SEARCH ENGINE USER
4.6 Data Integration
4.6.2 General Data Integration Framework
In Fig.4.10, we show a general end-to-end integration framework [37]. At first, the data needs to be preprocessed and transformed into network-based format as described in Sect. 4.6.1. Then, attribute resolution is performed, followed by entity resolution (i.e., merging). Entity resolution module takes a network with duplicated nodes as input and returns merged network, where new nodes (i.e., clusters) consist of a set of old duplicated nodes. The attribute resolution technique uses the same approach as entity resolution but works on attributes from different data sources and identifies which attributes represent the same data. Lastly, redundancy elimination step selects one representative value from each cluster and returns cleaned network. Post-processing step transforms the result network into selected format (e.g., attribute-value pairs, ontology-based) and returns it as a final result of integration execution.
Entity Resolution. A naive approach for entity resolution is simple pairwise comparison of attribute values among different entities. Although such approach could be already sufficient for flat data, this is not the case for network data, as the approach completely discards related data between the entities. For instance, when two entities are related to similar entities, they are more likely to represent the same entity. However, only the attributes of the related entities resolve to the same entities when their related entities resolve to not only similar but the same
entities. An approach that uses information, and thus resolves entities altogether, is denotedcollectiveentity resolution algorithm.
As an example, we show a state-of-the-art collective data clustering algorithm, proposed by Bhattacharya and Getoor [38]. The algorithm (Table4.6) is actually a greedy agglomerative clustering. Entities are represented as a group of clustersC, where each cluster represents a set of entities that resolve to the same entity.
Contexts (User, Data, Trust)
Fig. 4.10 General end-to-end integration framework
Table 4.6 Collective entity
resolution algorithm Collective entity resolution algorithm 1 Initialize clusters asC¼{{k}|k∈K} 2 Initialize priority queueQ¼∅ 3 forci,cj∈Candsim(ci,cj)θSdo
At the beginning, each entity resides in a separate cluster. Then at each step, the algorithm merges two clusters in C that are most likely to represent the same entity. During the algorithm, similarity of clusters is computed using a joint similarity measure, combining attribute, and related data similarity. First is a basic pairwise comparison of attribute values, while second introduces related information into the computation of similarity (i.e., data accessible using cluster neighbors in a network).
The algorithm (Table 4.6) first initializes clusters C and priority queue of similarities Q, considering the current set of clusters (lines 1–5). Each cluster represents at most one entity as it is composed out of a single knowledge chunk.
Algorithm then, at each iteration, retrieves currently the most similar clusters and merges them (i.e., matching of resolved entities), when their similarity is greater than threshold θS (lines 7–11), which represents minimum similarity for two clusters that are considered to represent the same entities. In line 11, clusters are simply concatenated. Next, lines 12–17 update similarities in the priority queueQ, and lines 18–22 insert (or update) also neighbors’ similarities (required due to related similarity measure). When the algorithm terminates, clusters Crepresent a sets of data resolved to the same entity. These clusters are then used to merge data at the redundancy elimination step.
After the entities have been resolved by entity resolution, the next step is to eliminate the redundancy and merge the data. Let c∈C be a cluster representing some entity,k1,k2,. . .,kn∈c be its merged references, andkc∈KC be the merged data within cluster. Furthermore, for some attribute a ∈A, we have precalculated values per data source. The algorithm (Table 4.7) first initializes merged network KC. Then for each attribute kc.a, it finds the most probable value among all given references ki within cluster c(line 3). When the algorithm unfolds, KC represents a merged dataset with resolved entities and eliminated redundancy.
In Fig.4.11, we show example of data integration execution. First part represents input data in form of networks from three different data sources. Secondly, the result of entity resolution contains merged network in which some nodes contain more values (from each data source). Lastly, after redundancy elimination step, the final result contains a cleaned network and the most appropriate value for each node.
Table 4.7 Redundancy elimination algorithm Redundancy elimination algorithm 1 Initialize merged cluster nodesKC
2 forc∈Canda∈Ado
4.7 Summary
Many medical applications and current ongoing medical research depend on text mining techniques. A lot of research work has already been done, and therefore in this chapter, we have overviewed some methods that enable researchers to auto-matically retrieve, extract, and integrate unstructured medical data. Due to increas-ing number of unstructured documents, the automatic text minincreas-ing methods ease access to relevant data, already conducted research along with its results, and save money by trying to eliminate repeated research experiments.
data
Fig. 4.11 Example of data integration execution on person domain
In the last decade, the text mining field has been generally fast evolving, and still, there is a lot of research to be done. In information retrieval, biomedical language resources typically use simple query models, which seem sufficient when enough of relevant data is extracted. Information extraction is currently receiving a lot of attention because researchers are trying to adapt techniques from other domains to work on biomedical data. Further, these techniques are essentials for automatic research texts processing and extraction of findings from research literature. Lastly, also very important topic of data integration still needs to improve models to merge data and select representative values. The latter is especially important as a reference to the same entity can be represented using many different forms.
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