CIKM 2016

Document Filtering for Long-tail Entities

Filtering relevant documents with respect to entities is an essential task in the context of knowledge base construction and maintenance. It entails processing a time-ordered stream of documents that might be relevant to an entity in order to select only those that contain vital information. State-of-the-art approaches to document filtering for popular entities are entity-dependent: they rely on and are also trained on the specifics of differentiating features for each specific entity. Moreover, these approaches tend to use so-called extrinsic information such as Wikipedia page views and related entities which is typically only available only for popular head entities. Entity-dependent approaches based on such signals are therefore ill-suited as filtering methods for long-tail entities.

In this paper we propose a document filtering method for long-tail entities that is entity-independent and thus also generalizes to unseen or rarely seen entities. It is based on intrinsic features, i.e., features that are derived from the documents in which the entities are mentioned. We propose a set of features that capture informativeness, entity-saliency, and timeliness. In particular, we introduce features based on entity aspect similarities, relation patterns, and temporal expressions and combine these with standard features for document filtering.

Experiments following the TREC KBA 2014 setup on a publicly available dataset show that our model is able to improve the filtering performance for long-tail entities over several baselines. Results of applying the model to unseen entities are promising, indicating that the model is able to learn the general characteristics of a vital document. The overall performance across all entities–i.e., not just long-tail entities–improves upon the state-of-the-art without depending on any entity-specific training data.

  • [PDF] R. Reinanda, E. Meij, and M. de Rijke, “Document filtering for long-tail entities,” in Cikm 2016: 25th acm conference on information and knowledge management, 2016.
    [Bibtex]
    @inproceedings{CIKM:2016:Reinanda,
    Author = {Reinanda, Ridho and Meij, Edgar and de Rijke, Maarten},
    Booktitle = {CIKM 2016: 25th ACM Conference on Information and Knowledge Management},
    Date-Added = {2016-09-05 18:55:21 +0000},
    Date-Modified = {2016-09-05 19:00:33 +0000},
    Month = {October},
    Publisher = {ACM},
    Title = {Document filtering for long-tail entities},
    Year = {2016}}
WSDM

Dynamic Collective Entity Representations for Entity Ranking

Entity ranking, i.e., successfully positioning a relevant entity at the top of the ranking for a given query, is inherently difficult due to the potential mismatch between the entity’s description in a knowledge base, and the way people refer to the entity when searching for it. To counter this issue we propose a method for constructing dynamic collective entity representations. We collect entity descriptions from a variety of sources and combine them into a single entity representation by learning to weight the content from different sources that are associated with an entity for optimal retrieval effectiveness. Our method is able to add new descriptions in real time and learn the best representation as time evolves so as to capture the dynamics of how people search entities. Incorporating dynamic description sources into dynamic collective entity representations improves retrieval effectiveness by 7% over a state-of-the-art learning to rank baseline. Periodic retraining of the ranker enables higher ranking effectiveness for dynamic collective entity representations.

  • [PDF] D. Graus, M. Tsagkias, W. Weerkamp, E. Meij, and M. de Rijke, “Dynamic collective entity representations for entity ranking,” in Proceedings of the ninth acm international conference on web search and data mining, 2016.
    [Bibtex]
    @inproceedings{WSDM:2016:Graus,
    Author = {Graus, David and Tsagkias, Manos and Weerkamp, Wouter and Meij, Edgar and de Rijke, Maarten},
    Booktitle = {Proceedings of the ninth ACM international conference on Web search and data mining},
    Date-Added = {2016-01-07 17:24:16 +0000},
    Date-Modified = {2016-01-07 17:25:55 +0000},
    Series = {WSDM 2016},
    Title = {Dynamic Collective Entity Representations for Entity Ranking},
    Year = {2016},
    Bdsk-Url-1 = {http://aclweb.org/anthology/P15-1055}}

Learning to Explain Entity Relationships in Knowledge Graphs

We study the problem of explaining relationships between pairs of knowledge graph entities with human-readable descriptions. Our method extracts and enriches sentences that refer to an entity pair from a corpus and ranks the sentences according to how well they describe the relationship between the entities. We model this task as a learning to rank problem for sentences and employ a rich set of features. When evaluated on a large set of manually annotated sentences, we find that our method significantly improves over state-of-the-art baseline models.

  • [PDF] N. Voskarides, E. Meij, M. Tsagkias, M. de Rijke, and W. Weerkamp, “Learning to explain entity relationships in knowledge graphs,” in Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), 2015, pp. 564-574.
    [Bibtex]
    @inproceedings{ACL:2015:Voskarides,
    Author = {Voskarides, Nikos and Meij, Edgar and Tsagkias, Manos and de Rijke, Maarten and Weerkamp, Wouter},
    Booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
    Date-Added = {2015-08-06 13:08:02 +0000},
    Date-Modified = {2015-08-06 13:08:14 +0000},
    Location = {Beijing, China},
    Pages = {564--574},
    Publisher = {Association for Computational Linguistics},
    Title = {Learning to Explain Entity Relationships in Knowledge Graphs},
    Url = {http://aclweb.org/anthology/P15-1055},
    Year = {2015},
    Bdsk-Url-1 = {http://aclweb.org/anthology/P15-1055}}

Fast and Space-Efficient Entity Linking in Queries

Entity linking deals with identifying entities from a knowledge base in a given piece of text and has become a fundamental building block for web search engines, enabling numerous downstream improvements from better document ranking to enhanced search results pages. A key problem in the context of web search queries is that this process needs to run under severe time constraints as it has to be performed before any actual retrieval takes place, typically within milliseconds. In this paper we propose a probabilistic model that leverages user-generated information on the web to link queries to entities in a knowledge base. There are three key ingredients that make the algorithm fast and space-efficient. First, the linking process ignores any dependencies between the different entity candidates, which allows for a O(k^2) implementation in the number of query terms. Second, we leverage hashing and compression techniques to reduce the memory footprint. Finally, to equip the algorithm with contextual knowledge without sacrificing speed, we factor the distance between distributional semantics of the query words and entities into the model. We show that our solution significantly outperforms several state-of-the-art baselines by more than 14% while being able to process queries in sub-millisecond times—at least two orders of magnitude faster than existing systems.

  • [PDF] R. Blanco, G. Ottaviano, and E. Meij, “Fast and space-efficient entity linking in queries,” in Proceedings of the eighth acm international conference on web search and data mining, 2015.
    [Bibtex]
    @inproceedings{WSDM:2015:blanco,
    Author = {Blanco, Roi and Ottaviano, Giuseppe and Meij, Edgar},
    Booktitle = {Proceedings of the eighth ACM international conference on Web search and data mining},
    Date-Added = {2011-10-26 11:21:51 +0200},
    Date-Modified = {2015-01-20 20:29:19 +0000},
    Series = {WSDM 2015},
    Title = {Fast and Space-Efficient Entity Linking in Queries},
    Year = {2015},
    Bdsk-Url-1 = {http://doi.acm.org/10.1145/1935826.1935842}}

Linking queries to entities

I’m happy to announce we’re releasing a new test collection for entity linking for web queries (within user sessions) to Wikipedia. About half of the queries in this dataset are sampled from Yahoo search logs, the other half comes from the TREC Session track. Check out the L24 dataset on Yahoo Webscope, or drop me a line for more information. Below you’ll find an excerpt of the README text associated with it.

With this dataset you can train, test, and benchmark entity linking systems on the task of linking web search queries – within the context of a search session – to entities. Entities are a key enabling component for semantic search, as many information needs can be answered by returning a list of entities, their properties, and/or their relations. A first step in any such scenario is to determine which entities appear in a query – a process commonly referred to as named entity resolution, named entity disambiguation, or semantic linking.

This dataset allows researchers and other practitioners to evaluate their systems for linking web search engine queries to entities. The dataset contains manually identified links to entities in the form of Wikipedia articles and provides the means to train, test, and benchmark such systems using manually created, gold standard data. With releasing this dataset publicly, we aim to foster research into entity linking systems for web search queries. To this end, we also include sessions and queries from the TREC Session track (years 2010–2013). Moreover, since the linked entities are aligned with a specific part of each query (a “span”), this data can also be used to evaluate systems that identify spans in queries, i.e, that perform query segmentation for web search queries, in the context of search sessions.

The key properties of the dataset are as follows.

  • Queries are taken from Yahoo US Web Search and from the TREC Session track (2010-2013).
  • There are 2635 queries in 980 sessions, 7482 spans, and 5964 links to Wikipedia articles in this dataset.
  • The annotations include the part of the query (the “span”) that is linked to each Wikipedia article. This information can also be used for query segmentation experiments.
  • The annotators have identified the “main” entity/ies for each query, if available.
  • The annotators also labeled the queries, identifying whether they are non-English, navigational, quote-or-question, adult, or ambiguous and also if an out-of-Wikipedia entity is mentioned in the query, i.e., when an entity is mentioned in a query but no suitable Wikipedia article exists.
  • The file includes session information: each session consists of an anonymized id, initial query, as well as all the queries issued within the same session and their relative date/timestamp if available.
  • Sessions are demarcated using a 30 minute time-out.

Entity Linking and Retrieval Tutorial @ SIGIR 2013 – Slides, Code, and Bibliography

The material for our “Entity Linking and Retrieval” tutorial (with Krisztian Balog and Daan Odijk) for SIGIR 2013 has been updated and is available online on GitHub (slides), Dropbox (slides), Mendeley, and CodeAcademy. All material is summarized at the webpage for the tutorial: http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/. See my other blogpost for a brief summary.

Semantic TED

Multilingual Semantic Linking for Video Streams: Making “Ideas Worth Sharing” More Accessible

Semantic TEDThis paper describes our (winning!) submission to the Developers Challenge at WoLE2013, “Doing Good by Linking Entities.” We present a fully automatic system – called “Semantic TED” – which provides intelligent suggestions in the form of links to Wikipedia articles for video streams in multiple languages, based on the subtitles that accompany the visual content. The system is applied to online conference talks. In particular, we adapt a recently proposed semantic linking approach for streams of television broadcasts to facilitate generating contextual links while a TED talk is being viewed. TED is a highly popular global conference series covering many research domains; the publicly available talks have accumulated a total view count of over one billion at the time of writing. We exploit the multi-linguality of Wikipedia and the TED subtitles to provide contextual suggestions in the language of the user watching a video. In this way, a vast source of educational and intellectual content is disclosed to a broad audience that might otherwise experience difficulties interpreting it.

  • [PDF] D. Odijk, E. Meij, D. Graus, and T. Kenter, “Multilingual semantic linking for video streams: making "ideas worth sharing" more accessible,” in Proceedings of the 2nd international workshop on web of linked entities (wole 2013), 2013.
    [Bibtex]
    @inproceedings{WOLE:2013:Odijk,
    Author = {Odijk, Daan and Meij, Edgar and Graus, David and Kenter, Tom},
    Booktitle = {Proceedings of the 2nd International Workshop on Web of Linked Entities (WoLE 2013)},
    Date-Added = {2013-05-15 14:09:58 +0000},
    Date-Modified = {2013-05-15 14:11:37 +0000},
    Title = {Multilingual Semantic Linking for Video Streams: Making "Ideas Worth Sharing" More Accessible},
    Year = {2013}}
Example entity linking for tweets, to support tweets summarization

Personalized Time-Aware Tweets Summarization

To appear as full paper at SIGIR 2013.

In this paper we focus on selecting meaningful tweets given a user’s interests. Specifically, we consider the task of time-aware tweets summarization, based on a user’s history and collaborative social influences from “social circles.” Continue reading “Personalized Time-Aware Tweets Summarization” »