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### 6.3 Experimental Setup

In this section we introduce the experimental environment and the experiments that we perform to answer the research questions for this chapter. We start by detailing our data sets and then introduce our evaluation methods and manual assessments.

#### 6.3.1 Data

 Session ID Query ID Query ($Q$) jyq4navmztg 715681456 santa claus canada jyq4navmztg 715681569 santa claus emigrants jyq4navmztg 715681598 santa claus australia jyq4navmztg 715681633 christmas sun jyq4navmztg 715681789 christmas australia jyq4navmztg 715681896 christmas new zealand jyq4navmztg 715681952 christmas overseas
Table 6.4: An example of queries issued in a (partial) session, translated to English.

Two main types of data are needed for our experiments, namely search engine queries and a structured knowledge repository. We have access to a set of 264,503 queries issued between 18 November 2008 to 15 May 2009 to the audiovisual catalog maintained by Sound and Vision. Sound and Vision logs the actions of users on the site, generating session identifiers and time stamps. This allows for a series of consecutive queries to be linked to a single search session, where a session is identified using a session cookie. A session is terminated once the user closes the browser. This data set is analyzed and described more fully in [142], an example is given in Table 6.4. All queries are Dutch language queries (although we emphasize that nothing in our approach is language dependent). As the “history” of a query, we take all queries previously issued in the same user session. The DBpedia version we use is the most recently issued Dutch language release (3.2). We also downloaded the Wikipedia dump from which this DBpedia version was created (dump date 20080609); this dump is used for all our text-based processing steps and features.

#### 6.3.2 Training Data

Figure 6.3: Screen dump of the web interface the annotators used to manually link queries to concepts. On the left the sessions, in the middle a full-text retrieval interface, and on the right the made annotations.

For training and testing purposes, five assessors were asked to manually map queries to DBpedia concepts using the interface depicted in Figure 6.3. The assessors were presented with a list of sessions and the queries in them. Once a session had been selected, they were asked to find the most relevant DBpedia concepts (in the context of the session) for each query therein. Our assessors were able to search through Wikipedia using the fields described in Section 6.2.1. Besides indicating relevant concepts, the assessors could also indicate whether a query was ambiguous, contained a typographical error, or whether they were unable to find any relevant concept at all. For our experiments, we removed all the assessed queries in these “anomalous” categories and were left with a total of 629 assessed queries (out of 998 in total) in 193 randomly selected sessions. In our experiments we primarily focus on evaluating the actual mappings to the LOD cloud and discard queries which the assessors deemed too anomalous to confidently map to any concept. In this subset, the average query length is 2.14 terms per query and each query has 1.34 concepts annotated on average. In Section 6.5.1 we report on the inter-annotator agreement.

#### 6.3.3 Parameters

As to retrieval, we use the entire Wikipedia document collection as background corpus and set $\mu$ to the average length of a Wikipedia article [356], i.e., $\mu =315$ (cf. Eq. 2.7). Initially, we select the 5 highest ranked concepts as input for the concept selection stage. In Section 6.5.3 we report on the influence of varying the number of highest ranked concepts used as input.

As indicated earlier in Section 6.2.2, we use the following three supervised machine learning algorithms for the concept selection stage: J48, Naive Bayes and Support Vector Machines. The implementations are taken from the Weka machine learning toolkit [344]. J48 is a decision tree algorithm and the Weka implementation of C4.5 [253]. The Naive Bayes classifier uses the training data to estimate the probability that an instance belongs to the target class, given the presence of each feature. By assuming independence between the features these probabilities can be combined to calculate the probability of the target class given all features [154]. SVM uses a sequential minimal optimization algorithm to minimize the distance between the hyperplanes which best separate the instances belonging to different classes, as described in [246]. In the experiments in the next section we use a linear kernel. In Section 6.5.3 we discuss the influence of different parameter settings to see whether fine-grained parameter tuning of the algorithms has any significant impact on the end results.

#### 6.3.4 Testing and Evaluation

We define the mapping of search engine queries to the LOD cloud as a ranking problem. The system that implements a solution to this problem has to return a ranked list of concepts for a given input query, where a higher rank indicates a higher degree of relevance of the concept to the query. The best performing method puts the most relevant concepts towards the top of the ranking. The assessments described above are used to determine the relevance status of each of the concepts with respect to a query. We employ several measures that were introduced in Chapter 3.

To verify the generalizability of our approach, we perform 10-fold cross validation [344]. This also reduces the possibility of errors being caused by artifacts in the data. Thus, we use 90% of the annotated queries for training and validation and the remainder for testing in each of the folds. The reported scores are averaged over all folds, and all evaluation measures are averaged over the queries used for testing. In Section 6.5.3 we discuss what happens when we vary the size of the folds. For determining the statistical significance of the observed differences between runs we use a one-way ANOVA test to determine if there is a significant difference ($p\le 0.05$) as introduced in Section 3.2.2.

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