IntentsKB: A Knowledge Base of Entity-Oriented Search Intents
This repository provides resources developed within the following article:
D. Garigliotti and K. Balog. IntentsKB: A Knowledge Base of Entity-Oriented Search Intents. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM '18). ACM. Turin, Italy. October 2018. DOI: 10.1145/3269206.3269257
You can get the author version of the article here.
Abstract
We address the problem of constructing a knowledge base of entity-oriented search intents. Search intents are defined on the level of entity types, each comprising of a high-level intent category (property, website, service, or other), along with a cluster of query terms used to express that intent. These machine-readable statements can be leveraged in various applications, e.g., for generating entity cards or query recommendations. By structuring service-oriented search intents, we take one step towards making entities actionable. The main contribution of this paper is a pipeline of components we develop to construct a knowledge base of entity intents. We evaluate performance both component-wise and end-to-end, and demonstrate that our approach is able to generate high-quality data.
1. Refiner Acquisition
Data obtained in the refiners acquisition pipeline stage (Sect. 5.1 of the paper) are placed under the directory data/1-refiners_acquisition/
.
- A tab-separated file
1-top_entities_per_type/<type>/entity_pageviews.tsv
contains the top 1,000- most popular entities of the Freebase type<type>
. Each row in the file is in the format:
<entity> <pageviews>
- After decompressing
2-API_query_suggestions.tar.gz
(into a directory of 1.3 GB of size), a JSON file2-API_query_suggestions/<type>/<i>.json
contains the API query suggestions obtained for the(i+1)
-th entity in the entities file of the type<type>
(i
>= 0). The content is of the shape:
{
"<i>": <list of API query suggestions>
}
- A tab-separated file
3-type_level_query_patterns/<type>.tsv
contains the type-level query patterns for type<type>
, decreasingly sorted by frequency (total number of API query suggestions for entities in<type>
). Each row in the file is in the format:
<type-level query pattern> <frequency>
2. Refiner Categorization
Intent categories predicted in the refiners categorization pipeline stage (Sect. 5.2 of the paper) are contained in the tab-separated file data/2-refiners_categorization/predictions.tsv
. Each row in the file is of the shape:
<type> <refiner> <category>
Search results
Additionally, the results of searching for entity-bearing queries with Google Custom Search Engine (CSE) (Sect. 4.2 of the paper) are contained under 2 compressed directories (freq_{5_to_9,10+}.tar.gz
), shared in this link. Once decompressed:
- A file
cse_results-freq_10+/<type>/query_<i>.json
contains the JSON response after searching for the actual entity-bearing query that results from instantiating the(i+1)
-th type-level query pattern with the most prominent entity of that type. (Such a pattern is the one in the(i+1)
-th row ofdata/1-refiners_acquisition/3-type_level_query_patterns/<type>.tsv
.) - Analogously, a file
cse_results-freq_5_to_9/<type>/query_<i>.json
contains the JSON responses for less frequent type-level refiners. Specifically, it contains the response for the(i+1)
-th type-level query pattern, considering only the patterns with frequency in the range[5..9]
(i.e., the pattern in the(i + N_<type> + 1)
-th row ofdata/1-refiners_acquisition/3-type_level_query_patterns/<type>.tsv
, withN_<type>
being the number of query patterns for<type>
with frequency larger than 9).
Each JSON file is in this format:
{
"1_type": <type>,
"2_tl_q": <type-level query pattern>,
"3_ae_q": <actual entity-bearing query>,
"4_f": <frequency of type-level query pattern>,
"5_results": {
"items": <list of top-10 search results>,
...
}
}
Table of lexical features
This is the full list of lexical features for refiner categorization (Sect. 4.2.1 of the paper):
3. Intent Discovery
Entity-oriented search intents discovered in the intents discovery pipeline stage (Sect. 5.3 of the paper) are stored in the tab-separated file data/3-intents_discovery/clustering.tsv
. Each row in the file is in the format:
<type> <category> <cluster ID> <cluster label> <refiner> <similarity>
4. IntentsKB Knowledge Base
The final knowledge base (Sect. 5.4 of the paper) is stored in the tab-separated file intentsKB/quadruples.tsv
, which contains rows each of the shape as follows (Sect. 3 of the paper):
<intent ID> <predicate> <object> <confidence>
Here, <object>
is -
for the predicate isAProfile
(each meaning a quadruple that only states the confidence of the intent profile; cf. Eq. (1) of the paper).
Citation
If you use the resources presented in this repository, please cite:
@inproceedings{Garigliotti:2018:IKB,
author = {Dar\'{i}o Garigliotti and Krisztian Balog},
title = {IntentsKB: A Knowledge Base of Entity-Oriented Search Intents},
booktitle = {Proceedings of the 27th ACM International Conference on Information and Knowledge Management},
series = {CIKM '18},
year = {2018},
pages = {1659--1662},
doi = {10.1145/3269206.3269257},
publisher = {ACM}
}
Contact
Should you have any questions, please contact Darío Garigliotti at dario.garigliotti[AT]uis.no (with [AT] replaced by @).