Abstract:

The expansion of ontology resource is one of the key for the whole natural language processing. Since the information obtained traditionally from single data source could not reflect the overall picture and the coverage rate doesn’t reach targeted one, the construction of an integrated data management platform would be required to store and organize data sources by classification. The AVP data platform was  proposed firstly. In the process of data construction on AVP platform, the most important issue is to integrate multi-source data, in other words, to perform semantic role labeling on web data coming from different sources, to identify ambiguous entries, and to eventually merge into data warehouses which use sense as the basic unit. An automated method of semantic role matching has been suggested, and it would solve the problem of semantic role matching resulted from multi-source data fusion. The basic idea is to use attribute-values of entries as the feature template, and then apply expand vector space model to identity ambiguity for entries while assisted by the cooccurrence probability of attribute values. Through the massive experimental contrast, the system mentioned above performed very well in all respects. The theory and algorithm proposed in this paper could solve the problem of semantic role matching existed in multi-source data fusion effectively.

Key words: natural language processing; ontology; multisource data fusion; semantic role matching