| Entity linking|
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Entity linking is included as keyword or extra keyword in 1 datasets, 0 tools and 9 publications.
|Google dataset linking strings and concepts||~10 GB|
There is no tools for this keyword.
|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Evaluating the helpfulness of linked entities to readers||Yamada I.
|HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media||English||2014||When we encounter an interesting entity (e.g., a person's name or a geographic location) while reading text, we typically search and retrieve relevant information about it. Entity linking (EL) is the task of linking entities in a text to the corresponding entries in a knowledge base, such as Wikipedia. Recently, EL has received considerable attention. EL can be used to enhance a user's text reading experience by streamlining the process of retrieving information on entities. Several EL methods have been proposed, though they tend to extract all of the entities in a document including unnecessary ones for users. Excessive linking of entities can be distracting and degrade the user experience. In this paper, we propose a new method for evaluating the helpfulness of linking entities to users. We address this task using supervised machine-learning with a broad set of features. Experimental results show that our method significantly outperforms baseline methods by approximately 5.7%-12% F1. In addition, we propose an application, Linkify, which enables developers to integrate EL easily into their web sites.||0||0|
|A Cross-Lingual Dictionary for English Wikipedia Concepts||Valentin I. Spitkovsky
Angel X. Chang
|Proceedings of the Eighth International Conference on Language Resources and Evaluation||English||2012||We present a resource for automatically associating strings of text with English Wikipedia concepts. Our machinery is bi-directional, in the sense that it uses the same fundamental probabilistic methods to map strings to empirical distributions over Wikipedia articles as it does to map article URLs to distributions over short, language-independent strings of natural language text. For maximal interoperability, we release our resource as a set of flat line-based text files, lexicographically sorted and encoded with UTF-8. These files capture joint probability distributions underlying concepts (we use the terms article, concept and Wikipedia URL interchangeably) and associated snippets of text, as well as other features that can come in handy when working with Wikipedia articles and related information.||5||0|
|A graph-based approach for ontology population with named entities||Shen W.
|ACM International Conference Proceeding Series||English||2012||Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web and knowledge management techniques. This issue naturally consists of two subtasks: (1) for the entity mention whose mapping entity does not exist in the ontology, attach it to the right category in the ontology (i.e., fine-grained named entity classification), and (2) for the entity mention whose mapping entity is contained in the ontology, link it with its mapping real world entity in the ontology (i.e., entity linking). Previous studies only focus on one of the two subtasks and cannot solve this task of populating ontology with named entities integrally. This paper proposes APOLLO, a grAph-based aPproach for pOpuLating ontoLOgy with named entities. APOLLO leverages the rich semantic knowledge embedded in the Wikipedia to resolve this task via random walks on graphs. Meanwhile, APOLLO can be directly applied to either of the two subtasks with minimal revision. We have conducted a thorough experimental study to evaluate the performance of APOLLO. The experimental results show that APOLLO achieves significant accuracy improvement for the task of ontology population with named entities, and outperforms the baseline methods for both subtasks.||0||0|
|APOLLO: A general Framework for POpuLating ontoLOgy with named entities via random walks on graphs||Shen W.
|WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion||English||2012||Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web. This issue naturally consists of two subtasks: (1) for the entity mention whose mapping entity does not exist in the ontology, attach it to the right category in the ontology (i.e., fine-grained named entity classification), and (2) for the entity mention whose mapping entity is contained in the ontology, link it with its mapping real world entity in the ontology (i.e., entity linking). Previous studies only focus on one of the two subtasks. This paper proposes APOLLO, a general weakly supervised frAmework for POpuLating ontoLOgy with named entities. APOLLO leverages the rich semantic knowledge embedded in the Wikipedia to resolve this task via random walks on graphs. An experimental study has been conducted to show the effectiveness of APOLLO. Copyright is held by the author/owner(s).||0||0|
|Context-aware in-page search||Lin Y.-H.
|Proceedings of the 24th Conference on Computational Linguistics and Speech Processing, ROCLING 2012||English||2012||In this paper we introduce a method for searching appropriate articles from knowledge bases (e.g. Wikipedia) for a given query and its context. In our approach, this problem is transformed into a multi-class classification of candidate articles. The method involves automatically augmenting smaller knowledge bases using larger ones and learning to choose adequate articles based on hyperlink similarity between article and context. At run-time, keyphrases in given context are extracted and the sense ambiguity of query term is resolved by computing similarity of keyphrases between context and candidate articles. Evaluation shows that the method significantly outperforms the strong baseline of assigning most frequent articles to the query terms. Our method effectively determines adequate articles for given query-context pairs, suggesting the possibility of using our methods in context-aware search engines.||0||0|
|LINDEN: Linking named entities with knowledge base via semantic knowledge||Shen W.
|WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web||English||2012||Integrating the extracted facts with an existing knowledge base has raised an urgent need to address the problem of entity linking. Specifically, entity linking is the task to link the entity mention in text with the corresponding real world entity in the existing knowledge base. However, this task is challenging due to name ambiguity, textual inconsistency, and lack of world knowledge in the knowledge base. Several methods have been proposed to tackle this problem, but they are largely based on the co-occurrence statistics of terms between the text around the entity mention and the document associated with the entity. In this paper, we propose LINDEN1, a novel framework to link named entities in text with a knowledge base unifying Wikipedia and Word-Net, by leveraging the rich semantic knowledge embedded in the Wikipedia and the taxonomy of the knowledge base. We extensively evaluate the performance of our proposed LINDEN over two public data sets and empirical results show that LINDEN significantly outperforms the state-of-the-art methods in terms of accuracy.||0||0|
|NAMED ENTITY DISAMBIGUATION: A HYBRID APPROACH||Nguyen H.T.
|International Journal of Computational Intelligence Systems||English||2012||Semantic annotation of named entities for enriching unstructured content is a critical step in development of Semantic Web and many Natural Language Processing applications. To this end, this paper addresses the named entity disambiguation problem that aims at detecting entity mentions in a text and then linking them to entries in a knowledge base. In this paper, we propose a hybrid method, combining heuristics and statistics, for named entity disambiguation. The novelty is that the disambiguation process is incremental and includes several rounds that filter the candidate referents, by exploiting previously identified entities and extending the text by those entity attributes every time they are successfully resolved in a round. Experiments are conducted to evaluate and show the advantages of the proposed method. The experiment results show that our approach achieves high accuracy and can be used to construct a robust entity disambiguation system.||0||0|
|Extracting information about security vulnerabilities from Web text||Mulwad V.
|Proceedings - 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2011||English||2011||The Web is an important source of information about computer security threats, vulnerabilities and cyberattacks. We present initial work on developing a framework to detect and extract information about vulnerabilities and attacks from Web text. Our prototype system uses Wikitology, a general purpose knowledge base derived from Wikipedia, to extract concepts that describe specific vulnerabilities and attacks, map them to related concepts from DBpedia and generate machine understandable assertions. Such a framework will be useful in adding structure to already existing vulnerability descriptions as well as detecting new ones. We evaluate our approach against vulnerability descriptions from the National Vulnerability Database. Our results suggest that it can be useful in monitoring streams of text from social media or chat rooms to identify potential new attacks and vulnerabilities or to collect data on the spread and volume of existing ones.||0||0|
|Using linked data to interpret tables||Mulwad V.
|CEUR Workshop Proceedings||English||2010||Vast amounts of information is available in structured forms like spreadsheets, database relations, and tables found in documents and on the Web. We describe an approach that uses linked data to interpret such tables and associate their components with nodes in a reference linked data collection. Our proposed framework assigns a class (i.e. type) to table columns, links table cells to entities, and inferred relations between columns to properties. The resulting interpretation can be used to annotate tables, confirm existing facts in the linked data collection, and propose new facts to be added. Our implemented prototype uses DBpedia as the linked data collection and Wikitology for background knowledge. We evaluated its performance using a collection of tables from Google Squared, Wikipedia and the Web.||0||0|