## 5 ETOE Enrichment and Correlation  
ETOEs extracted by the GATE component of the ARCOMEM offline analysis depicted in Figure 1 provide an initial classification and structure for the crawled web documents, for instance, the association of terms with entity types defined in the ARCOMEM data model. However, as the content analysis extracts structured data from unstructured resources, such as text and images, the generated data is: (i) heterogeneous, _i.e._, not well interlinked; (ii) ambiguous; and (iii) provides only very limited information. This is due to the data being generated by different components and during independent processing cycles. Data enrichment and consolidation (achieved by the linked data enrichment and consolidation component in Figure 1) follows three aims: (1) enrich existing entities with related publicly-available knowledge; (2) disambiguation; and (3) identify data correlations, such as the ones above, by aligning ARCOMEM entities with reference datasets. Both (1) and (2) exploit publicly available data from the Linked Open Data Cloud [20], which offers a vast amount of data of both a domain-specific and domain-independent nature (the current release of the LOD Cloud consists of more than 31 billion distinct triples, _i.e._, RDF statements). The ETOE enrichment approach [2] first identifies correlating enrichments from reference datasets, which are associated with the respective entities and, secondly, uses these shared enrichments to identify correlating entities in the ARCOMEM KB. In particular, the enrichment approach of ARCOMEM uses DBpedia [3] and Freebase [4] as reference datasets. **Named Entity Disambiguation:** An important aspect of ETOE enrichment and correlation is the disambiguation of named entities in the face of multiple enrichment candidates within a knowledge base. Whereas the DBpedia Spotlight service [21] used in our enrichment process ranks enrichment candidates based on their popularity within the DBpedia graph, such a ranking is independent of the particular entity context and does not resolve the problem of entity ambiguity. In the context of Freebase, structured queries used for enrichment do not solve this problem either as they return a set of all potential enrichment candidates. The disambiguation problem occurs whenever a named entity corresponds to more than one resource within the knowledge base. For example, the most popular entity corresponding to \"[PERSON]\" in DBpedia is an English football manager and former player: \"[[http://dbpedia.org/page/[PERSON]_](http://dbpedia.org/page/[PERSON]_)]([http://dbpedia.org/page/[PERSON]_](http://dbpedia.org/page/[PERSON]_))(footballer)\". However, in the context of the U.S. Elections 2012 dataset, the entity \"[PERSON]\" is most likely referring to an American politician: \"[[http://dbpedia.org/page/[PERSON]](http://dbpedia.org/page/[PERSON])]([http://dbpedia.org/page/[PERSON]](http://dbpedia.org/page/[PERSON]))\". Another example is the location \"Athens\", which typically corresponds to the capital of Greece. However, in the context of the U.S. Elections 2012, it most likely referring to one of the locations in U.S., such as Athens, Georgia, or Athens, Ohio. In order to decide which knowledge base resource from a list of candidates should be used for semantic enrichment of a particular named entity, we take the similarity of the entity contexts in both sources, the web resource and the knowledge base, into account. Other work on this topic has been done using Wikipedia as a resource, such as the famous learning to link approach by [22]. However, this method requires interlinked description of related entities, which are currently not available in linked data knowledge bases. In the ARCOMEM project, we implemented a context similarity computation for DBpedia. To this extent, we represent the contexts of entities in web resources and enrichment candidates in DBpedia as context vectors.