An Interface Sketch for Queripidia

Dietz, Laura; Schuhmacher, Michael

doi:10.1145/2810133.2810145

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…Finally, the task of querying by entities and for entities, where both the query and the results are entities, can be classified as related entity finding [62, §4.4.3]. Dietz and Schuhmacher [131] introduced Queripidia, as a set of knowledge portfolios. A knowledge portfolio represented a query-specific collection of relevant entities, combined with text passages from the web that explain why the entity is rel-.…”

Section: --mentioning

confidence: 99%

“…Despite the importance of the cross-referencing aspect, few attention has been given to the relations within and across corpora and knowledge bases in the design of representation models. Joint representation models have been explored through the usage of the inverted index, to store virtual documents built from passages mentioning the indexed entities [4,131] and, while mention relations were implicitly captured in the process, no other relations (e.g., entity relations) are available in the model to inform retrieval. Representation learning has also been used to find a common word and entity embedding space [86] and, despite capturing latent relations between words and entities, these are not explicit or particularly exploited for retrieval.…”

Section: --mentioning

confidence: 99%

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Graph-based entity-oriented search

Devezas

2021

SIGIR Forum

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Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relying on two separate representation models inhibits the effective cross-referencing of information, discarding otherwise available relations that could lead to a better ranking. Moreover, different retrieval tasks often demand separate implementations, although the problem is, at its core, the same. With the goal of harnessing all available information to optimize retrieval, we explore joint representation models of documents and entities, while taking a step towards the definition of a more general retrieval approach. Specifically, we propose that graphs should be used to incorporate explicit and implicit information derived from the relations between text found in corpora and entities found in knowledge bases. We also take advantage of this framework to elaborate a general model for entity-oriented search, proposing a universal ranking function for the tasks of ad hoc document retrieval (leveraging entities), ad hoc entity retrieval, and entity list completion. At a conceptual stage, we begin by proposing the graph-of-entity, based on the relations between combinations of term and entity nodes. We introduce the entity weight as the corresponding ranking function, relying on the idea of seed nodes for representing the query, either directly through term nodes, or based on the expansion to adjacent entity nodes. The score is computed based on a series of geodesic distances to the remaining nodes, providing a ranking for the documents (or entities) in the graph. In order to improve on the low scalability of the graph-of-entity, we then redesigned this model in a way that reduced the number of edges in relation to the number of nodes, by relying on the hypergraph data structure. The resulting model, which we called hypergraph-of-entity, is the main contribution of this thesis. The obtained reduction was achieved by replacing binary edges with n -ary relations based on sets of nodes and entities (undirected document hyperedges), sets of entities (undirected hyperedges, either based on cooccurrence or a grouping by semantic subject), and pairs of a set of terms and a set of one entity (directed hyperedges, mapping text to an object). We introduce the random walk score as the corresponding ranking function, relying on the same idea of seed nodes, similar to the entity weight in the graph-of-entity. Scoring based on this function is highly reliant on the structure of the hypergraph, which we call representation-driven retrieval. As such, we explore several extensions of the hypergraph-of-entity, including relations of synonymy, or contextual similarity, as well as different weighting functions per node and hyperedge type. We also propose TF-bins as a discretization for representing term frequency in the hypergraph-of-entity. For the random walk score, we propose and explore several parameters, including length and repeats, with or without seed node expansion, direction, or weights, and with or without a certain degree of node and/or hyperedge fatigue, a concept that we also propose. For evaluation, we took advantage of TREC 2017 OpenSearch track, which relied on an online evaluation process based on the Living Labs API, and we also participated in TREC 2018 Common Core track, which was based on the newly introduced TREC Washington Post Corpus. Our main experiments were supported on the INEX 2009 Wikipedia collection, which proved to be a fundamental test collection for assessing retrieval effectiveness across multiple tasks. At first, our experiments solely focused on ad hoc document retrieval, ensuring that the model performed adequately for a classical task. We then expanded the work to cover all three entity-oriented search tasks. Results supported the viability of a general retrieval model, opening novel challenges in information retrieval, and proposing a new path towards generality in this area.

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Section: --mentioning

confidence: 99%

Section: --mentioning

confidence: 99%

Graph-based entity-oriented search

Devezas

2021

SIGIR Forum

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“…This work shares goals with Dalton and Dietz (2013) and Dietz and Schuhmacher (2015), who create "knowledge sketches": distributions over documents, entities, and relations related to a query. The primary difference is that our work creates a KB instead of returning results from an existing one.…”

Section: Related Workmentioning

confidence: 99%

“…PKBs offer a set of answers for queries with clues as to their relationship, like Marc Bolland and Stuart Rose are related because of events described using the word replaced. KBs are also useful information retrieval (IR) tools (Dietz and Schuhmacher, 2015) for human guided corpus exploration. PKBs serve this goal by providing ranked lists of related entities and over the mentions describing their relationship to the query.…”

Section: Pocket Knowledge Base Populationmentioning

confidence: 99%

“…Our work addresses two new aspects of this problem: 1) how to automatically find related entities, which are assumed given in that work and 2) how to find the salient parts of support sentences (trigger words) by aggregating evidence across sentences. This work shares goals with Dalton and Dietz (2013) and Dietz and Schuhmacher (2015), who create "knowledge sketches": distributions over documents, entities, and relations related to a query. The primary difference is that our work creates a KB instead of returning results from an existing one.…”

Section: Related Entities Vs Slot Fillersmentioning

confidence: 99%

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Pocket Knowledge Base Population

Wolfe

Dredze

Durme

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 2: Short Papers)

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Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage, leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities.We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well as new annotations collected for this work. Our methods produce high quality KBs from just text with many more entities and relationships than existing KBP systems.

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A Review of Graph-Based Models for Entity-Oriented Search

Devezas

Nunes

2021

SN COMPUT. SCI.

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Entity-oriented search tasks heavily rely on exploiting unstructured and structured collections. Moreover, it is frequent for text corpora and knowledge bases to provide complementary views on a common topic. While, traditionally, the retrieval unit was the document, modern search engines have evolved to also retrieve entities and to provide direct answers to the information needs of the users. Cross-referencing information from heterogeneous sources has become fundamental, however a mismatch still exists between text-based and knowledge-based retrieval approaches. The former does not account for complex relations, while the latter does not properly support keyword-based queries and ranked retrieval. Graphs are a good solution to this problem, since they can be used to represent text, entities and their relations. In this survey, we examine textbased approaches and how they evolved to leverage entities and their relations in the retrieval process. We also cover multiple aspects of graph-based models for entity-oriented search, providing an overview on link analysis and exploring graph-based text representation and retrieval, leveraging knowledge graphs for document or entity retrieval, building entity graphs from text, using graph matching for querying with subgraphs, exploiting hypergraph-based representations, and ranking based on random walks on graphs. We close with a discussion on the topic and a view of the future to motivate the research of graphbased models for entity-oriented search, particularly as joint representation models for the generalization of retrieval tasks.

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An Interface Sketch for Queripidia

Cited by 5 publications

References 9 publications

Graph-based entity-oriented search

Graph-based entity-oriented search

Pocket Knowledge Base Population

A Review of Graph-Based Models for Entity-Oriented Search

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