The current Web is not only a place for the changing, how it absorbs people with their opinions, content available in any time and location. It is also a place ratings and tags1. Especially its dynamic nature prewhere we actually spend time to perform our working tasks, vents us from a direct employing of the most methods a place where we look for not only interesting informa- developed for closed information worlds (even though tion, but also entertainment, and friends, a place where big or actually present on the Web). And its size rewe spend part of our rest. The Web is also an infrastruc- quires automatic (or semiautomatic) approaches for tsuoremfaonryaapspplieccattsioonfsthwehiWchebo®tehratvathriiosudsivseerrsveiceosr.gaTnhiesmre iiss information acquisition from this large heterogeneous a subject of study of researchers from various disciplines. information space. In this paper we concentrate on information retrieval aspect The Web is undergoing constant development with of the Web, which is still prevailing. How we can improve { the Semantic Web initiative, which aims for a mainformation retrieval, be it goal-driven or exploratory? To chine readable representation of the Web [3], which extent we are able to give our machines means for { the Adaptive Web initiative, which stresses the helping us in information retrieval tasks? Is there any level need for personalization and broader context of semantics, which we can supply for the Web in general, adaptation on the Web [6], and it will help? We present some aspects of information { the Web 2.0 initiative called also the Social Web, palceqsuiosfitaiopnprboyacsheeasrctho poanrttihceul\awritlda"skWsteobwtaorgdesththeer iomfperxoavme-- which focuses on social and collaborative aspects ment of information search, which were proposed in last of the Web [14]. two years within the Institute of Informatics and Software Development in this area matures to the point wheEngineering at the Slovak University of Technology, espe- re the Web is becoming so important and in fact still cially within the PeWe (Personalized Web) research group. unknown phenomenon that is identi¯ed as a separate, original object of investigation, and there are even initiatives which want to establish the Web Science as 1 Introduction a new scienti¯c discipline [7]. Considering information retrieval based on search The Web is amazing by the amount of diversity of its (be it goal-driven or exploratory) includes also e®ecstu®, by the conception of so much thoughts, discus- tive means for expressing users' information needs { sions, opinions that all show in many cases wisdom how should a user specify his query or a broader aim and creativity of people. This is also the bottleneck of the search (be it a concrete requirement for explaof current web { it is its nature, which involves \web nation of particular term or an abstract need for ¯ndobjects" of various type (text, multimedia, programs) ing out what is interesting or new in some domain). representing conceptually di®erent entities (the con- The \e®ective" here means that the user gets what he tent, people, things, services) and constantly chang- expects, even if his expectations are not completely ing. Particular objects are not formally de¯ned, e.g. known { this is pretty similar to the software requirethe content is semistructured, which leads to the com- ments speci¯cation, but within the \wild" Web we plexity considering machine processing. have so much and so diverse users with various needs Obvious sentences are expected here { how is the that we are not able to do this manually as software Web important for our lives (both work and private), engineers do with the software speci¯cation. how the Web grows, how it is dynamic and constantly In general, user's information needs usually come into existence while the user solves a task. Information needs can be classi¯ed into three categories [5]:
? This work was partially supported by the projects
VEGA 1/0508/09, KEGA 028-025STU-4/2010, and it is the partial result of the Research & Development Operational Programme for the project SMART II, ITMS 26240120029, co-funded by the ERDF.
1 We do not mention and elaborate further another important view on the Web as an infrastructure for services and software applications.
These categories cannot be directly inferred from the user's query. However, good search engine should consider various information needs as this implicates a move from a static information retrieval (¯rst two categories) to the third category, which integrates not just data stored on the Web, but also services that can provide right information (e.g. planning a °ight).
{ Informational. The user's intent is to get speci¯c 2.1 Considering the web content information assumed to be present on the Web.
The only assumed interaction is reading. The content, or resources in general are basically de{ Navigational. The user's intent is to reach a par- scribed by metadata. Metadata were used by libraticular web page. It is assumed that a user will rians already before the Web era. They typically rec\travel" through the Web space taking advantage ognize three categories of metadata: administrative, of getting a starting point. structural, and descriptive [21]. Considering the Web { Transactional. The user's intent is to perform and it content we focus on descriptive metadata resome activity enabled by the Web, i.e. the use of lated to the content. Moreover, metadata for the Web a service o®ered by particular web page. comparing to libraries resources should conform the fact that we cannot predict all kinds of the Web objects and their evolution.
The semantics of the content can be expressed many ways ranging from { the set of keywords (or tags) through { the Resource Description Framework or topic maps as a general model for conceptual description of resources to { ontologies with all power resultant from formal logic where the ontology consists of concepts, relations, attributes, data types, a concept hierarchy, and a relation hierarchy. 2
Web and semantics I do not feel a need for putting here well-known arguments about the importance of semantics for automatic reasoning. Yes, it is important! This fact was Having ontologies that cover (almost) \complete" stated already many times from its ¯rst publishing semantics which we are presently able to specify seems in [3] even though what we give a machine actually is to be a solution for the Semantic Web. But it is not, not the semantics; for the machine it is only a syntax at least now. Considering the complexity of de¯ning { formal description of a resource. such semantics recalls the situation some more than
The question is not what we can do with the se- 40 years back when people tried devise general solmantics when it is perfect, but how to acquire it. How ving machines. Even though they moved later to exmuch semantics we can acquire for constantly chang- pert knowledge capturing, the results were still limited ing world of the Web, or what amount is already useful mainly due to the ability of people to specify knowto such extent that we can report an improvement in ledge explicitly. So the situation repeats in some sense. ful¯lling our information needs. Right after the Semantic Web establishment we
With the Web development several sources for the have witnessed a boom of various approaches to repsemantics come into existence. Except the resenting semantics for speci¯c domains and methods for reasoning including mapping ontologies. However, { web content as a fundamental source for the se- ontology-based semantics is spreading slowly because mantics, we obviously have solutions just for very speci¯c and there are other sources of the semantics that can be rather static domains. It is perfect way for the applimined: cation architecture as knowledge bases were in 70ties.
But it does not ¯t well with the \wild" Web. { web structure with the focus on links analysis, and Even if we would have formally represented knowl{ usage logs with the focus on a user activity on the edge that would be su±cient for the best part of our Web mainly by an analysis of clickstreams. needs (knowledge representation problem in Arti¯cial Intelligence), and would have strong reasoning meAs a special case of the content source we consider chanisms, it is not enough for the changing Web { we { web annotations, still miss a component for matching this knowledge to particular web objects. Moreover, the Web is evolving when viewing the annotations as a layer above the as we people evolve in unpredictable way. New inforcontent created either automatically [11] or manually mation and knowledge is constantly added to the Web (in particular by user interactions and social tagging). either as semistructured content or as services or apThe web annotations can be viewed also as a result of plications running on the Web. the users' activity and as such considered as a source Web 2.0 brought or vitalized a role of people in for the web usage mining. the whole process. We witness the power of crowd and its limitations. Folksonomy is simply a returning back sults [15]. This models conforms also with existing and to the most elemental way to enrich a resource with evolving folksonomies that can supplement extracted semantics employing a set of keywords. Fundamental metadata, and can be fully captured within the model. di®erence lays in the process of keywords acquisition. We believe that proposed model can improve inforFolksonomy is created by users through the process mation search. Our con¯dence is supported by partial of social tagging [12]. The advantage is real power of results achieved (some of them are brie°y mentioned users, so keywords attached to the resource by social in the Section 3). There are still some issues related to tagging represent rather objective notation of a web the proposed model. As the most serious we consider: page content. The problem is that folksonomies are coarse-grained, informal and °at. { extracting the right terms (concepts);
Following this trend we proposed a model of light- { creating and typing relationships between conweight semantics of the web content referred to as the cepts; resource metadata [17]. It is promising in the sense { multilingual and multicultural aspects as for exof its automatic acquisition for open corpus, or vast ample some terms can have completely di®erent and dynamic domains. It provides a meaningful ab- meaning in dependence of culture. straction of the Web content, i.e. provides a metadata Especially term extraction is well developed ¯eld model, and a mapping between web pages and this with term-indexing approaches and named entity resmetadata model. olution. Considering the model alone, the semantics
The model consists of interlinked concepts and is still rather low as we cannot recognize properly imrelationships connecting concepts to resources (sub- portant terms for particular user in particular context. jects of the search) or concepts themselves (see Fig- That is why there is the need to combine all sources for ure 1). Concepts feature domain knowledge elements the semantics [13]. We mention here except the con(e.g., keywords or tags) related to the resource content tent also web users' activity (web structure and web (e.g., web pages or documents). Both the resource-to- annotation are out of the scope of this paper). concept and the concept-to-concept relationships are weighted. Weights determine the degree of concept relatedness to the resource or to other concept, respec- 2.2 Considering web users' activity tively. Interlinked concepts result in a structure resembling lightweight ontology, and form a layer above the resources allowing an improvement of the search.
Monitoring a user's activity can serve as important source for semantics. Utilizing an implicit user feedback we can recognize which web pages (or even their parts) are interesting in particular context, and thus adjust or enrich metadata related to that content. User related metadata (i.e., a user model) allow personalization. Considering the \wild" Web with its light
Metadata weight semantics the spreading the personalization to (keywords, tags, the whole Web becomes possible (to some extent). concepts) Resource metadata model introduced above serves also as a bottom layer for an overlayed user model. As we operate in open corpus it is not possible to have either of the models in advance. We propose to represent user's interests (discovered via web usage mining) by
Resources the same means as the resource metadata, and provide (web content) r1 r2 r4 r5 rn consIftawnet wmaanptptinogembeptlwoeyesnutchhemseotdweolsmfoordtehlse. purpose r3 of information retrieval on the \wild" Web, we need to
acquire terms (keywords, tags, concepts) from the web Fig. 1. Content model based on lightweight semantics. pages visited by the users. Because the Web is an open information space, we need to track down and process every page the user has visited in order to update his
The advantage of modeling domain knowledge as model appropriately. described above lies in its simplicity. Hence, it is pos- To achieve this, we developed an enhanced proxy sible to generate metadata enabling lightweight se- server, which allows for realization of advanced opermantic search for a vast majority of resources on the ations on the top of requests °owing from a user with Web. We have already performed several experiments responses coming back from the web servers, all over of automatic metadata extraction with promising re- the Internet [2]. Figure 2 depicts the schema how the request
request User + .js p r o x y
s e r v e r translate readability (main textual content extraction) .js metadata extraction metadat a............ extraction metadata extraction user model proxy server operates. When the web server sends the 3 Examples response to the required resource back to the user, the We present several examples of approaches to parproxy server enriches the resource by a script able to ticular tasks towards the improvement of information capture the user activities (due evaluation of the user search, which were proposed and evaluated in last two feedback). In parallel we run a process of extracting years within the Institute of Informatics and Software the meta-data and concepts from the web page. To- Engineering at the Slovak University of Technology in gether with the user feedback, these are stored in the Bratislava, especially within the PeWe (Personalized user pro¯le. Before the extraction phase based on var- Web) research group. ious algorithms to semantic annotation and keywords, and category extraction, we realize main content de- 3.1 Gaming as a source of semantics tection (relevant textual part of the HTML document) and machine based translation into English, which is Computer games are potential sources of metadata required by the extraction algorithms. that are hard to extract by machines. With game rules
The aforementioned process gathers metadata for properly set and su±cient motivation, players can
inevery requested web page, and creates a basic (ev- directly solve otherwise costly problems.
idence) layer of a user model. Naturally, as the time Little Google Game. We proposed a method for
°ows, the keywords which represent long-term user in- term relationship network extraction via analysis of
terests occur more often than the others. Therefore, by the logs of unique web search game [
We deployed our enhanced proxy platform to de- amount of results returned by the search engine (we termine the e±ciency of the solution in real-world us- use Google at the moment). Afterwards we mine the age. The proxy solution can be, apart from user ac- game logs and extract relationships of terms based on tivity logging, used to improve user experience with their frequent common occurrence in the Web. ordinary web pages by adapting them according actual user needs. More, we provide users with a wordle- 3.2 Domain dependent approaches based visualization (Wordle tag cloud generator, In spite of domain independence of proposed models, http://www.wordle.net/) of their user pro¯les, and col- knowing the domain allows for more accurate models. lected a precious feedback, which helped us to deter- This is common approach also used by the most popmine \web stop-words", i.e., words which occur often ular web search engines, which blend data from mulon web pages but do not make any sense from the tiple sources in order to ful¯l the user's need behind user's interests point of view. An example of such his query using the advantage when domain is known a user pro¯le of one of the proxy authors is displayed (e.g. °ight planning or cooking a meal). in Figure 3.
ALEF, Adaptive Learning Framework. We pro- Adaptive faceted browser. We devised a faceted seposed a schema for adaptive web-based learning and mantic exploratory browser taking advantage of adabased on it we developed ALEF (Adaptive LEarning ptive and social web approaches to provide personFramework), a framework for creating adaptive and alized visual query construction support and address highly interactive web-based learning systems [16]. guidance and information overload [22]. It works on
ALEF domain model follows the resource meta- semantically enriched information spaces (both data data model described above. The content includes lear- and metadata describing the information space strucning objects that can be of three types: explanation, ture are represented by ontologies). Our browser faciliquestion and exercise. The domain model covers for tates user interface generation using metadata describevery learning object: actual content (text and me- ing the presented information spaces (e.g., photos). dia), and additional metadata that contain information which is relevant for personalization services (concepts, tags, comments). Comparing to other existing 3.3 User centric approaches approaches, the notion of metadata in ALEF is quite simpli¯ed, which allows for automatic construction of domain model, and on the other hand, it still provides a solid basis for reasoning resulting in advanced operations such as metadata-based personalized navigation.
Monitoring users and implicit feedback is promising approach for the \wild" Web. Even though an explicit user feedback (¯lling forms by a user) is easy to implement, it has serious problems with credibility, disturbing the user and dependence on his will.
based news recommendation based on articles
similarity. Considering high dynamic and large every day
volume of news we devised and evaluated in real
settings two representations for e®ective news
recommendation:
Query expansion by social context. We proposed
a method which implicitly infers the context of search
by leveraging a social network, and modi¯es the user's
search query to include it [
for adaptive link recommendation [8]. It is based on an analysis of the user navigational patterns and his behavior on the web pages while browsing through a web portal. We extract interesting information from the
Di®erent approach to news recommendation pro- web portal and recommend it in the form of
personalvided on the same e-news portal (www.sme.sk) is pre- ized calendar and additional personalized links.
sented in [