Automatically linking text to concepts in a knowledge base

Patent metadata

Field	Value
Publication number	US-2016012336-A1
Application number	US-201514657343-A
Country	US
Kind code	A1
Filing date	Mar 13, 2015
Priority date	Jul 14, 2014
Publication date	Jan 14, 2016
Grant date	—

Abstract

According to an aspect, automatically linking text to concepts in a knowledge base using differential analysis includes receiving a text string and selecting, based on contents of the text string, a plurality of data sources that correspond to concepts in the knowledge base. In a further aspect, automatically linking the text to the concepts includes calculating, for each of the selected data sources, a probability that the text string is output by a language model built using the selected data source, calculating a probability that the text string is output by a generic language model, calculating link confidence scores for each concept based on a differential analysis of the probabilities, and creating a link from the text string to one of the concepts in the knowledge base. The creating is based on a link confidence score of the concept being more than a threshold value away from a prescribed threshold.

First claim

What is claimed is:

1 . A method for automatically linking text to concepts in a knowledge base using a differential analysis, the method comprising:
receiving a text string; selecting a plurality of data sources that correspond to concepts in the knowledge base, the selecting based on contents of the text string; calculating, for each of the selected data sources, a probability that the text string is output by a language model built using the selected data source; calculating a probability that the text string is output by a generic language model; calculating link confidence scores for each concept based on a differential analysis of the probabilities; and creating a link from the text string to one of the concepts in the knowledge base, the creating based on a link confidence score of the concept being more than a threshold value away from a prescribed threshold.

2 . The method of claim 1 , wherein the differential analysis compares the probability that the text string is output by a language model built using a data source to the probability that the text string is output by a generic language model.

3 . The method of claim 1 , wherein the differential analysis compares the probability that the text string is output by a language model built using a data source to a probability that the text string is output by a language model built using a competing data source.

4 . The method of claim 1 , wherein the generic language model is derived from a generic data source not specific to any of the concepts in the knowledge base.

5 . The method of claim 1 , wherein the calculating link confidence scores includes comparing the probabilities to a probability that the text string is contained in a generic data source that is not associated with any of the concepts in the knowledge base.

6 . The method of claim 1 , wherein the text string is linked to a second one of the concepts in the knowledge base.

7 . The method of claim 1 , wherein the link applies to a subset of the text string and the subset is indicated in the link.

8 . The method of claim 7 , wherein words in the subset are not consecutive in the text string.

9 . The method of claim 1 , wherein the text string is one of a collection of words, a sentence, a paragraph, and a whole document.

10 . The method of claim 1 , wherein each of the selected data sources includes one or more collection of names for the corresponding concept, a description for the corresponding concept, sentences referring to the corresponding concept, and paragraphs referring to the corresponding concept.

Assignees

• IBM

Inventors

• Franceschini Michele M
• Lastras-Montano Luis A
• Soares Livio B
• Wegman Mark N

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06F40/134

  Hyperlinking · CPC title

• G06F16/313 Primary

  Selection or weighting of terms for indexing · CPC title

• G06F16/3334

  Selection or weighting of terms from queries, including natural language queries · CPC title

• G06F16/3346

  using probabilistic model · CPC title