Precision in processing data from heterogeneous resources

Much information is becoming available on the world-wide-web, on Intranets, and on publicly accessible databases. The benefits of integrating related data from distinct sources are great, since it allows the discovery or validation of relationships among events and trends in many areas of science and commerce. But most sources are established autonomously, and hence are heterogeneous in form and content. Resolution of heterogeneity of form has been an exciting research topic for many years now. We can access information from diverse computers, alternate data representations, varied operating systems, multiple database models, and deal with a variety of transmission protocols. But progress in these areas is raising a new problem: semantic heterogeneity. Semantic heterogeneity comes about because the meaning of words depends on context, and autonomous sources are developed and maintained within their own contexts. Types of semantic heterogeneity include spelling variations, use of synonyms, and the use of identically spelled words to refer to different objects. The effect of semantic heterogeneity is not only failure to find desired material, but also lack of precision in selection, aggregation, comparison, etc., when trying to integrate information. While browsing we may complain of ‘information overload’. But when trying to automate these processes, an essential aspect of business-oriented operations, the imprecision due to semantic heterogeneity can be become fatal. Manual resolutions to the problem do work today, but it forces businesses to limit the scope of their partnering. In expanding supply chains and globalized commerce we have to deal in many more contexts, but cannot afford manual, case-by-case resolution. In business we become efficient by rapidly carrying out processes on regular schedules. XML is touted as the new universal medium for electronic commerce, but the meaning of the tags identifying data fields remains context dependent. Attempting a global resolution of the semantic mismatch is futile. The number of participants is immense, growing, and dynamic. Terminology changes, and must be able to change as our knowledge grows. Using precise, finely differentiated terms and abbreviations is important for efficiency within a domain, but frustrating to outsiders. In this paper we indicate research directions to resolve inconsistencies incrementally, so that we may be able to interoperate effectively in the presence of inter-domain inconsistencies. This work is an early stage, and will provide research opportunities for a range of disciplines, including databases, artificial intelligence, and formal linguistics. We also sketch an information systems architecture which is suitable for such services and their infrastructure. Research issues in managing complexity of multiple services arise here as well. The conclusion of this paper can be summarized as stating that today, and even more in the future, precision and relevance will be more valuable than completeness and recall. Solutions are best composed from many small-scale efforts rather than by overbearing attempts at standardization. This observation will, in turn, affect research directions in information sciences.