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to the CO (coreference) task. Given the lower performance achievable on the CO task, semantic IE is in general a much harder task. OBIE poses two main challenges: the identi cation of instances from the ontology in the text; the automatic population of ontologies with new instances in the text. 3.3.1.1. Identi cation of Instances From the Ontology If an ontology is already populated with instances, the task of an OBIE system may be simply to identify instances from the ontology in the text. Similar methodologies can be used for this as for traditional IE systems, using an ontology rather than a at gazetteer. For rule-based systems, this is relatively straightforward. For learning-based systems, however, this is more problematic because training data is required. Collecting such training data is, however, likely to be a large bottleneck. Unlike traditional IE systems for which training data exists in domains like news texts in plentiful form, thanks to efforts from MUC, ACE (ACE, 2004) and other collaborative and/or competitive programs, there is a dearth of material currently available for semantic web applications. New training data needs to be created manually or semi-automatically, which is a timeconsuming and onerous task, although systems to aid such metadata creation are currently being developed. 3.3.1.2. Automatic Ontology Population In this task, an OBIE application identi es instances in the text belonging to concepts in a given ontology, and adds these instances to the ontology in the correct location. It is important to note that instances may appear in more than one location in the ontology because of the multidimensional nature of many ontologies and/or ambiguity which cannot or should not be resolved at this level (see e.g., Felber, 1984; Bowker, 1995 for a discussion).



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4b Client access bandwidth (multiples of video bit-rate b)

Figure 18.4 Start-up latency versus client access bandwidth (network bandwidth = 6b)

In this section, we give a brief overview of some current state-of-the-art systems which apply traditional IE techniques for semantic web applications such as annotating web pages with metadata. Unlike ontologybased IE applications, these do not incorporate ontologies into the system, but either use ontologies as a bridge between the IE system and the nal annotation (as with AERODAML) or rely on the user to provide the relevant information through manual annotation (as with the Amilcare-based tools).





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broadcasting schemes require a client access bandwidth to be equal to the network bandwidth. Therefore, if the client access bandwidth is the bottleneck, the network bandwidth in fact cannot be fully utilized, leading to the performance degradation. Finally, we note that the performance of PHB and CB converge when the client access bandwidth is increased to 6b, i.e., same as the network bandwidth, as CB reduces to PHB when the client access bandwidth constraint is removed.

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Figure 18.5 plots the maximum client buffer requirement versus the network bandwidth, ranging from 2b to 10b. The client buffer requirement is normalized and expressed as the ratio of the size of the media stream. For example, a ratio of 0.3 means that the client buffer must be large enough to store up to 30% of the whole media stream. We can observe from Figure 18.5 that the maximum client buffer requirement for all the schemes are comparable, and varies within a range from 0.2 to 0.5. For example, at a network bandwidth of 5b, the maximum client buffer requirements are 27%, 43%, 24%, and 32% for SB, GDB, SDB, and CB (with m = 4) respectively. The only broadcasting scheme that consistently achieves lower client buffer requirement is SDB. Therefore, the client buffer requirements of these broadcasting schemes are comparable.

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