BACKGROUND

The invention relates to the field of computerized information retrieval.

Information retrieval systems typically operate by receiving a query and searching for pertinent information in a corpus of electronic data—be it text, images, audio, video, or any other type of data. For example, Web search engines receive user queries and search for relevant information in a centralized index of the World Wide Web, maintained by each search engine. Similarly, organizational information retrieval systems conduct searches in an index of the organization's internal collection of electronic documents and other data.

To improve the relevancy of retrieved information, many information retrieval systems invoke a task called “query expansion” before returning search results. While some query expansion techniques directly improve the query, such as by correcting spelling errors and adding synonyms, others perform more complex computations in order to enhance the relevancy of the retrieved information to the query. Of the latter, a technique called “pseudo relevance feedback” (or sometimes “blind relevance feedback”) executes an initial search with the original query, retrieves a ranked list of documents, assumes that the top-k ranked documents are generally relevant, and re-ranks these top-k documents based on some logic that is believed to better suit the user's information needs.

A specific task within the rich realm of information retrieval is table retrieval. Data contained in tables is often of high quality, and enabling information retrieval systems to effectively search such tabular data is of great benefit. In many scenarios, a user's information needs can be better satisfied by retrieving information contained in tables than by retrieving information contained in unstructured text documents. One of the fundamental types of table retrieval, termed “ad-hoc table retrieval,” involves, for a given query in natural language, returning a ranked list of the most relevant tables found in the pertinent corpus.

The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.

SUMMARY

The following embodiments and aspects thereof are described and illustrated in conjunction with systems, tools and methods which are meant to be exemplary and illustrative, not limiting in scope.

One embodiment is directed to a method comprising, in a computerized information retrieval system, the following automatic steps: receiving a query; executing a search based on the query, to retrieve a set of tables ranked according to their relevancy to the query, wherein each of the tables includes one or more columns; selecting, from the set of tables, a predefined number of highest-ranking tables; scoring each column in the highest-ranking tables using a link analysis algorithm, and selecting, from the scored columns, a predefined number of highest-scoring columns; scoring terms contained within each of the highest-scoring columns, and selecting, from the scored terms, a predefined number of highest-scoring terms; re-ranking the highest-ranking tables by using the highest-scoring terms as pseudo relevance feedback that expands the query; and providing, as a response to the query, at least one of: (a) the re-ranked tables, ordered according to the re-ranking, and (b) data contained in at least one of the re-ranked tables, wherein the data are ordered according to the re-ranking.

Another embodiment relates to a system comprising: at least one hardware processor, and a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by said at least one hardware processor to, automatically: receive a query; execute a search based on the query, to retrieve a set of tables ranked according to their relevancy to the query, wherein each of the tables includes one or more columns; select, from the set of tables, a predefined number of highest-ranking tables; score each column in the highest-ranking tables using a link analysis algorithm, and select, from the scored columns, a predefined number of highest-scoring columns; score terms contained within each of the highest-scoring columns, and select, from the scored terms, a predefined number of highest-scoring terms; re-rank the highest-ranking tables by using the highest-scoring terms as pseudo relevance feedback that expands the query; and provide, as a response to the query, at least one of: (i) the re-ranked tables, ordered according to the re-ranking, and (ii) data contained in at least one of the re-ranked tables, wherein the data are ordered according to the re-ranking.

A further embodiment relates to a computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to, automatically: receive a query; execute a search based on the query, to retrieve a set of tables ranked according to their relevancy to the query, wherein each of the tables includes one or more columns; select, from the set of tables, a predefined number of highest-ranking tables; score each column in the highest-ranking tables using a link analysis algorithm, and select, from the scored columns, a predefined number of highest-scoring columns; score terms contained within each of the highest-scoring columns, and select, from the scored terms, a predefined number of highest-scoring terms; re-rank the highest-ranking tables by using the highest-scoring terms as pseudo relevance feedback that expands the query; and provide, as a response to the query, at least one of: (a) the re-ranked tables, ordered according to the re-ranking, and (b) data contained in at least one of the re-ranked tables, wherein the data are ordered according to the re-ranking.

In some embodiments, the method further comprises, or the program code is further executable for: calculating content similarity between each of the highest-ranking tables and each of the columns of the highest-ranking tables, wherein the calculated content similarities are factored in the scoring of the columns of the highest-ranking tables.

In some embodiments, the link analysis algorithm is the Hyperlink-Induced Topic Search (HITS) algorithm; and in the scoring of the columns of the highest-ranking tables: the highest-ranking tables are represented as hubs, the columns of the highest-ranking tables are represented as authorities, hub weights and authority weights are iteratively updated while factoring in the calculated content similarities, and the scoring of the columns of the highest-ranking tables is based on the authority weights upon convergence of the HITS algorithm.

In some embodiments, the content similarity is calculated using a technique selected from the group consisting of: Bhattacharyya coefficient; Term Frequency-Inverse Document Frequency (TF-IDF); Pointwise Mutual Information (PMI); and Kullback-Leibler divergence.

In some embodiments, the scoring of the terms comprises scoring each of the terms according to its similarity to the query.

In some embodiments, the similarity of each of the terms to the query is evaluated using a technique selected from the group consisting of: Bhattacharyya coefficient; Term Frequency-Inverse Document Frequency (TF-IDF); Pointwise Mutual Information (PMI); and Kullback-Leibler divergence.

In some embodiments, the re-ranking is based on: cross-entropy between each of at least some of the terms and each of the highest-ranking tables; and cross-entropy between each of at least some of the terms and the query.

In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the figures and by study of the following detailed description.

