ESTRATEGIAS DEL MARKETING

In the source selection systems, a list of sources are presented to the user to enable a more efficient analysis. However, one must decide which of the retrieved documents are relevant to the information they are looking for to proceed with the relevant information. In this subsection, we are looking for the answers for the questions i) can we find the sources with highly ranked contexts w.r.t. the query contexts (domains of the user interest)? i) can we filter/rank lower the sources unrelated contexts to the query?

In the scope of this work, we aim at improving retrieval performance by using query contexts which are derived from explicit or implicit user context information. This process allows us to ease of discovering suitable sources for the queries and rank them in the top-k results by assigning context-aware ranking scores computed using relations between context nodes in the context ontology model.

the most relevant ones among them to provide the meaningful results. Thus, we exploit from context and quality information to rank the sources according to multi criteria because we have more than one measure to assess the relevancy of a source. Given a query, if the returned source addresses the stated information need, then, the source is relevant [63]. In our case, these criteria are: context similarity, quality score, and source cardinality. The idea is ranking the sources w.r.t. the number of triples related with each context and quality information. Thus, we compute each criteria as follows:

• Context Distance described in Definition 2.5 is computed to find the shortest path between source context and query context for a given query. We compute the shortest path using least common ancestor (lca) of the context nodes for the given contexts c1and c2:

d(c1, c2) = d(lca(c1, c2), c1) + d(lca(c1, c2), c2) (4.2)

• In the scope of this thesis, dereferencability metric is used to assess the quality of the data sources. Quality score for dereferencability metric is computed as follows:

qsS_der= dere f erencableTripleNumber in a source/totalTripleNumber in a source (4.3) • Cardinality is the number of triples per source which contributes to the query result depend- ing on the size of the intersected bucket. In our specific case, higher cardinality implies a higher relevance of the source.

In this study, the relevance score is computed for each source via linear combination. Changing the weights of the criteria allows us to change the importance of the criteria dynamically, e.g., giving a higher weight for the context similarity means that context similarity has higher importance. In the scope of this work, we define a ranking function introduced as linear combination which aggregates mentioned three criteria and produces a score for each source.

Definition 4.1. (Multi Criteria Ranking Function) R(Q, S) is a ranking function which associates a score with the pair(Q, S), where Q is a query and S is a source. Let assume v₁, v₂, v₃, ...v_n∈ R are variables and c₁, c2, ..., cn∈ R are scalars (or weights). A ranking function is a linear combination

of variables and scalars which is defined as R(Q, S) = c1v1+ c2v2+ · · · + cnvn, where n

∑

i=1

c_i= 1. The join space results are sent to the rankSources method in Algorithm 9 to rank the sources according to these join space results. Every source coming from the join space with context , quality and number of cardinality is summed up to find an overall ranking value for the sources. Algorithm 9 shows the ranking methodology for the sources. In the first loop (Line 3-10), each source in the overall buckets are iterated to assign to find their contribution to the buckets, beside

assigning their quality and context values at the same time. In the second loop, (Line 12-15) contexts of the sources are compared to the query context. Maximum context similarity of the source context to the query context is found by computing the shortest path between them which requires the minimum distance between compared contexts. Beside maximum context value is found for the overall sources to normalize their value. In the third foreach loop (Line 17-23), quality score, context similarity and source cardinality are used to find a source relevance value by using linear ranking function. These three criteria values are all normalized according to their maximum values before computation to ensure that criteria dependencies are consistent and logical. In the last loop, to avoid same relevance value a small difference value is added for the sources which have same relevance value.

Algorithm 9: Ranking Algorithm

Input : JoinSpace result, Query context queryCxt Output : List of ranked sources

1 begin

2 Function rankSources(result,queryCxt)

3 foreach Buckets B  JoinSpace do

4 foreach Source src  Bucket do

5 Double tripleCount = src.getCount() ;

6 Get source count rankCount in rankList ;

7 if rankCount.isNull then rankCount ==0.0;

8 rankCount+=tripleCount ; 9 rankList.put(source,new ContextQuality()); rankList.get(source).setCount(rankCount); rankList.get(source).setContext(); rankList.get(source).setQualityValues(); 10 end 11 end

12 foreach Source src ∈ rankList do

13 Compute source triple count as tripleCount;

14 Compute maximum context similarity as maxSim;

15 Retrieve source quality as sourceQuality;

16 Find mean source relevance(tripleCount, maxSim, sourceQuality) as

sourceRelevance;

17 sourceList.put(source, sourceRelevance) ;

18 end

19 end

We have now found a criteria for improving the source selection which guides us to the question that remains is whether can we do better. In the next section we will discuss another version of the Extended QTree to have more optimal results.