Control in Cataloguing) was undertaken at the University of Bradford. The goals of the project were to develop expert systems to select the best records, to link the databases and centralized authority control, to build a fully automated control package for day to day running, and to investigate interface problems for cataloguing [61].
4.2.2 Expert System in Classification
Classification is a difficult task to accomplish using an expert system. This is even true for a human expert. The main problem is although the schedules are available to determine subjects and class numbers, the relationships between the objects (here, the documents) and classes are often not explicit. There are some expert systems that have been developed on item, patent and book classification, for instance, by Sharif [22]; Cosgrove and Weimann [49]; Valkonen and Nykanen [50]; Gopinath and Prasad [23].
In 1986, Paul Burton conducted an exploratory research at the University of Strathclyde, United Kingdom. The aims were to assess the merits of different ways of knowledge representation and suitability of expert systems in classification [39, p.64]. As an outcome of the experiment, a prototype expert system was designed. The system was able to provide the Dewey class number based on the information provided by the users. In another research, OCLC developed an expert system, called Cataloguer’s Assistant. The system was tested in Carnegie-Mellon University to reclassify the mathematics and computer science collection [39, p.64].
4.2.3 Expert System in Document Delivery
There are very few references available related to expert systems in document delivery [39]. Brown [51] explained the use of expert system technologies in equipment division of Raytheon Company’s for coordinating requests of specifications and standards documents with purchases made via the acquisitions unit. Abate [52] reported about an ES in the library of a law firm which was developed for delivery of document in decision making using the ES shell and VP-Expert.
4.2.4 Expert Systemin Abstracting
The researches on abstracting have been primarily focused on abstracting the scientific articles from journals and conference proceedings. The first reported work on automatic abstracting was in 1958 by H.P Luhn. There are few other works also have taken place on automatic abstracting, for instance, DeJong [53], Lebowitz [54], and Husk [55]. DeJong developed the system known as FRUMP which analyses articles from newspapers using frame-based techniques. The articles were first scanned and then data were automatically fed into the different slots within frames. Scripts were then executed to generate summaries of the information held in the relevant frames. Besides generating abstract for scientific articles, the research on abstracting also extended to other kind of materials. For instance, Rau, Jacobs and Zernik (1989) [18] developed a system known as SCISOR that generate reports on corporate acquisitions and mergers.
Besides the above areas, expert systems have been developed in many other areas of LIS. For instance, expert system in acquisition, in collection development and in indexing. Expert systems have been also used as an intelligent intermediaries for database selection, for query formulation, and so forth. For further details on expert systems in various areas of LIS, students are suggested to go through the review article [39]. It worth to mention here that a very few expert systems are reportedly operating in practice in the various areas of LIS. Some systems have progressed commercially but have later failed and been withdrawn from the market (e.g., Tome Searcher and Tome Selector).
5. Hypertext and Hypermedia Systems
5.1 Hypertext
Hypertext is a text which is displayed on a computer screen or other digital device with references (hyperlinks) to other text that a reader can access, or where text can be followed progressively at multiple levels of detail [62]. WordNet 2.1 (https://wordnet.princeton.edu/) defines hypertext as a “machine-readable text that is not sequential but is organized so that related items of information are connected.” The hypertext words connected through hyperlinks can be clicked by a mouse or by touching the screens. Apart from linking between the texts, the linking can be created between pictures, between tables or any other presentational content forms using the hyperlinks. Web Pages in World Wide Web are mostly written in Hypertext Markup Language (HTML). This provides an efficient, flexible connection and sharing of information on the Internet. Hypertext documents are of two types either static (i.e. prepared and stored in advance) or dynamic (continually changing according to the response of user’s input) [62]. Static hypertext is used for the cross-references of a collection of data in documents, books on CDs/DVDs, and the web pages, etc. The most significant and popular implementation of hypertext is the World Wide Web. In 1963, Ted Nelson coined the word “hypertext.”
5.2 Hypermedia
Hypermedia, a logical extension of hypertext, is a non-linear medium of information space which includes plain text, audio, video, graphics and hyperlinks link [63]. Hypermedia contrasts with its broader term multimedia (a content consists of varieties of content, such as, text, images, animation, and video), which can be deployed to describe non-interactive linear presentations along with hypermedia. The term was coined by Ted Nelson in 1965. The World Wide Web is a classic example of hypermedia, whereas a non-interactive cinema presentation is an example of standard multimedia. It is non-interactive due to the lack of hyperlinks.
5.3 Information Retrieval Based on Hypertext and Hypermedia
The research on online search is focused on designing systems that would assist the professional intermediaries to retrieve a smaller set of result from a relatively larger set of records, e.g., scholarly journal abstracts, library catalogue records, etc. [64, p.105]. The focus is primarily on designing systems that would help or replace the professional intermediaries. Professional online searchers perform the search in a systematic manner. They clarify the search queries with the users before they provide the result. To search, the professionals in advance plans the search, consult the thesauri, and combine the terms using the logical operators (AND, OR, NOT) and also by adjusting proximity limits (the set of words within which query terms must co-occur) and scoping limits (the set of documents over which search takes place) [64, p.105].
Today’s electronic retrieval systems were mainly designed to replace the professional intermediaries or to emulate their performance. The systems focused, on indexing and cross- referencing for the organization and retrieval of the resources, instead on meaning, readability and understanding of information. Hence, the systems designed for end users must follow this philosophy and also constitute the suitable information seeking strategies [64].
Hypertext systems have the difference with respect to the present online retrieval systems. The online retrieval systems encourage the personalized, informal and content-oriented information- seeking strategies. In Hypertext system users can provide information during the retrieval process with the help of getting the context, and during browsing by saving, connecting, or transferring images or text [64, p.106]. Further, the current research trend is to support end users by providing flexible and powerful interfaces between the people (the end user) and computer. The idea is that the smart interface would be able to balance the end user browsing patterns using efficient analytical techniques similar like those used by the professional intermediaries.
In case of Hypermedia systems, they exhibit various types of relationships among the information elements. Typical examples of similarity relationships include similarity in meaning, similarity in logical sequence and temporal sequence, containment, etc. [65] [66]. Hypermedia allows these relationships to be used as links which as a result enables content navigation within the information space. Based on this, we can also build the taxonomies of links, which we can further discuss and analyze how best they are utilized. One possible example of a taxonomy could be based on mechanics of links (i.e., single source with single-destination, multiple-source with single-destination), the directionality of connections (i.e., unidirectional, bidirectional), and the mechanism for anchoring (i.e., generic links, dynamic links). Another alternative taxonomy example could be based on types of information relationships represented, in particular related to the organization of information space (e.g., structural links), related to the content of information space (e.g., associative and referential links) [65][66].
6. User Interface
In present era as the digital repositories are growing in terms of volume as well the diversified information content, the need for effective information retrieval systems are becoming increasingly high. In this section, we focus on an important component “interface” of an information retrieval system. We discuss the issues related to using and designing effective interfaces.
6.1 IR as a Problem Solving Process
Henninger, S. and Belkin [67] have divided the field of information retrieval into two categories: system-based and user-based. According to them, the system-based IR is concerned with the efficient search techniques for matching with the query and document representations. While the user-based IR is concerned with the cognitive state of the searcher and the context in which a problem is to be solved.
Generally speaking, an user of an IR system feel lonely or seek help whenever they perceive that they lack some knowledge to perform a task or to solve information search problem. This happen in particular when an user is searching for something which s/he knows little or nothing. In this context, it is expected that an IR not only just retrieve the information, but also help the users to describe and formulate the query. The idea is the system not only provides good query language, but also need to support an interactive dialogue model [67]. An iterative interface can interact with the users like the way professional intermediaries does and can understand and render help in solving their search problems.
6.2 Issue of Vocabulary
It is a very common and well-known problem of IR. It is quite often observed that even though the information is available in a repository, the users do not find it. It is due to the missing link between user’s search term and the IR defined terms, which are mostly assigned by the professionals. Users use multiple terms to refer to the same thing. So, unless there is match between the user search term and the IR defined terms, the result is going to be null. Also the situation becomes further worst when there is a mismatch between the ways a user characterize an object and the way a professional sees it. In most of the repositories, professionals describe the resources based upon the properties of the objects, which are mostly the inherent properties. Unless the users are also able to perceive and characterize in the same way, the query is likely to fail. It is noted in [67] that users look for information that is used for something and therefore for them how an object is used has greater relevance than its inherent properties.
6.3 Interfaces for Retrieval Systems
Current IR systems have addressed these some of inherent properties of information seeking strategies and indexing in various ways. Browsing has been placed to facilitate the iterative and ill-defined information seeking strategies [67]. A mixed approach including the support for information seeking strategies, such as, browsing and direct query facility, information visualization, feedback mechanism, etc. can be employed in designing an effective and efficient IR interface.
6.4 Interface Design Strategies
The design strategies of information retrieval systems must not only address problems related to look-and-feel, but it should also address the issues of ill-defined information seeking strategy including others. Besides designing an attractive and presentable interface (i.e., good look-and- feel), an interactive interface design is also crucial. Dialogue models based on relevant feedback and reformulation of query addressing the ill-defined nature of information seeking would enable users to learn from the repository and refine their information need. The IR systems need to have support for number of interaction patterns, such as making query and browsing for satisfying various kinds of search techniques users may need to use [67].
7. Summary
In this chapter we have discussed some of the IR techniques and technologies that evolved in the recent past. We have discussed some of the significant IR standards and protocols. We have also reported the state-of-the-art research in IR field, for instance, the initiative of global digital library, application of intelligent systems like expert system in library cataloguing, classification and abstracting, the application and issues of intelligent hypertext and hypermedia systems, and the research on human computer interaction.
8. References
1. The Z39.50 Information Retrieval Standard. Retrieved from http://www.dlib.org/dlib/april97/04lynch.html. Accessed on Sep. 20, 2014.
2. SRU: Search/Retrieve via URL. Retrieved from http://www.loc.gov/standards/sru/companionSpecs/srw.html. Accessed on Sep. 20, 2014.
3. Apache OpenOffice: The Free and Open Productivity Suite. Retrieved from http://www.openoffice.org/bibliographic/srw.html. Accessed on Sep. 20, 2014.
4. The Contextual Query Language. Retrieved from http://www.loc.gov/standards/sru/cql.
Accessed on Sep. 20, 2014.
5. Buntine. W., Taylor, M. P., & Lagunas, F. (2006). Standards for Open Source Information Retrieval, In Proceedings of the Open Source Information Retrieval Workshop (OSIR).
6. Z39.50 A Primer on the Protocol. Retrieved from www.niso.org/publications/press/Z3950_primer.pdf. Accessed on Sep. 20, 2014.
7. Alexandria Declaration of Principles (prepared by Robert M. Hayes and Ching-chih Chen) (1995). Microcomputers for Information Management, 12 (1-2), 3-8. Also In Planning Global Information Infrastructure, ed. by Ching-chih Chen. Norwood, pp. 1-6.
8. Bobinha, José Luis B. and Delgado, José Carlos M. (1996, November). Internet and the New Library. In Proceedings of NIT ’96: The 9th International Conference on New Information Technology, November 11-14, 1996, Pretoria, South Africa. Ed. by Ching- chih Chen. Newton, MA: MicroUse Information. pp. 1-12.
9. Chen, Ching-chih. (1986, December). Libraries in the information age: Where are the microcomputer and laser optical disc technologies taking us? Microcomputers for Information Management, 3 (4), 253-266.
10. Chen, Ching-chih. (1990, December). The challenge to library and information professionals in the visual information age. Microcomputers for Information Management, 7 (4), 255-272.
11. Chen, Ching-chih. (1993, November). Thriving in the digital communications environment: PROJECT EMPEROR-I’s experience — From print to multimedia, analog to digital. In Proceedings of NIT ’93: The Sixth International Conference on New Information Technology (PP. 59-68). West Newton: MicroUse Information. pp. 59-68.
12. Chen, Ching-chih. (1994, September). Information superhighway and the digital global library: Realities and challenges. Microcomputers for Information Management, 11 (3), 143-156.
13. Lynch & Garcia-Molina. (1996). Interoperability, scaling, and the digital libraries research agenda. Microcomputers for Information Management, 13 (2), 85-132.
14. Negrosponte, Nicholas. (1996, May). Caught browsing again. Wired, p. 200.
15. Ziegler, Bart. (1994, May 18). Building the highway: New obstacles, new solutions. The Wall Street Journal, pp. B1, B3.
16. Schoech, D. (1999). Human services technology: understanding, designing and implementing compuer and internet applications in the social services (2nd ed.). Haworth, New York.
17. Morris, Anne. (ed.) 1992. The Application of Expert Systems in Libraries and Infor- mation Centres. London : Bowker-Saur.
18. Rau, L. F.; P.S. Jacobs and U. Zernik. 1989. SCISSOR : information extraction and text summarization using linguistic knowledge acquisition. Information Processing and Management, Vol.25 no.4: 419 – 428.
19. Richardson, John. 1989. Towards an expert system for reference service: a research agenda for the 1990. College and Re-search Libraries, Vol.50 no.2: 230 – 248.
20. Endres-Niggemeyer, B. and G. Knorz. 1987. AUTOCAT: knowledge-based descriptive cataloguing of articles published in scien-tific journals. Knowledge Based Systems. Munich, October 20 – 21, 1987.
21. Weibel, Stuart; M. Oskins and Diane Vizine-Goetz. 1989. Automatic title page cataloguing: a feasibility study. Information Processing and Management, Vol.25 no.2: 187-203.
22. Sharif, Carolyn A. Y. 1988. Developing an expert system for classification of books using micro-based expert systems shells. British Library Research Paper 32.
23. Gopinath, M. A. and A.R.D. Prasad. 1994. A knowledge representation model for Ana- lytico-Synthetic classification. In: Know-ledge Organization and Quality Manage-ment. Proceedings of the 3rd ISKO Con-ference; 20-24 June 1994; Copenhagen, Denmark. pp. 320 – 327
24. T.H. Nelson, Literary Machines, Swarthmore, Penn., 1981. Available from Nelson.
25. Conklin, “Hypertext: An Introduction and Survey,” Computer, Sept. 1987, pp.
26. D. Goodman, “The Two Faces of Hyper- Card,” Macworld, Oct. 1987, pp. 122-129.
27. B. Shneiderman, Designing the User Interface: Strategies for Effective Human Computer Interaction, Addison-Wesley, Reading, Mass., 1987.
28. Belkin, N.J., Oddy, R.N, Brooks, H.M. ASK for Information Retrieval: Parts I&2, Journal of Documentation, 38(2,3), 1982, pp. 61-71; 145-164.
29. Card, S.K., Robertson, G.G. Mackinlay, J.D. The Information Visualizer, an Information Workspace, Human Factors in Computing Systems: CHI Ô91 Proceedings, ACM, 1991, pp.181- 194.
30. Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T. The Vocabulary Problem in Human- System Communication, Communications of the ACM, 30(11), 1987, pp. 964- 971.
31. Kwasnik, B.H. How a Personal Document’s Intended Use or Purpose Affects Its Classification in an Office, Proceedings SIGIR ’89, ACM, 1989, pp. 207-210.
32. Thompson, R.H., Croft, W.B. Support for Browsing in an Intelligent Text Retrieval System, International Journal of Man-Machine Studies, 30, 1989, pp. 639-668.
33. Winograd, T, Flores, F. Understanding Computers and Cognition: A New Foundation for Design. Ablex, 1986.
34. Chowdhury, G. C. (1999). Introduction to Modern Information Retrieval. Library Association Publishing.
35. XSLT. Version 1.0. http://www.w3.org/TR/xslt
36. Simple Object Access Protocol (SOAP) 1.1, Don Box, David Ehnebuske, Gopal Kakivaya, Andrew Layman, Noah Mendelsohn, Henrik Nielsen, Satish Thatte, Dave Winer, Editors. DevelopMentor, IBM, Microsoft, Lotus Development Corp., UserLand Software, Inc., 30 July 2003. This version is http://www.w3.org/TR/2000/NOTE-SOAP- 20000508/.
37. MindManager. http://www.mindjet.com/
38. IdeaFisher. http://www.innovationmanagement.se/imtool-articles/ideafisher-pro-the- perfect-catalyst-for-your-mind/
39. Silva, Sharon Manel De. (1997). A review of expert systems in library and information science. Malaysian Jour. of Lib. & Inf. Sc., vol. 2, no. 2, pp. 57-92.
40. Chen, Ching-chih, ed. (1995a). Planning Global Information Infrastructure. Norwood, NJ: Ablex. 518 p.
41. Chen, Ching-chih. (1995b). Digital global cultural and heritage information network: Personal viewpoint. In Planning Global Information Infrastructure, (pp. 181-185). Ching- chih Chen (Ed.). Norwood, NJ: Ablex. pp. 167-180.
42. Chen, Ching-chih. “Global digital library: Technology is ready, how about content?.” Proceedings of NIT’96, Pretoria, South Africa (1996): 41-56.
43. Sparck Jones, K., ‘Intelligent retrieval’. In: Jones, K. P. (ed.), Intelligent information retrieval: Informatics 7, London, Aslib, 1983, 136-42.
44. Zahendi, F., Intelligent systems for business expert systems with neural networks, Boston, MA, Wadsworth Publishing, 1993.
45. Ford, N., Experts systems and artificial intelligence: an information manager’s guide, London Association Publishing, 1991.
46. Morris, A., ‘Expert systems for library and information services: a review’, Information processing and management, 2 (6), 1991, 713-24.
47. Brooks, H. M., ‘ Expert systems and intelligent information retrieval’, Information processing and management, 23 (4), 1987, 367-82.
48. Mettrey, William (1987). An Assessment of Tools for Building Large Knowledge- BasedSystems. AI Magazine 8 (4).
49. S.J. Cosgrove, J.M. Weimann, (1992) “Expert system technology applied to item classification”, Library Hi Tech, Vol. 10 Iss: 1/2, pp.33 – 40.
50. Valkonen, P. and Nykanen, O. (1991), “An expert system for patent classification”, World Patent Information, Vol. 13 No. 3, pp. 143 ‐8.
51. Brown, Lynne C. Branche. 1993b. Expert sys-tems and document delivery in an automa- ted acquisitions environment. Library Ac-quisitions: Practice & Theory, Vol.17, no.3: 353 – 357.
52. Abate, A. K. 1995. Document delivery expert. Journal of Interlibrary Loan, Document Delivery and Information Supply, Vol.6, no.1: 17 – 37.
53. DeJong, Gerald. 1982. An overview of the FRUMP system. In: Lehnert, W. G. and Ringle, M. H. (eds.). Strategies for Natural Language Processing. London : Lawrence Erlbaum, pp. 149 – 172.
54. Lebowitz, M. 1986. An experiment in intel-ligent information systems – RESEAR- CHER. In: Davies, R., (ed.). Intelligent Information Systems: Progress and Pros-pects. Chichester, England : Ellis Hor-wood. pp. 127 – 149.
55. Husk, G. D. 1988. Techniques for automatic abstraction of technical documents using reference resolution and self-inducing phrases. Master’s dissertation. University of Lancaster.
56. Shneiderman, Ben. Designing the User Interface: Strategies for Effective Human- computer Interaction. Addison-Wesley, Reading, MA, 1997.
57. Internet protocol suite. https://en.wikipedia.org/wiki/Internet_protocol_suite
58. Jackson, Peter (1998), Introduction To Expert Systems (3 ed.), Addison Wesley, p. 2, ISBN 978-0-201-87686-4.
59. Expert system. https://en.wikipedia.org/wiki/Expert_system
60. Berners-Lee, T., Hendler, J. and Lassila, O. (2001). The Semantic Web: a new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American. http://www.scientificamerican.com/article.cfm?id=the- semantic-web
61. Qualcat: Automation of Quality Control in Cataloguing. British Library Research and Development, 1994, pp. 112.
62. Hypertext. https://en.wikipedia.org/wiki/Hypertext
63. Hypermedia. https://en.wikipedia.org/wiki/Hypermedia
64. Marchionini, G. and Shneiderman, Ben. (1993). Finding facts vs. browsing knowledge in hypertext systems. In “Sparks of innovation in Human-Computer interaction,” edited by Ben Shneiderman (ed.). Ablex Pub., New Jersey, pp. 103-122.
65. Lowe, David and Hall, Wendy (1999). Hypermedia & the Web: An Engineering Approach. Willy.
66. What is hypermedia? http://users.ecs.soton.ac.uk/lac/LoweNHall/extracts/Hypermedia.html
67. Henninger, S. and Belkin, N. J. (n.d.). Interface Issues and Interaction Strategies for Information Retrieval Systems. http://www.sigchi.org/chi96/proceedings/tutorial/Henninger/njb_txt.htm