Combining Data-Driven and Semantic Approaches for Text Mining

While the amount of structured data published on the Web keeps growing (fostered in particular by the Linked Open Data initiative), the Web still comprises of mainly unstructured—in particular textual—content and is therefore a Web for human consumption. Thus, an important question is which techniques are most suitable to enable people to effectively access the large body of unstructured information available on the Web, whether it is semantic or not. While the hope is that semantic technologies can be combined with standard Information Retrieval approaches to enable more accurate retrieval, some researchers have argued against this view. They claim that only data-driven or inductive approaches are applicable to tasks requiring the organization of unstructured (mainly textual) data for retrieval purposes. We argue that the dichotomy between data-driven/inductive and semantic approaches is indeed a false one. We further argue that bottom-up or inductive approaches can be successfully combined with top-down or semantic approaches and illustrate this for a number of tasks such as Ontology Learning, Information Retrieval, Information Extraction and Text Mining.