Notes Explorer: Toward Structured Retrieval in Semi-structured Information Spaces

Proceedings of IJCAI, the International Joint Conference in Artificial Intelligence. 1997
Notes Explorer: Toward Structured Retrieval in Semi-structured Information Spaces
Abstract

A semi-structured information space consists of multiple collections of textual documents containing fielded or tagged sections. The space can be highly heterogeneous, because each collection has its own schema, and there are no enforced keys or formats for data items across collections.

Thus, structured methods like SQL cannot be easily employed, and users often must make do with only full-text search. In this paper, we describe an intermediate approach that provides structured querying for particular types of entities, such as companies, people, and skills.

Entity-based retrieval is enabled by normalizing entity references in a heuristic, type-dependent manner. To organize and filter search results, entities are categorized as playing particular roles (e.g., company as client, as vendor, etc.) in particular collection types (directories, client engagement records, etc.).

The approach can be used to retrieve documents and can also be used to construct entity profiles - summaries of commonly sought information about an entity based on the documents’ content. The approach requires only a modest amount of meta-information about the source collections, much of which is derived automatically. On a set of typical user queries in a large corporate information space, the approach produces a dramatic improvement in retrieval quality over knowledge-free methods like full-text search.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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