Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking

Nunes, Bernardo Pereira; Dietze, Stefan; Casanova, Marco A.; Kawase, Ricardo; Fetahu, Besnik; Nejdl, Wolfgang

doi:10.1007/978-3-642-38288-8_37

Cited by 24 publications

(4 citation statements)

References 24 publications

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“…The entity-linking capability of Knowledge Graphs could also assist a Web search engine to provide personalized search results. Approaches that use graph analysis of reference datasets together with entity co-occurrence mapped on the Web, such as the one presented by Nunes et al in [24], could point out documents clearly related to the same topics (e.g. Basketball/Sports or Stochastic model/Probability) even in a lack of sufficient contextual information situation.…”

Section: Discussionmentioning

confidence: 99%

Semantic Data Structures for Knowledge Generation in Open World Information System

Tibau

Siqueira

Nunes

2020

XVI Brazilian Symposium on Information Systems

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As the amount of information grows exponentially online, Information Systems role in support knowledge flows encouraged by linked data increases as a driver to innovation, culture, business practices and people behavior. Web search engines are particularly affected by the open world challenges, notably as part of the growing digital ecosystems of networks and platforms of technology, media, and telecommunications (TMT) companies delivering personalized and customized services (e.g. Amazon in retailing, Uber in ride service hailing, food delivery, and bicycle-sharing system, and Airbnb in lodging). To recognize search intent drawn from user's behavior allows to provide personalized search results. The work presented in this paper has the purpose of exploring methods to represent semantic relationships between concepts indexed by Web search engines in order to aid them recognize search intent and display results that meet the search intent. The performance of two different types of data structures based on entity-centric indexing was compared. The data structures were: a knowledge base that used an entity-centric mapping of Wikipedia categories and the KBpedia Knowledge Graph. Through analysis of entity ranking and linking, we detected that the Knowledge Graph could identify approximately three times more properties and relationships, which increases Web search engines capability to "understand" what is being asked.

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Section: Discussionmentioning

confidence: 99%

Semantic Data Structures for Knowledge Generation in Open World Information System

Tibau

Siqueira

Nunes

2020

XVI Brazilian Symposium on Information Systems

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“…RelFinder [22] is an example of this approach since they perform a search over entities in an RDF dataset to find their relationships, while other approaches use schema paths to find relationships between entity individuals through their entity types [36,37]. Hutchison et al [33] also find relationships between entities but, besides considering the path between entities, also takes into account external resources by measuring the cooccurrence of entities in Web documents. Due to the holistic nature of our framework, we extract object properties from the entity type candidates generated and ranked.…”

Section: Related Workmentioning

confidence: 99%

Extracting data models from background knowledge graphs

Oliveira

d’Aquin

2022

Knowledge-Based Systems

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Knowledge Graphs have emerged as a core technology to aggregate and publish knowledge on the Web. However, integrating knowledge from different sources, not specifically designed to be interoperable, is not a trivial task. Finding the right ontologies to model a dataset is a challenge since several valid data models exist and there is no clear agreement between them. In this paper, we propose to facilitate the selection of a data model with the RICDaM (Recommending Interoperable and Consistent Data Models) framework. RICDaM generates and ranks candidates that match entity types and properties in an input dataset. These candidates are obtained by aggregating freely available domain RDF datasets in a knowledge graph and then enriching the relationships between the graph's entities. The entity type and object property candidates are obtained by exploiting the instances and structure of this knowledge graph to compute a score that considers both the accuracy and interoperability of the candidates. Datatype properties are predicted with a random forest model, trained on the knowledge graph properties and their values, so to make predictions on candidate properties and rank them according to different measures. We present experiments using multiple datasets from the library domain as a use case and show that our methodology can produce meaningful candidate data models, adaptable to specific scenarios and needs.

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“…Entity Interlinking tries to detect links between entities withing a knowledge base. The work done by Nunes et al [14] uses social network theory measures, such as Katz index to find links between entities. This is related to our work since we analyze the co-referencing of entities within Wikipedia, their collaborator structure and interlinking with events in the Wikipedia's event portal.…”

Section: Related Workmentioning

confidence: 99%

How much is Wikipedia Lagging Behind News?

Fetahu

Anand

2015

Proceedings of the ACM Web Science Conference

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Wikipedia, rich in entities and events, is an invaluable resource for various knowledge harvesting, extraction and mining tasks. Numerous resources like DBpedia, YAGO and other knowledge bases are based on extracting entity and event based knowledge from it. Online news, on the other hand, is an authoritative and rich source for emerging entities, events and facts relating to existing entities. In this work, we study the creation of entities in Wikipedia with respect to news by studying how entity and event based information flows from news to Wikipedia. We analyze the lag of Wikipedia (based on the revision history of the English Wikipedia) with 20 years of \emph{The New York Times} dataset (NYT). We model and analyze the lag of entities and events, namely their first appearance in Wikipedia and in NYT, respectively. In our extensive experimental analysis, we find that almost 20\% of the external references in entity pages are news articles encoding the importance of news to Wikipedia. Second, we observe that the entity-based lag follows a normal distribution with a high standard deviation, whereas the lag for news-based events is typically very low. Finally, we find that events are responsible for creation of emergent entities with as many as 12\% of the entities mentioned in the event page are created after the creation of the event page

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Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking

Cited by 24 publications

References 24 publications

Semantic Data Structures for Knowledge Generation in Open World Information System

Semantic Data Structures for Knowledge Generation in Open World Information System

Extracting data models from background knowledge graphs

How much is Wikipedia Lagging Behind News?

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