Improving WWW Access - from Single-Purpose Systems to Agent Architectures?

D.Sramek, P.Berka, J.Kosek, V.Svatek
Faculty of Informatics and Statistics, The University of Economics, Prague
W. Churchill Sq. 4, Prague, Czech Republic
{sramek,berka,kosek,svatek}@vse.cz

Abstract
Sophisticated techniques from various areas of Artificial Intelligence can be used to improve the access to the WWW; the most promising ones stem from Data Mining and Knowledge Modeling. We describe the process of building two experimental systems: the VSEved system for intelligent meta-search, and the VSEtecka system for navigation support. We discuss our experience from this process, which seems to justify the hypothesis that the Multi-Agent paradigm can improve the efficiency of web access tools, in the future. With this respect, we outline a web-oriented multi-agent architecture.

Keywords: WWW Access, Data Mining, Knowledge Modeling, Meta-Search, Navigation Support, Agent Architecture.

1 Introduction

During the last few years, the World-Wide Web has become one of the most widespread technologies of information presentation. Making the enormous amount of information on the web really useful is inconceivable without intelligent assistance, both for end users and for maintainers of large sites. From this point of view, we can distinguish two basic groups of tasks that are frequently attacked by web applications:
•	The most important user-oriented tasks are probably: 
1)	search (retrieval) of relevant documents, using one-shot queries
2)	filtering a stream of new documents against stable profiles
3)	navigation, i.e. support for the user during the browsing session
4)	question-answering, i.e. extraction of relevant low-grained data relevant to the user questions (see e.g. [Gaisauskas, Humphreys, 2000]); a particular form of question-answering is passage retrieval, which is rather similar to document retrieval.
•	As maintainer-oriented tasks, we can view especially: 
1)	overall site auditing and maintenance 
2)	low-level marketing tasks such as market-basket analysis of customers’ access.
A number of techniques can be used to solve these tasks (cf. e.g. [Chakrabarti, 2000]): statistically-grounded information retrieval methods, computational linguistics and natural language processing methods, as well as more ad-hoc artificial intelligence (AI) techniques. Within AI, the dominating paradigms seem to be those of data mining and knowledge modeling.
1.1 WWW and Data Mining
Zaiane and Han [Zaiane, Han, 1998] gave a nice taxonomy of data mining in the web environment, to say, web mining. They distinguish web content mining, web structure mining and web usage mining. 
The goal of web content mining is to extract knowledge from the web pages itself.  The tasks related to this goal can be:
•	web search and meta-search (find pages relevant to the user’s query), or filtering (recognize pages relevant to the user’s profile); this is the question of information retrieval,
•	text mining (find knowledge “hidden” in the pages); this is the question of  information extraction or question answering.
While the goal of information retrieval is to find relevant pages (strictly speaking, to find a set of pages with high precision and high recall), the goal of information extraction is to extract information from these pages. Text mining can be applied e.g. for discovering associations in collections of textual documents [Feldman, 1997].
Web structure mining means extracting knowledge from web structure and hyperlinks. An observation has been made that web space is not homogeneously interconnected. There are pages (called hubs) pointing to a large number of other pages; there are pages (called authorities, i.e. referential for some areas of interests) pointed to by large number of pages.  Doing web structure mining we can e.g. look for some regular patterns of  links between such types of pages [Tomkins, 2000].  The information about the structure can be useful for navigation. Methods from the graph theory are suitable for performing such kind of analysis.
The goal of web usage mining [Srivastava, 2000] is to discover access patterns (form web server logs) and find paths frequently traversed by users. This task is very similar to market basked analysis performed in standard data mining; what are the goods (pages) frequently purchased (visited) by the customers. The results of web usage mining can be used in marketing (Amazon uses such approach when recommending similar books) and for reorganizing the web site (pages frequently visited during one log should be linked together).
1.3 WWW and Knowledge Modeling
The hot topics in up-to-date knowledge modeling are ontologies and problem-solving methods, namely their construction, sharing and reuse.
The Web environment, containing a huge, diverse collection of textual documents, is highly favorable for experiments in ontological engineering. Most research has concentrated on the development of ontologies (hierarchical collections of concepts, their relations and other elements) as a background for semantic unification of the web content, in particular of metadata. In addition to „terminological“ ontologies (or, thesauri) used by digital libraries to annotate documents (such as the Dublin Core set [Weibel et al., 1998]), more sophisticated „knowledge“ ontologies  have emerged as a results of efforts within the AI community. The two seminal projects most widely discussed are SHOE [Luke et al., 1997] and Ontobroker [Fensel et al., 1998]. The central issue of both is the use of ontologies by authors of web pages, in specifying knowledge annotations (i.e. richly structured metadata) for the pages and their parts; the difference between them lays in the construction and management of ontologies (distributed in SHOE, centralized in Ontobroker). Ontologies are also used as a conceptual grounding for factual knowledge bases, describing the (interesting parts of) the web at the level of instances, for inferential knowledge bases describing recurring patterns in web data, as well as for data mining tasks aiming at discovery of inferential knowledge.
In contrast, hardly any attention has been paid to web-specific problem-solving methods. In the project outlined in this paper, we plan to fill this gap via defining skeletal action plans for web access tasks, which will be refined and executed part-by-part by different agents in a multi-agent environment.
There is a number of single-purpose systems oriented on particular tasks described above. In our paper we present a slightly different attempt of using multi agent architecture to solve more tasks simultaneously.
The paper is organized as follows. Section 2 describes the experimental VSEved system developed for „intelligent“ information retrieval (using the meta-search technology). Section 3 describes another system named VSEtecka - a browsing assistant, which is currently being developed in order to support the navigation in the web space via a collection of meta-information and links to associated pages. Section 4 compares different tasks with respect to the (largely overlapping) input data they require. Finally, section 5 discusses the pros and cons of agent architecture, and suggests an agent-based model for the WWW information access, and section 6 summarizes the whole work.

2 The VSEved Meta-search System
2.1 Search and Meta-Search
Due to the enormous growth of the web, finding information about a specific topic can be extremely demanding. Search engines attempt to automate this task by means of building (off-line; manually - e.g. Yahoo, or automatically - e.g AltaVista) inverted indices of WWW pages, which can be then searched according to words/phrases given by the user. However, the use of search engines themselves entails significant difficulties for a common (inexperienced) user. There is a vast number of search engines at different locations, each with its own way of user interaction - the user thus has to know which one to choose, how to reach it and how to utilize it. Moreover, each engine is constrained by its own index, which usually covers only a small fraction of the whole WWW space.
The idea of WWW meta-search has subsequently emerged to help users to find more relevant information in a more convenient way. The essence of all meta-search systems consists in giving access to more than one search engine, while their other features may vary. The typical models of meta-search are as follows:
•	the user selects himself which search engine is to be queried (e.g. in the All-in-One system: www.albany.net/allinone)
•	the system itself queries all accessible search engines (e.g. the MetaCrawler system [Etzioni, 1997])
•	the system itself selects the most promising search engines to be queried (e.g. the SavvySearch system [Howe, 1997])
•	the system queries both its local database of frequently asked questions, and some remote search engines (e.g. the AskJeeves system: www.askjeeves.com).
As the main advantages of meta-search we can view:
•	simultaneous submission of the query to different search engines
•	exploitation of search engines possibly unknown to the user
•	single interface on the user’s side
•	merging and sometimes further post-processing of returned information (lists of hits).
2.2 Overview of the VSEved system
We have developed an experimental system named VSEved, which combines usual WWW meta-search with Artificial Intelligence techniques. In the former, VSEved has been inspired mostly by AskJeeves, in particular in 
•	directing the queries both to (multiple) remote search engines and to a local database of „direct answers“
•	linguistic preprocessing of the query, which can be written in natural language.
The local database of VSEved’s answers contains links to pages judged as interesting with respect to the particular community of the users of the system (it has been used mainly by university campus users). Linguistic preprocessing in VSEved consists in language recognition (English or Czech) simple lemmatization (for Czech), and extraction of linguistic Boolean operators (for Czech).
In addition, the hit lists returned by search engines are post-processed by means of a rule–based expert system, which accounts for their „cleaning“ (removing duplicities and dead links), integration, re-ordering (according to a „quality“ criterion based on query-term coverage) and structuring, in order to provide more concise and informative output to the user. Unlike other document-structuring systems that perform unsupervised clustering [Zamir, Etzioni, 1998], [Honkela et al., 1996], we have decided to use assignment to pre-defined categories (page types). Typological categorization is one of the display options of the system, besides linear ordering according to quality and (URL-) domain-based grouping. The process of typological categorization will be described in more detail in the next two subsections; more details of the VSEved systems can be found in [Berka et al., 1999].
2.3 VSEved and Knowledge Modeling
The design of VSEved’s typological categorization has been grafted on previous knowledge modeling (ontological engineering) efforts. An ontology named WEB-ONT, covering different aspects of the web (websites, logical documents, physical pages, tag structures, addressing etc.) had been elaborated [Simek, Svatek, 1998] and implemented in several languages (incl. e.g. SHOE). A part of this ontology, dealing with page typologies, has been reused for constructing the categories that could be assigned to the search hits retrieved (and thus to the pages referenced, respectively).
The three typologies are, in turn:
•	Bibliographic categorization, operating on concepts like „article“, „bibliography“, „image“, „pricelist„ or „newsgroup message“. This categorization was essentially borrowed from the existing Dublin Core metadata system [Weibel et al., 1998] (element ResourceType), which had been previously embedded into the WEB-ONT ontology.
•	Categorization according to the sphere of origin, such as „commercial“, „academic“, „governmental“, „non-profit“ or „private“.
•	Categorization according to technological type, such as „plain text“,  „form“ or „index“.
The operational rulebase (written in the CLIPS language) capable of recognizing (to a certain extent) these categories, has been constructed using, essentially, data-mining approaches.
2.4 VSEved and Data Mining
In the data-mining part of the VSEved project, we have attempted to build a rulebase relating
web document types to the following information:
•	Terms from and structure of the URL - this part of the rulebase is generally applicable (i.e. not only for meta-search, but also for navigation, filtering etc.).
•	Other information returned by search engines (name, size, date and textual „snippet“ of the page) - this part is specific for the meta-search task. Using this information can lead to reduction of ambiguity in the categorization.
Due to the structural nature (overall hit structure, plus linear structure of the URL, name and „snippet“) of the data, Inductive Logic Programming (ILP) seems to be a good choice for the learning approach. However, due to the high computational complexity of ILP, we have decided to use
•	fast and straightforward frequency analysis of terms, pairs of terms and specific symbols from a large set of URLs, in the overall type-assignment task (the details of the frequency-analysis process can be found in [Svatek, Berka, 2000])
•	sensitive but costly ILP, in the specific subtasks for which frequency analysis alone lead to ambiguous models - this was the case e.g. for the URL terms „art“, „pub“, „cat“ or „bio“.
The overall rulebase thus consists of two layers: pure URL-based rules (applicable for various categorization tasks) and rules encompassing general features identifiable in search results (applicable for the meta-search categorization only). We have shown in [Svatek, Berka, 2000] that even the first layer, consisting of a few dozens of pure URL-based rules, can more-or-less successfully assign some generic category to approx. 70-90% (depending on the language and query-specificity settings) of pages retrieved by search engines; 25-50% of the assignments account for Dublin-Core-like bibliographic categories. Future experiments will show the impact of the newly-introduced, ILP-based disambiguation on these figures.

3 The VSEtecka System for Navigation Support
3.1 Searching vs. Browsing
Today's web search engines clearly separate the phases of searching and using (i.e. reading pages) information stored in „Web Space“. You must specify your query to search engine as much precisely as you can. Search engine returns you list of pages which may be of your interest. Then you manually browse through this result. If founded pages are not exactly on your subject, you must go back to search engine, precise your query and hope that search results would be closer to your expectations.
Above described approach is used by most of today's search engines. But if we tighter integrate intelligent search process with browsing, we will get environment which is much more effective, user-friendly and intuitive for users. Our aim is to develop navigation support system VSEtecka which will provide useful information related to actually viewed page. 
3.2 Navigation-Support Information
After finishing VSEtecka will provide the following information in an easily-accessible way:

Meta-information about the current page
Useful information related to the current page alone, such as like author, title, keywords and so on. The meta-information set of VSEtecka will also include the same information as delivered by the VSEved meta-search system, see previous section.

Links to similar pages
The links to similar pages are categorized according several similarity criteria, such a content similarity, structural similarity and so on. This functionality will allow to traverse similar pages without the need for explicit web search service.

Associations
Almost every web page is a part of a larger document or web-site. Not all pages are well designed and, as a result, they do not contain very helpful navigation links. This part of the system will automatically provide the links (if appropriate) to the previous and next page in a sequence, to the table of contents of the document, to a relevant home page, and so on. This should prevent the user from getting lost on badly designed sites.

User definable filter
Filtering is an effective way of narrowing the scope of pages offered in the similar and associated pages’ sections. Only the pages which fit to restrictions set on the author’s name, page title, subject etc. (basically, any of the meta-information fields can be used here) will be supplied to the user.

Domain-specific information
In many domains, specific page categories and relations among them are important for the user’s understanding of the site structure, in addition to generic ones (which are listed under „associations“). This may be the case e.g. for course, lecturer and department pages in an academic environment (see e.g. [Craven et al., 1998]), or for company homepage, pricelist and press–releases’ page in a business environment.
3.3 Notes on Implementation and Data Input
In order to provide easy access to all above listed functions, we must seamlessly integrate our system with current browsers. There are several ways which can be used to enhance today's browsers – e.g. enhance the browser with a Java-applet or ActiveX component, write a completely new browser by reusing existing components and add our specialized behavior. Finally we decided to base our work on the Mozilla browser. In Mozilla, it is very easy to add to the browser an additional pane with our navigation system. Additional user components can be defined using XUL (XML User Interface Language) in conjunction with JavaScript. This solution is cross-platform, since the Mozilla browser is cross-platform.
For the system to operate conveniently on-line, the responses to user’s actions must be fast. Navigation information on the pane has to be updated in a few seconds after a new page has been loaded into the browser. This makes the option of collecting necessary data on-line for each request rather problematic. Instead, we assume that the system will mostly rely on a web-crawler that will scan a pre-defined subspace of the web: currently, we index the pages on (a huge number of) web-servers within the campus of the University of Economics, Prague. The information will be extracted from this pages using a synergy of AI methods, and stored in a factual knowledge-base (FKB), analogical to the knowledge base currently built within the WebKB project. The existence of the FKB will guarantee fast response to requests from the users aiming within its scope. For the requests outside its scope, the system will either have to perform on-line document retrieval and analysis, or will rely on limited information, such as URLs and anchor text of links (see [Svatek, Berka, 2000]) in a similar way as the VSEved system.

4 Search, Navigation and Beyond: Overlap of Web-Mining Tasks

The experience with the running prototype of the meta-search system, as well as the design of the (not finished yet) navigation support system have led us to the conclusion that the requirements of input data vary but significantly overlap for different web-related tasks. Let us enumerate the most characteristic information resources, as identified by us and others (e.g. [Chakrabarti, 2000], [Craven et al., 1998]), in the rough order of increasing grain-size:
•	Elementary data types, which (in particular, the first two) appear in different places and situations around the „web-world“; these mainly amount to
1)	text in a natural language
2)	URLs.
3)	images and other multimedia information
•	Static data structures:
1)	tag structure of pages, expressing (namely, HTML in the common way of use) display formatting and/or logical structure
2)	explicit, more-or-less standardized metadata on pages
3)	frequency count of linguistic terms, tag structures and non-text objects, as well as more abstract concepts (such as „personal names“ or „downward links“), within pages
4)	topology on the space of pages, induced by interconnecting links
•	Dynamic data structures:
1)	user-access data (web server or proxy logs)
2)	search queries and their results
Let us then match the data with the (somewhat refined) list of web-related tasks from section 1:
•	Meta-search with „limited information“ post-processing, as performed by the above described VSEved system, uses URLs, page titles, and, possibly, short (usually almost worthless from the point of view of linguistic analysis) text snipped from the beginning of the page.
•	Common search engine indexing, as well as some other off-line tasks such as filtering, process page fulltexts, thus yielding word frequencies, but possibly also linguistic, HTML and link structures; some search engines also rely on information about the users’ choice.
•	Navigation support as well as all site-maintenance oriented tasks put stress on the link structure; the latter are often based on usage mining, i.e. exploitation of user access data.
•	Marketing-related tasks always require user access data.
The overlap of lower-level data analysis tasks is, in our opinion, a strong incentive for the development of modular (agent) architectures for web analysis and access.

5 Analyzing and Accessing the Web using Agents - Discussion

Earlier in this paper, we have mentioned several powerful techniques which make it possible to exploit information hidden in the web more effectively. Somewhat surprisingly current search engines incorporate quite a few of them. The most often used method for web search is still the ancient word or term indexing. Current success of Google search engine [Chakrabarti, 2000] (in particular gaining Altavista users) proves that using more sophisticated techniques can provide a competitive advantage.
The following reasons may slow down the implementation of modern algorithms:
1)	Each technique may require different data representation
2)	User still expects integrated result (therefore the result aggregation must be implemented)
3)	Sophisticated algorithms are likely to require a lot of computer power
How can we build an integrated, powerful and efficient system? We are constrained by the need of several different data representations. For example we use a word index for classical fulltext search but a kind of an associative net for storing information about web structure. It is difficult to predict all possible representations required in the future. For each data representation we require a different kind of processing. This led us to consideration of many agents, each specialized for a specific web searching or mining task. As a first step we plan to develop a multi-agent architecture which should enable:
1)	Bottom up development model
2)	Possibility of gradual evolution of the system and easy adding of new features
3)	Natural decomposition of the system based on data representation
4)	Communication framework for query decomposition and result aggregation
In addition we believe that competition or cooperation of the specialized agents can bring better results than a single process, single representation system.
The schema shows our current preliminary version of the multi-agent system.

Basically there are several classes of agents:
1)	Data harvesting / preprocessing / storing in a standardized format
2)	Query decomposer and dispatcher / result aggregator
3)	Analytical agents (performing specific tasks)
4)	Helpers (performing useful common tasks like linguistic analysis)
As we want the analytical modules to focus just to their task we provide a standardized data format. For example all harvested web pages are checked for errors and converted to canonical XML. The meta-information about pages is also saved in the pre-defined format. In the future the web-harvesting module should also serve as a cache for requested documents which are temporary inaccessible (which is not a rare case).
For the same reason we develop helper agents which should resolve common requests from analytical agents e.g. a specialized text parsing or linguistic processing.
The analytical agents can be classified by a generality of task for which they are responsible. For example the structure-analyzing agent can solve rather wide area of tasks (e.g. find the nearest neighbors by mutual hyperlinks or by similar hyperlinks to other pages). On the other hand it should be possible to add an agent for such a specific task like answering who is the chair of the department X of the university Y (which can be implemented as filling this pattern by standard methods of IE). Currently we are finishing specifications of the analytical agents which should be implemented first to check the agent model features. A brief example of basic agents is included further in this paper.
One of the most difficult parts of implementing the suggested multi-agent system will be designing the agent responsible for communication with a user. This task represents understanding the user query and dispatching it to the right analytical agents. On the other hand the results of the specific agents must be aggregated to the qualified answer. We believe that quality of answers is the most important goal of the described system.
Examples of analytical agents
•	Word indexing agent – the basic agent which can answer questions like: What documents do contain the word X? Which document is the most relevant to the word X? In the most basic version this is nothing new but it will serve mainly for testing the communications among agents. In the future this agent will be upgraded to work with terms or possibly concepts and will use the text parsing and linguistic agents.
•	Structure analyzing agent – will answer questions like: What pages are near to this page in terms of number of clicks needed to get there? What pages cite the same addresses as this page does? This agent should store the web structure in the form of multidimensional hypergraph. Its answers can be used for example to sorting final results by number of citations.
We anticipate some drawbacks of suggested solution. In particular:
1)	The agent communication leads to overhead in processing time
2)	The different data representations lead to overhead in data store
3)	It is difficult to make global optimizations of query processing
We expect that the possible problems will arise as soon as we start to test the minimal prototype with just a few analytical agents and the basic dispatcher agent. We expect that most of the problems can be solved and the system can be tuned up in this minimal setting.  We plan exploit the experience to design maximally robust and efficient architecture which could be used as a framework for building powerful search engines and a testbed for testing modern and sophisticated AI techniques in exploiting the information richness of the Internet.

6 Conclusions

We have analyzed the problem of improving the WWW access using AI techniques, with stress on most common web-related tasks. For two of these tasks we have designed experimental systems: the VSEved system for intelligent meta-search, and the VSEtecka system for navigation support. We discuss our experience from this process, which seems to justify the hypothesis that the Multi-Agent paradigm can improve the efficiency of web access tools.
In the future, we would like to verify this hypothesis via implementing such modular architecture, which will embed also the components for the above mentioned meta-search and navigation systems.

Acknowledgements
This work is partially supported by the grant MSMT VS 96008.

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