Weakly-supervised Contextualization of Knowledge Graph Facts

Knowledge graphs (KGs) model facts about the world; they consist of nodes (entities such as companies and people) that are connected by edges (relations such as founderOf ). Facts encoded in KGs are frequently used by search applications to augment result pages. When presenting a KG fact to the user, providing other facts that are pertinent to that main fact can enrich the user experience and support exploratory information needs. KG fact contextualization is the task of augmenting a given KG fact with additional and useful KG facts. The task is challenging because of the large size of KGs; discovering other relevant facts even in a small neighborhood of the given fact results in an enormous amount of candidates. We introduce a neural fact contextualization method (NFCM) to address the KG fact contextualization task. NFCM first generates a set of candidate facts in the neighborhood of a given fact and then ranks the candidate facts using a supervised learning to rank model. The ranking model combines features that we automatically learn from data and that represent the query-candidate facts with a set of hand-crafted features we devised or adjusted for this task. In order to obtain the annotations required to train the learning to rank model at scale, we generate training data automatically using distant supervision on a large entity-tagged text corpus. We show that ranking functions learned on this data are effective at contextualizing KG facts. Evaluation using human assessors shows that it significantly outperforms several competitive baselines.

  • [PDF] [DOI] N. Voskarides, E. Meij, R. Reinanda, A. Khaitan, M. Osborne, G. Stefanoni, P. Kambadur, and M. de Rijke, “Weakly-supervised contextualization of knowledge graph facts,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, New York, NY, USA, 2018, p. 765–774.
    [Bibtex]
    @inproceedings{SIGIR:2018:Voskarides,
    Acmid = {3210031},
    Address = {New York, NY, USA},
    Author = {Voskarides, Nikos and Meij, Edgar and Reinanda, Ridho and Khaitan, Abhinav and Osborne, Miles and Stefanoni, Giorgio and Kambadur, Prabhanjan and de Rijke, Maarten},
    Booktitle = {The 41st {International ACM SIGIR Conference on Research} \& {Development in Information Retrieval}},
    Date-Added = {2018-07-26 18:23:41 +0000},
    Date-Modified = {2018-09-27 21:55:17 +0100},
    Doi = {10.1145/3209978.3210031},
    Isbn = {978-1-4503-5657-2},
    Keywords = {distant supervision, fact contextualization, knowledge graphs},
    Location = {Ann Arbor, MI, USA},
    Numpages = {10},
    Pages = {765--774},
    Publisher = {ACM},
    Series = {SIGIR '18},
    Title = {Weakly-supervised Contextualization of Knowledge Graph Facts},
    Url = {http://doi.acm.org/10.1145/3209978.3210031},
    Year = {2018},
    Bdsk-Url-1 = {http://doi.acm.org/10.1145/3209978.3210031},
    Bdsk-Url-2 = {https://doi.org/10.1145/3209978.3210031}}

Utilizing Knowledge Graphs for Text-Centric Information Retrieval

The past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The depth and breadth of content in these KGs made them not only rich sources of structured knowledge by themselves, but also valuable resources for search systems. A surge of recent developments in entity linking and entity retrieval methods gave rise to a new line of research that aims at utilizing KGs for text-centric retrieval applications. This tutorial is the first to summarize and disseminate the progress in this emerging area to industry practitioners and researchers.

  • [PDF] [DOI] L. Dietz, A. Kotov, and E. Meij, “Utilizing knowledge graphs for text-centric information retrieval,” in The 41st international acm sigir conference on research & development in information retrieval, New York, NY, USA, 2018, p. 1387–1390.
    [Bibtex]
    @inproceedings{SIGIR:2018:Dietz-Tut,
    Acmid = {3210187},
    Address = {New York, NY, USA},
    Author = {Dietz, Laura and Kotov, Alexander and Meij, Edgar},
    Booktitle = {The 41st International ACM SIGIR Conference on Research \& Development in Information Retrieval},
    Date-Added = {2018-07-26 18:24:31 +0000},
    Date-Modified = {2018-07-26 18:31:50 +0000},
    Doi = {10.1145/3209978.3210187},
    Isbn = {978-1-4503-5657-2},
    Keywords = {entity linking, entity retrieval, information retrieval, knowledge graphs},
    Location = {Ann Arbor, MI, USA},
    Numpages = {4},
    Pages = {1387--1390},
    Publisher = {ACM},
    Series = {SIGIR '18},
    Title = {Utilizing Knowledge Graphs for Text-Centric Information Retrieval},
    Url = {http://doi.acm.org/10.1145/3209978.3210187},
    Year = {2018},
    Bdsk-Url-1 = {http://doi.acm.org/10.1145/3209978.3210187},
    Bdsk-Url-2 = {https://doi.org/10.1145/3209978.3210187}}
ECIR 2017

Generating descriptions of entity relationships

Large-scale knowledge graphs (KGs) store relationships between entities that are increasingly being used to improve the user experience in search applications. The structured nature of the data in KGs is typically not suitable to show to an end user and applications that utilize KGs therefore benefit from human-readable textual descriptions of KG relationships. We present a method that automatically generates textual descriptions of entity relationships by combining textual and KG information. Our method creates sentence templates for a particular relationship and then generates a textual description of a relationship instance by selecting the best template and filling it with appropriate entities. Experimental results show that a supervised variation of our method outperforms other variations as it captures the semantic similarity between a relationship instance and a template best, whilst providing more contextual information.

  • [PDF] N. Voskarides, E. Meij, and M. de Rijke, “Generating descriptions of entity relationships,” in Ecir 2017: 39th european conference on information retrieval, 2017.
    [Bibtex]
    @inproceedings{ECIR:2017:voskarides,
    Author = {Voskarides, Nikos and Meij, Edgar and de Rijke, Maarten},
    Booktitle = {ECIR 2017: 39th European Conference on Information Retrieval},
    Date-Added = {2017-01-10 21:27:37 +0000},
    Date-Modified = {2017-01-10 21:27:58 +0000},
    Month = {April},
    Publisher = {Springer},
    Series = {LNCS},
    Title = {Generating descriptions of entity relationships},
    Year = {2017}}
wsdm 2017

Utilizing Knowledge Bases in Text-centric Information Retrieval (WSDM 2017)

The past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The increasing depth and breadth of content in KGs makes them not only rich sources of structured knowledge by themselves but also valuable resources for search systems. A surge of recent developments in entity linking and retrieval methods gave rise to a new line of research that aims at utilizing KGs for text-centric retrieval applications, making this an ideal time to pause and report current findings to the community, summarizing successful approaches, and soliciting new ideas. This tutorial is the first to disseminate the progress in this emerging field to researchers and practitioners.

Utilizing Knowledge Bases in Text-centric Information Retrieval (ICTIR 2016)

General-purpose knowledge bases are increasingly growing in terms of depth (content) and width (coverage). Moreover, algorithms for entity linking and entity retrieval have improved tremendously in the past years. These developments give rise to a new line of research that exploits and combines these developments for the purposes of text-centric information retrieval applications. This tutorial focuses on a) how to retrieve a set of entities for an ad-hoc query, or more broadly, assessing relevance of KB elements for the information need, b) how to annotate text with such elements, and c) how to use this information to assess the relevance of text. We discuss different kinds of information available in a knowledge graph and how to leverage each most effectively.
Continue reading “Utilizing Knowledge Bases in Text-centric Information Retrieval (ICTIR 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, p. 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}}

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.