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Visualization of data in general and also visualization of document collections is a method for obtaining early measures of data quality, content, and distribution (Fayyad et al., 2001). For instance, by applying document visualization it is possible to get an overview of the content of a Web site or some other document collection. This can be useful especially for the rst phases of semi-automatic ontology construction aiming at domain and data understanding (see Section 2.4). Visualization can be also used for visualizing an existing ontology or some parts thereof, which is potentially relevant for all the ontology learning scenarios de ned in Section 2.5. One general approach to document collection visualization is based on clustering of the documents (Grobelnik and Mladenic, 2002) by rst representing the documents as word-vectors and performing k-means clustering on them (see Subsection 2.6.1). The obtained clusters are then represented as nodes in a graph, where each node in the graph is described by the set of most characteristic words in the

(1,1 t) (0,t)

(t,0)

Figure 2.1 An example output of a system for graph-based visualization of document collection. The documents are 1700 descriptions of European research projects in information technology (5FP IST).

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As you can see, the Write() method in C# uses the fact that the boxed arrays know how long they are. Please note that the message is not sent (at least it is not received by the custom server) until it is complete, based on the four-byte length. This means that you can write the message in a number of sections, as above, or put together a single buffer and send that. To read the data, we use the Read() method. We start by reading the four-byte message length and then we read the actual data. If your protocol is such that your packets are supposed to be of a xed size then you might think that you could read the whole message in one operation, but I suggest that you stick with reading it in two stages.

So each must wait up to 30 min for the other for them to meet with probability 3/4

corresponding cluster. Similar nodes, as measured by their cosinesimilarity (Equation (2.2)), are connected by a link. When such a graph is drawn, it provides a visual representation of the document set (see Figure 2.1 for an example output of the system). An alternative approach that provides different kinds of document corpus visualization is proposed in Fortuna et al., 2005b). It is based on Latent Semantic Indexing, which is used to extract hidden semantic concepts from text documents and multidimensional scaling which is used to map the high dimensional space onto two dimensions. Document visualization can be also a part of more sophisticated tasks, such as generating a semantic graph of a document or supporting browsing through a news collection. For illustration, we provide two examples of document visualization that are based on Knowledge Discovery methods (see Figure 2.2 and Figure 2.3). Figure 2.2 shows an example of visualizing a single document via its semantic graph (Leskovec et al., 2004). Figure 2.3 shows an example of visualizing news stories via visualizing relationships between the named entities that appear in the news stories (Grobelnik and Mladenic, 2004).

retval = myStream.Read(message);(4, out oResponse); int msgLen = 0; if(retval == 4) { byte[] buff = (byte[])oResponse; msgLen = ReadInt32(ref buff, 0); System.Console.WriteLine("Message length is {0}", msgLen); } else { System.Console.WriteLine("Failed to read message length"); } if(msgLen > 0) { retval = myStream.Read(msgLen, out oResponse); if(retval == msgLen) { byte[] buff = (byte[])oResponse; for(int i = 0 ; i < msgLen ; i++) {System.Console.WriteLine("byte {0} = {1}", i, buff[i]);} } else { System.Console.WriteLine("Tried to read {0} but only read {1}", msgLen, retval); } }

In the previous example, we speci ed the probability that Joan and Jim meet Now suppose we want to know how the waiting time, say t hours for each person, affects the probability that they meet The probability that they meet is 1 2

(1 t)2

There are several advantages to using NLG rather than using xed templates where the query results are lled in: NLG can use different sentence structures depending on the number of query results, for example conjunction versus itemised list. Depending on the user s pro le of their interests, NLG can include different types of information af liations, email addresses, publication lists, indications on collaborations (derived from project information). Given the variety of information which can be included and how it can be presented, and depending on its type and amount, writing templates may not be feasible because of the number of combinations to be covered. This variation in presentational formats comes from the fact that each user of the system has a pro le comprising user supplied (or system derived) personal information (name, contact details, experience, projects worked on), plus information derived semi-automatically from the user s interaction with other applications. Therefore, there will be a need to tailor the generated presentations according to user s pro le.

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