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it is that it uses an ontology to provide a very speci c and personalised viewpoint of the webpages the user wishes to browse. This is important because different users often have different degrees of knowledge and/ or familiarity with the information presented, and have different browsing needs and objectives. Magpie s main limitation is that it does not perform automatic population of the ontology with new instances, that is, it is restricted only to matching mentions of already existing instances.



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When Harvey sat back down, the rest of the crowd had dissipated and we were alone in the booth He said: Did you see, Harry, he started, did you see what just happened there I had to admit that the only thing that I had seen were two stupid people overpaying for currency I told him so Okay Does that apply to trading I thought for a moment This depressed me I didn t want to think about my losses from that morning I am sure that it does, I replied I am sure that there is a lesson in here for me I honestly don t want to talk about it, though I paused The truth struck me then: Harvey would soon leave and I would never trade successfully Soon I would return to work at some crap law firm if anyone would still take me.





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j, n 1 + n 2,0 + . . . + n 2, j n 1 1 ,

(18.21)

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The PANKOW system (Pattern-based Annotation through Knowledge on the Web) (Cimiano et al., 2004) exploits surface patterns and the redundancy on the Web to categorise automatically instances from text with respect to a given ontology. The patterns are phrases like: the hINSTANCEi hCONCEPTi (e.g. the Ritz hotel) and hINSTANCEi is a hCONCEPTi (e.g., Novotel is a hotel). The system constructs patterns by identifying all proper names in the text (using a part-of-speech tagger) and combining each one of them with each of the 58 concepts from their tourism ontology into a hypothesis. Each hypothesis is then checked against the Web via Google queries and the number of hits is used as a measure of the likelihood of this pattern being correct. The system s best performance on this task in fully automatic mode is 24.9 % while the human performance is 62.09 %. However, when the system is used in semi-automatic mode, that is, it suggests the top ve most likely concepts and the user chooses among them, then the performance goes up to 49.56 %. The advantages of this approach are that it does not require any text processing (apart from POS tagging) or any training data. All the information comes from the web. However, this is also a major disadvantage because the method does not compare the context in which the proper name occurs in the document to the contexts in which it occurs on the Web, thus making it hard to classify instances with the same name that belong to different classes in different contexts (e.g., Niger can be a river, state, country, etc.). On the other hand, while IE systems are more costly to set up, they can take context into account when classifying proper names.

(18.22)

My eyes welled up with tears Harvey, I am not sure I can talk about this I lost almost all of our savings today He eyed me sternly: I already know that Not an ounce of sympathy from a man who had just given $100 to some complete strangers I know that Tell me something I don t know, or let me finish I didn t let him finish I stood up and walked out of the restaurant With a step outside the restaurant, he gripped my shoulder and spun me around He was very, very angry His eyes were on fire Then he began to speak..

where Bi = W j for all i s in the range g j i < g j+1 . To prove playback continuity for media segments broadcast in Type-II channels, we consider an arbitrary Type-II group, say group j, comprising media segments {L i | g j i < g j+1 }. As the client begins receiving all channels in group j at time t0 + (m + h j ) U (18.23)

The SemTag system (Dill et al., 2003) performs large-scale semantic annotation with respect to the TAP ontology.1 It rst performs a lookup

and it takes (U b)/W j seconds to completely receive the media segments, the time s j at which all media segments in the group is ready for playback can be computed from s j = t0 + (m + h j ) U + U b Wj (18.24)

Substituting W j from equation (18.19) into equation (18.24) we obtain s j = t0 + (m + h j ) U + (g j h j ) U = t0 + (m + g j ) U t0 + (m + i) U, for g j i < g j+1 (18.25)

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