Active objects

Lin, Thomas; Pantel, Patrick; Gamon, Michael; Kannan, Anitha; Fuxman, Ariel

doi:10.1145/2187836.2187916

Cited by 61 publications

(7 citation statements)

References 20 publications

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“…The Knowledge Graph released by Google in 2012 (Singhal, 2012) provided a next-generation information retrieval service with ontology-based intelligent search based on free-style user queries. Similar techniques, e.g., Safari, were developed based on achievements in entity-centric search (Lin et al, 2012). However, existing entity-based search engines cannot perform fully automatic content parsing on heterogeneous modalities, and thus they cannot provide entity-based information retrieval for crossmedia content.…”

Section: Cross-media Knowledge Graph Construction and Learning Methodmentioning

confidence: 99%

Cross-media analysis and reasoning: advances and directions

Peng

Zhu

Zhao

et al. 2017

Frontiers Inf Technol Electronic Eng

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Abstract:Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows: (1) theory and model for cross-media uniform representation; (2) cross-media correlation understanding and deep mining; (3) cross-media knowledge graph construction and learning methodologies; (4) cross-media knowledge evolution and reasoning; (5) cross-media description and generation; (6) cross-media intelligent engines; and (7) cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.

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Section: Cross-media Knowledge Graph Construction and Learning Methodmentioning

confidence: 99%

Cross-media analysis and reasoning: advances and directions

Peng

Zhu

Zhao

et al. 2017

Frontiers Inf Technol Electronic Eng

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“…According to Guo et al [28], over 70% of queries in web search contain named entities. The study by Lin et al [49] reports a lower number, 43%, albeit using different annotation guidelines. The bottom line is that by being able to recognize entities in queries, the user experience can be improved for a significant portion of search requests (e.g., by enhanced result ranking or presentation).…”

Section: Entity Linking In Queriesmentioning

confidence: 96%

Understanding Information Needs

Balog

2018

Entity-Oriented Search

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Understanding what the user is looking for is at the heart of delivering a quality search experience. After all, it is rather difficult to serve good results, unless we can comprehend the intent and meaning behind the user's query. Query understanding is the first step that takes place before the scoring of results. Its overall aim is to infer a semantically enriched representation of the information need. This involves, among others, classifying the query according to higher-level goals or intent, segmenting it into parts that belong together, interpreting the query structure, recognizing and disambiguating the mentioned entities, and determining if specific services or verticals 1 should be invoked. Such semantic analysis of queries has been a longstanding research area in information retrieval. In Sect. 7.1, we give a brief overview of IR approaches to query understanding. In the rest of the chapter, we direct our focus of attention to representing information needs with the help of structured knowledge repositories. The catchphrase "things, not strings" was coined by Google when introducing their Knowledge Graph. 2 It aptly describes the current chapter's focus: Capturing what the query is about by automatically annotating it with entries from a knowledge repository. These semantic annotations can then be utilized in downstream processing for result ranking (see Chap. 4) and/or result presentation. Specifically, in Sect. 7.2, we seek to identify the types or categories of entities that are targeted by the query. In Sect. 7.3, we perform entity linking in queries, which is about recognizing specific entity mentions and annotating them with unique identifiers from the underlying knowledge repository. Additionally, we consider the case of unresolvable ambiguity, when queries have multiple possible interpretations. 1 A vertical is a specific segment of online content. Some of the most common verticals include shopping, travel, job search, the automotive industry, medical information, and scholarly literature.

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“…According to various studies, 40-70% of queries in web search mention or target specific entities [20,23,32]. These queries commonly seek a particular entity, albeit often an ambiguous one (e.g., "harry potter") or a list of entities (e.g., "doctors in barcelona").…”

Section: Entities As the Unit Of Retrievalmentioning

confidence: 99%

Introduction

Balog¹

2018

Entity-Oriented Search

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Active objects

Cited by 61 publications

References 20 publications

Cross-media analysis and reasoning: advances and directions

Cross-media analysis and reasoning: advances and directions

Understanding Information Needs

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