## 1 Introduction: Name Disambiguation and Collocation
### Why undisambiguated names are a problem  
[PERSON] and [PERSON] state, \"For any work of literature, a fundamental issue is to identify the individual(s) who wrote it, and conversely, to identify all of the works that belong to a given individual\" [4]. This identification becomes ambiguous when more than one person shares a name, and those people are represented in a database. For example, if you do a search in Google for [PERSON], you will retrieve results that are about [PERSON] the American author, a [PERSON] who is a lawyer, and a [PERSON] who was a nineteenth-century banker. Still, search engines such as Google are unable to effectively group its search results by the individuals; they present the names all together, and the filtering is left to the searcher.  
In scholarly databases, such as citation and abstracting and indexing databases, forenames are often represented by initials, a practice that increases the name disambiguation problem. Whereas a search engine can easily perceive the difference between \"[PERSON], [PERSON],\" and \"[PERSON], [PERSON],\" without human intervention or special programming, most search software will be unable to differentiate between these two names when they are represented only as \"[PERSON], [PERSON]\" and \"[PERSON], M.\" Many style guides prescribe using only initials for forenames. For example the _Publication Manual of the American Psychological Association_ says, \"Invert all authors' names; give surnames and initials for only and up to including six authors\" [5]. The popular database WorldCat.org buys much of its article metadata from the British Library; the product is called British Library Serials, and virtually all of the name metadata in the product uses only initials for forenames. British Library Serials contains tens of millions of records with this abbreviated data.  
One of the problems with abbreviated forenames occurs not only with disambiguating names in search results but in the searching itself. In large databases populated with name metadata that includes only initials, searches on a fuller form of the name may not match on documents or metadata that only contains the abbreviated form. For example, a natural-language search on \"[PERSON], [PERSON]\" doesn't match metadata recorded as \"[PERSON]\" and metadata and documents that only contain the shortened form of the name may not be retrieved in the search. Some metadata databases, such as ISI Web ofKnowledge, convert all searches using the full form of the name to the shortened form. For example, an author search on \"[PERSON], [PERSON]\" is searched by the system as \"[PERSON], [PERSON]*\" where the asterisk indicates truncation. This procedure has the advantage of including more of the author's works in the search results, but at the expense of including works by other authors whose names also begin with \"[PERSON]\" in the results. This procedure will not work in full-text databases such as Google, because the system searches full-text and not metadata.  
In information retrieval, search precision is the proportion of relevant items retrieved to the total number of items retrieved in the search. In the context of name searches, this means the proportion of documents that are by or about the particular person the searcher had in mind to the total number of documents in the search. In some databases, such as in some high-quality abstracting and indexing databases that search only metadata created by humans, precision can often approach or equal 1.0. In full-text databases that rely on word matching, the value is much lower.  
Large digital libraries are making research that incorporates data mining possible. Searches for names run against large textual corpora frequently yield voluminous results with low precision. Any system that can disambiguate names in large textual corpora will significantly facilitate research.