Canonicalizing Open Knowledge Bases

Galárraga, Luis; Heitz, Geremy; Murphy, Kevin; Suchanek, Fabian M.

doi:10.1145/2661829.2662073

Cited by 93 publications

(92 citation statements)

References 20 publications

(18 reference statements)

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“…However, OIE extractions provide a suitable starting point which is exploited by ENTICE. (Galárraga et al, 2014) addresses the problem of normalizing (or canonicalizing) OIE extractions which can be considered as one of the components of ENTICE (see Section 3.3).…”

Section: Related Workmentioning

confidence: 99%

An Entity-centric Approach for Overcoming Knowledge Graph Sparsity

Hegde

Talukdar

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Automatic construction of knowledge graphs (KGs) from unstructured text has received considerable attention in recent research, resulting in the construction of several KGs with millions of entities (nodes) and facts (edges) among them. Unfortunately, such KGs tend to be severely sparse in terms of number of facts known for a given entity, i.e., have low knowledge density. For example, the NELL KG consists of only 1.34 facts per entity. Unfortunately, such low knowledge density makes it challenging to use such KGs in real-world applications. In contrast to best-effort extraction paradigms followed in the construction of such KGs, in this paper we argue in favor of ENTIty Centric Expansion (ENTICE), an entity-centric KG population framework, to alleviate the low knowledge density problem in existing KGs. By using ENTICE, we are able to increase NELL's knowledge density by a factor of 7.7 at 75.5% accuracy. Additionally, we are also able to extend the ontology discovering new relations and entities.

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Section: Related Workmentioning

confidence: 99%

An Entity-centric Approach for Overcoming Knowledge Graph Sparsity

Hegde

Talukdar

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…There has been some studies [6, 14] on clustering synonymous relation phrases based on different kinds of signals and clustering methods (see Sec. 6).…”

Section: Clustering-integrated Type Propagation On Graphsmentioning

confidence: 99%

“…In particular, type signatures of relation phrases have proven very useful in clustering of relation phrases which have infrequent or ambiguous strings and contexts [6]. In contrast to previous approaches, our method leverages the type information derived by the type propagation and thus does not rely strictly on external sources to determine the type information for all the entity arguments.…”

Section: Clustering-integrated Type Propagation On Graphsmentioning

confidence: 99%

ClusType

Ren

El-Kishky

Wang

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called ClusType, that runs data-driven phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be softly clustered when propagating type information between their argument entities. Then we predict the type of each entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, type propagation with relation phrases and multi-view relation phrase clustering. Our experiments on multiple genres—news, Yelp reviews and tweets—demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method.

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“…In [4], we canonicalize the relations of a significant percentage (up to 33%) of the triples of a set of 1.3M Reverb extractions from ClueWeb09 3 . Our method assumes subjects and objects have been canonicalized somehow, for example by linking them to Freebase using the method described in [7].…”

Section: Canonicalization Of Open Kbsmentioning

confidence: 99%

Applications of Rule Mining in Knowledge Bases

Galárraga

2014

Proceedings of the 7th Workshop on Ph.D Students

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The continuous progress of Information Extraction (IE) techniques has led to the construction of large Knowledge Bases (KBs) containing facts about millions of entities such as people, organizations and places. KBs are important nowadays because they allow computers to understand the real world and are used in multiple domains and applications. Furthermore, the discovery of useful and non-trivial patterns in KBs, known as rule mining, opens the door for multiple applications in the areas of data analysis, prediction and automatic data engineering. In this article we present an overview of our ongoing work on rule mining on KBs and some of its applications. The scale of current KBs as well as their inherent incompleteness and noise make this endevour challenging.

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Canonicalizing Open Knowledge Bases

Cited by 93 publications

References 20 publications

An Entity-centric Approach for Overcoming Knowledge Graph Sparsity

An Entity-centric Approach for Overcoming Knowledge Graph Sparsity

ClusType

Applications of Rule Mining in Knowledge Bases

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