LinkSUM: Using Link Analysis to Summarize Entity Data

Thalhammer, Andreas; Lasierra, Nelia; Rettinger, Achim

doi:10.1007/978-3-319-38791-8_14

Cited by 37 publications

(31 citation statements)

References 12 publications

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“…In [48], person timelines are generated by ranking relations extracted from Wikipedia and YAGO knowledge graphs. Similarly, in [28] entity summarisation is created based on link counts, but without taking temporal data into account. In difference to our work, in both these approaches the feature weights are handcrafted and no machine learning is involved.…”

Section: Biographical Timeline Generationmentioning

confidence: 99%

EventKG – the hub of event knowledge on the web – and biographical timeline generation

Gottschalk

Demidova

2019

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One of the key requirements to facilitate the semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events, entities and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. In this article we address this limitation, formalise the concept of a temporal knowledge graph and present its instantiation -EventKG. EventKG is a multilingual event-centric temporal knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations obtained from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical RDF representation. Whereas popular entities often possess hundreds of relations within a temporal knowledge graph such as EventKG, generating a concise overview of the most important temporal relations for a given entity is a challenging task. In this article we demonstrate an application of EventKG to biographical timeline generation, where we adopt a distant supervision method to identify relations most relevant for an entity biography. Our evaluation results provide insights on the characteristics of EventKG and demonstrate the effectiveness of the proposed biographical timeline generation method.

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Section: Biographical Timeline Generationmentioning

confidence: 99%

EventKG – the hub of event knowledge on the web – and biographical timeline generation

Gottschalk

Demidova

2019

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“…RELIN [Cheng et al ., 2011], FACES [Gunaratna et al ., 2015; 2016], LinkSum [Thalhammer et al ., 2016], SUMMARUM [Thalhammer and Rettinger, 2014], diversity-based summaries [Sydow et al ., 2013], and contextual entity summaries by mining query logs [Yan et al ., 2016] are good examples. In these approaches, RELIN, SUMMARUM, and LinkSum approaches adapt modified random surfer models (PageRank) to rank facts and then select them for summaries.…”

Section: Related Workmentioning

confidence: 99%

“…For example, the RELIN [Cheng et al ., 2011] and LinkSum [Thalhammer et al ., 2016] entity summarization systems have employed PageRank-based ranking mechanisms, the FACES system [Gunaratna et al ., 2015] demonstrated an incremental conceptual hierarchical clustering-based approach in creating comprehensive (diverse) summaries, and [Sydow et al ., 2013] investigated entity neighborhoods in the graph to generate diverse summaries. Systems such as those mentioned above focus on summarizing individual entities by giving precedence to selecting the most important facts for distinctly identifying an entity.…”

Section: Introductionmentioning

confidence: 99%

Relatedness-based Multi-Entity Summarization

Gunaratna

Yazdavar

Thirunarayan

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches.

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“…9. Top-k Properties A summary of the entity is visualized using an external service called LinkSUM [10] provided by Andreas Thalhammer. 10.…”

Section: Feedback Functionalitymentioning

confidence: 99%

Trill: A Reusable Front-End for QA Systems

Diefenbach

Amjad

Both

et al. 2017

Lecture Notes in Computer Science

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Abstract. The Semantic Web contains an enormous amount of information in the form of knowledge bases. To make this information available to end-users many question answering (QA) systems over knowledge bases were created in the last years. Their goal is to enable users to access large amounts of structured data in the Semantic Web by bridging the gap between natural language and formal query languages like SPARQL. But automatically generating a SPARQL query from a user's question is not sufficient to bridge the gap between Semantic Web data and the end-users. The result of a SPARQL query consists of a list of URIs and/or literals, which is not a user-friendly presentation of the answer. Such a presentation includes the representation of the URI in the right language and additional information like images, maps, entity summaries and more. We present Trill, the first reusable user-interface (UI) for QA systems over knowledge bases supporting text and audio input, able to present answers from DBpedia and Wikidata in 4 languages (English, French, German, and Italian). It is designed to be used together with Qanary, an infrastructure for composing QA pipelines. This front-end enables the QA community to show their results to end-users and enables the research community to explore new research directions like studying and designing user-interactions with QA systems.

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LinkSUM: Using Link Analysis to Summarize Entity Data

Cited by 37 publications

References 12 publications

EventKG – the hub of event knowledge on the web – and biographical timeline generation

EventKG – the hub of event knowledge on the web – and biographical timeline generation

Relatedness-based Multi-Entity Summarization

Trill: A Reusable Front-End for QA Systems

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