Relevant bibliographies by topics / Entities on the Web

Academic literature on the topic 'Entities on the Web'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Entities on the Web"

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Efthymiou, Vasilis, Kostas Stefanidis, and Vassilis Christophides. "Benchmarking Blocking Algorithms for Web Entities." IEEE Transactions on Big Data 6, no. 2 (June 1, 2020): 382–95. http://dx.doi.org/10.1109/tbdata.2016.2576463.

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Huang, Xinyan, Xinjun Wang, and Hui Li. "Mining Similar Traces of Entities on Web." Cybernetics and Information Technologies 15, no. 6 (December 1, 2015): 219–29. http://dx.doi.org/10.1515/cait-2015-0081.

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Abstract Events about entities have been widely collected on Web, allowing us to analyze how peer entities interact and learn the relationships that exist among the entities. In this paper we investigate similar traces that have not been adequately studied so far. Intuitively, peer entities tend to have similar traces. The challenges in mining similar traces are: (1) the occurring time lags of traces are usually unknown and varying; (2) the existence of large-scale events of entities and complexity of the model representing all the events. In this paper we propose a simple, but practical method that addresses all these challenges. Firstly, sliding windows are adopted to filter out the significant events and then find the candidate topic sequences. Secondly, dynamic programming is employed to mine similar candidate topic sequences of entities. Finally, an efficient method is proposed to mine all the similar traces of entities. It is able to mine similar traces of peer entities with high accuracy. We conduct comprehensive experiments on synthetic datasets to demonstrate the efficiency of the method proposed.
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Thakare, Abhijeet Ramesh, and Parag S. Deshpande. "Comparative Search of Entities." International Journal of Software Engineering and Knowledge Engineering 27, no. 08 (October 2017): 1333–57. http://dx.doi.org/10.1142/s0218194017500498.

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Nowadays, every manufacturer or retailer displays their product information on various websites. The customer has to visit, the number of such web pages to choose the right product, because the information is not available at one place. There are some websites that show such information in one place, but they are product specific and in general information is manually updated. In this paper, we propose a novel concept of web-spreadsheet, which displays product information by crawling through related web pages and generates information like a spreadsheet where each row represents product information and each column represents product attributes. We are extracting the product name of specified product class using decision tree-based classifier by features obtained using Part of Speech (POS) tagging and distance measure. It also extracts the value-measure pairs of preset attributes using distance measure, POS tagging and Data type. This approach will save a lot of time of comparing different products and customers need not have to scan a number of websites for comparison. We present promising results in various product classes which surpass many existing techniques in the literature. The proposed method can work accurately without initial trained labeled data which is expensive to obtain.
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Yin Ming, Ming, Dion Hoe‐lian Goh, Ee‐Peng Lim, and Aixin Sun. "Discovery of concept entities from web sites using web unit mining." International Journal of Web Information Systems 1, no. 3 (August 2005): 123–36. http://dx.doi.org/10.1108/17440080580000088.

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Jang, Myungha, Jin-woo Park, and Seung-won Hwang. "Predictive Mining of Comparable Entities from the Web." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 66–72. http://dx.doi.org/10.1609/aaai.v26i1.8112.

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Comparing entities is an important part of decision making. Several approaches have been reported for mining comparable entities from Web sources to improve user experience in comparing entities online.However, these efforts extract only entities explicitly compared in the corpora, and may exclude entities that occur less-frequently but potentially comparable. To build a more complete comparison machine that can infer such missing relations, here we develop a solutionto predict transitivity of known comparable relations. Named CliqueGrow, our approach predicts missing links given a comparable entity graph obtained from versus query logs. Our approach achieved the highest F1-score among five link prediction approaches and a commercial comparison engine provided by Yahoo!.
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Barbosa, Luciano. "Learning representations of Web entities for entity resolution." International Journal of Web Information Systems 15, no. 3 (August 19, 2019): 346–58. http://dx.doi.org/10.1108/ijwis-07-2018-0059.

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Purpose Matching instances of the same entity, a task known as entity resolution, is a key step in the process of data integration. This paper aims to propose a deep learning network that learns different representations of Web entities for entity resolution. Design/methodology/approach To match Web entities, the proposed network learns the following representations of entities: embeddings, which are vector representations of the words in the entities in a low-dimensional space; convolutional vectors from a convolutional layer, which capture short-distance patterns in word sequences in the entities; and bag-of-word vectors, created by a bow layer that learns weights for words in the vocabulary based on the task at hand. Given a pair of entities, the similarity between their learned representations is used as a feature to a binary classifier that identifies a possible match. In addition to those features, the classifier also uses a modification of inverse document frequency for pairs, which identifies discriminative words in pairs of entities. Findings The proposed approach was evaluated in two commercial and two academic entity resolution benchmarking data sets. The results have shown that the proposed strategy outperforms previous approaches in the commercial data sets, which are more challenging, and have similar results to its competitors in the academic data sets. Originality/value No previous work has used a single deep learning framework to learn different representations of Web entities for entity resolution.
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Dai, Hong-Jie, Chi-Hsin Huang, Ryan T. K. Lin, Richard Tzong-Han Tsai, and Wen-Lian Hsu. "BIOSMILE web search: a web application for annotating biomedical entities and relations." Nucleic Acids Research 36, suppl_2 (May 31, 2008): W390—W398. http://dx.doi.org/10.1093/nar/gkn319.

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Sung, Ki-Youn, and Bo-Hyun Yun. "Topic based Web Document Clustering using Named Entities." Journal of the Korea Contents Association 10, no. 5 (May 28, 2010): 29–36. http://dx.doi.org/10.5392/jkca.2010.10.5.029.

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Presutti, Valentina, and Aldo Gangemi. "Identity of Resources and Entities on the Web." International Journal on Semantic Web and Information Systems 4, no. 2 (April 2008): 49–72. http://dx.doi.org/10.4018/jswis.2008040103.

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Köpcke, Hanna, Andreas Thor, and Erhard Rahm. "Learning-Based Approaches for Matching Web Data Entities." IEEE Internet Computing 14, no. 4 (July 2010): 23–31. http://dx.doi.org/10.1109/mic.2010.58.

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Dissertations / Theses on the topic "Entities on the Web"

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Iofciu, Tereza [Verfasser]. "Users and entities on the web / Tereza Iofciu." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2013. http://d-nb.info/1035391759/34.

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Urbansky, David. "Automatic Extraction and Assessment of Entities from the Web." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-97469.

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The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval.
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Alsarem, Mazen. "Semantic snippets via query-biased ranking of linked data entities." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI044/document.

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Dans cette thèse, nous introduisons un nouvel artefact interactif pour le SERP: le "Snippet sémantique". Les snippets sémantiques s'appuient sur la coexistence des deux Webs pour faciliter le transfert des connaissances aux utilisateurs grâce a une contextualisation sémantique du besoin d'information de l'utilisateur. Ils font apparaître les relations entre le besoin d'information et les entités les plus pertinentes présentes dans la page Web
In this thesis, we introduce a new interactive artifact for the SERP: the "Semantic Snippet". Semantic Snippets rely on the coexistence of the two webs to facilitate the transfer of knowledge to the user thanks to a semantic contextualization of the user's information need. It makes apparent the relationships between the information need and the most relevant entities present in the web page
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Demartini, Gianluca [Verfasser]. "From people to entities : typed search in the enterprise and the web / Gianluca Demartini." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2011. http://d-nb.info/1013472055/34.

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Gunaratna, Kalpa. "Semantics-based Summarization of Entities in Knowledge Graphs." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1496124815009777.

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Nakashole, Ndapandula T. [Verfasser], and Gerhard [Akademischer Betreuer] Weikum. "Automatic extraction of facts, relations, and entities for web-scale knowledge base population / Ndapandula T. Nakashole. Betreuer: Gerhard Weikum." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2013. http://d-nb.info/1052779654/34.

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GUDIVADA, RANGA CHANDRA. "DISCOVERY AND PRIORITIZATION OF BIOLOGICAL ENTITIES UNDERLYING COMPLEX DISORDERS BY PHENOME-GENOME NETWORK INTEGRATION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195161740.

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Fotsoh, Tawaofaing Armel. "Recherche d’entités nommées complexes sur le web : propositions pour l’extraction et pour le calcul de similarité." Thesis, Pau, 2018. http://www.theses.fr/2018PAUU3003/document.

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Les récents développements des nouvelles technologies de l’information et de la communication font du Web une véritable mine d’information. Cependant, les pages Web sont très peu structurées. Par conséquent, il est difficile pour une machine de les traiter automatiquement pour en extraire des informations pertinentes pour une tâche ciblée. C’est pourquoi les travaux de recherche s’inscrivant dans la thématique de l’Extraction d’Information dans les pages web sont en forte croissance. Aussi, l’interrogation de ces informations, généralement structurées et stockées dans des index pour répondre à des besoins d’information précis correspond à la Recherche d’Information (RI). Notre travail de thèse se situe à la croisée de ces deux thématiques. Notre objectif principal est de concevoir et de mettre en œuvre des stratégies permettant de scruter le web pour extraire des Entités Nommées (EN) complexes (EN composées de plusieurs propriétés pouvant être du texte ou d’autres EN) de type entreprise ou de type événement, par exemple. Nous proposons ensuite des services d’indexation et d’interrogation pour répondre à des besoins d’informations. Ces travaux ont été réalisés au sein de l’équipe T2I du LIUPPA, et font suite à une commande de l’entreprise Cogniteev, dont le cœur de métier est centré sur l’analyse du contenu du Web. Les problématiques visées sont, d’une part, l’extraction d’EN complexes sur le Web et, d’autre part, l’indexation et la recherche d’information intégrant ces EN complexes. Notre première contribution porte sur l’extraction d’EN complexes dans des textes. Pour cette contribution, nous prenons en compte plusieurs problèmes, notamment le contexte bruité caractérisant certaines propriétés (pour un événement par exemple, la page web correspondante peut contenir deux dates : la date de l’événement et celle de mise en vente des billets). Pour ce problème en particulier, nous introduisons un module de détection de blocs qui permet de focaliser l’extraction des propriétés sur des blocs de texte pertinents. Nos expérimentations montrent une nette amélioration des performances due à cette approche. Nous nous sommes également intéressés à l’extraction des adresses, où la principale difficulté découle du fait qu’aucun standard ne se soit réellement imposé comme modèle de référence. Nous proposons donc un modèle étendu et une approche d’extraction basée sur des patrons et des ressources libres.Notre deuxième contribution porte sur le calcul de similarité entre EN complexes. Dans l’état de l’art, ce calcul se fait généralement en deux étapes : (i) une première calcule les similarités entre propriétés et (ii) une deuxième agrège les scores obtenus pour le calcul de la similarité globale. En ce qui concerne cette première étape, nous proposons une fonction de calcul de similarité entre EN spatiale, l’une représentée par un point et l’autre par un polygone. Elle complète l’état de l’art. Notons que nos principales propositions se situent au niveau de la deuxième étape. Ainsi, nous proposons trois techniques pour l’agrégation des scores intermédiaires. Les deux premières sont basées sur la somme pondérée des scores intermédiaires (combinaison linéaire et régression logistique). La troisième exploite les arbres de décisions pour agréger les scores intermédiaires. Enfin, nous proposons une dernière approche basée sur le clustering et le modèle vectoriel de Salton pour le calcul de similarité entre EN complexes. Son originalité vient du fait qu’elle ne nécessite pas de passer par le calcul de scores de similarités intermédiaires
Recent developments in information technologies have made the web an important data source. However, the web content is very unstructured. Therefore, it is a difficult task to automatically process this web content in order to extract relevant information. This is a reason why research work related to Information Extraction (IE) on the web are growing very quickly. Similarly, another very explored research area is the querying of information extracted on the web to answer an information need. This other research area is known as Information Retrieval (IR). Our research work is at the crossroads of both areas. The main goal of our work is to develop strategies and techniques for crawling the web in order to extract complex Named Entities (NEs) (NEs with several properties that may be text or other NEs). We then propose to index them and to query them in order to answer information needs. This work was carried out within the T2I team of the LIUPPA laboratory, in collaboration with Cogniteev, a company which core business is focused on the analysis of web content. The issues we had to deal with were the extraction of complex NEs on the web and the development of IR services supplied by the extracted data. Our first contribution is related to complex NEs extraction from text content. For this contribution, we take into consideration several problems, in particular the noisy context characterizing some properties (the web page describing an event for example, may contain more than one dates: the event’s date and the date of ticket’s sales opening). For this particular problem, we introduce a block detection module that focuses property's extraction on relevant text blocks. Our experiments show an improvement of system’s performances. We also focused on address extraction where the main issue arises from the fact that there is not a standard way for writing addresses in general and on the web in particular. We therefore propose a pattern-based approach which uses some lexicons for extracting addresses from text, regardless of proprietary resources.Our second contribution deals with similarity computation between complex NEs. In the state of the art, this similarity computation is generally performed in two steps: (i) first, similarities between properties are calculated; (ii) then the obtained similarities are aggregated to compute the overall similarity. Our main proposals focuses on the second step. We propose three techniques for aggregating property’s similarities. The first two are based on the weighted sum of these property’s similarities (simple linear combination and logistic regression). The third technique however, uses decision trees for the aggregation. Finally, we also propose a last approach based on clustering and Salton vector model. This last approach evaluates the similarity at the complex NE level without computing property’s similarities. We also propose a similarity computation function between spatial EN, one represented by a point and the other by a polygon. This completes those of the state of the art
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Urbansky, David [Verfasser], Alexander [Akademischer Betreuer] Schill, James [Akademischer Betreuer] Thom, and Michael [Akademischer Betreuer] Schroeder. "Automatic Extraction and Assessment of Entities from the Web / David Urbansky. Gutachter: Alexander Schill ; James Thom ; Michael Schroeder. Betreuer: Alexander Schill ; James Thom." Dresden : Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://d-nb.info/1068148233/34.

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Watanabe, Willian Massami. "Auxílio à leitura de textos em português facilitado: questões de acessibilidade." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22092010-164526/.

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A grande capacidade de disponibilização de informações que a Web possibilita se traduz em múltiplas possibilidades e oportunidades para seus usuários. Essas pessoas são capazes de acessar conteúdos provenientes de todas as partes do planeta, independentemente de onde elas estejam. Mas essas possibilidades não são estendidas a todos, sendo necessário mais que o acesso a um computador e a Internet para que sejam realizadas. Indivíduos que apresentem necessidades especiais (deficiência visual, cognitiva, dificuldade de locomoção, entre outras) são privados do acesso a sites e aplicações web que façam mal emprego de tecnologias web ou possuam o conteúdo sem os devidos cuidados para com a acessibilidade. Um dos grupos que é privado do acesso a esse ambiente é o de pessoas com dificuldade de leitura (analfabetos funcionais). A ampla utilização de recursos textuais nas aplicações pode tornar difícil ou mesmo impedir as interações desses indivíduos com os sistemas computacionais. Nesse contexto, este trabalho tem por finalidade o desenvolvimento de tecnologias assistivas que atuem como facilitadoras de leitura e compreensão de sites e aplicações web a esses indivíduos (analfabetos funcionais). Essas tecnologias assistivas utilizam recursos de processamento de língua natural visando maximizar a compreensão do conteúdo pelos usuários. Dentre as técnicas utilizadas são destacadas: simplificação sintática, sumarização automática, elaboração léxica e reconhecimento das entidades nomeadas. Essas técnicas são utilizadas com a finalidade de promover a adaptação automática de conteúdos disponíveis na Web para usuários com baixo nível de alfabetização. São descritas características referentes à acessibilidade de aplicações web e princípios de design para usuários com baixo nível de alfabetização, para garantir a identificação e entendimento das funcionalidades que são implementadas nas duas tecnologias assistivas resultado deste trabalho (Facilita e Facilita Educacional). Este trabalho contribuiu com a identificação de requisitos de acessibilidade para usuários com baixo nível de alfabetização, modelo de acessibilidade para automatizar a conformidade com a WCAG e desenvolvimento de soluções de acessibilidade na camada de agentes de usuários
The large capacity of Web for providing information leads to multiple possibilities and opportunities for users. The development of high performance networks and ubiquitous devices allow users to retrieve content from any location and in different scenarios or situations they might face in their lives. Unfortunately the possibilities offered by the Web are not necessarily currently available to all. Individuals who do not have completely compliant software or hardware that are able to deal with the latest technologies, or have some kind of physical or cognitive disability, find it difficult to interact with web pages, depending on the page structure and the ways in which the content is made available. When specifically considering the cognitive disabilities, users classified as functionally illiterate face severe difficulties accessing web content. The heavy use of texts on interfaces design creates an accessibility barrier to those who cannot read fluently in their mother tongue due to both text length and linguistic complexity. In this context, this work aims at developing an assistive technologies that assists functionally illiterate users during their reading and understanding of websites textual content. These assistive technologies make use of natural language processing (NLP) techniques that maximize reading comprehension for users. The natural language techniques that this work uses are: syntactic simplification, automatic summarization, lexical elaboration and named entities recognition. The techniques are used with the goal of automatically adapting textual content available on the Web for users with low literacy levels. This work describes the accessibility characteristics incorporated into both resultant applications (Facilita and Educational Facilita) that focus on low literacy users limitations towards computer usage and experience. This work contributed with the identification of accessibility requirements for low-literacy users, elaboration of an accessibility model for automatizing WCAG conformance and development of accessible solutions in the user agents layer of web applications
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Books on the topic "Entities on the Web"

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Inc, ebrary, ed. WCF 4.0 multi-tier services development with LINQ to entities: Build SOA applications on the Microsoft platform with this hands-on guide updated for VS2010. Birmingham [England]: Packt Pub., 2010.

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Sekine, Satoshi, and Elisabete Ranchhod, eds. Named Entities. Amsterdam: John Benjamins Publishing Company, 2009. http://dx.doi.org/10.1075/bct.19.

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Cowling, Sam. Abstract Entities. 1 [edition]. | New York : Routledge, 2017. | Series: New problems: Routledge, 2017. http://dx.doi.org/10.4324/9781315266619.

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Abstract entities. Basingstoke: Macmillan, 1992.

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Teichmann, Roger. Abstract Entities. London: Palgrave Macmillan UK, 1992. http://dx.doi.org/10.1007/978-1-349-21863-9.

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Abstract entities. New York: St. Martin's Press, 1992.

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Burgdoerfer, Jerry J. Illinois business entities. Newark, N.J: LexisNexis Matthew Bender, 2005.

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Unincorporated business entities. Cincinnati: Anderson Pub. Co., 1996.

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Unincorporated business entities. 3rd ed. Newark, NJ: LexisNexis, 2004.

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M, Lipshaw Jeffrey, ed. Unincorporated business entities. 4th ed. Newark, NJ: LexisNexis, 2009.

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Book chapters on the topic "Entities on the Web"

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Gangemi, Aldo, Andrea Giovanni Nuzzolese, Valentina Presutti, Francesco Draicchio, Alberto Musetti, and Paolo Ciancarini. "Automatic Typing of DBpedia Entities." In The Semantic Web – ISWC 2012, 65–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35176-1_5.

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Sigletos, Georgios, Georgios Paliouras, Constantine D. Spyropoulos, and Michalis Hatzopoulos. "Mining Web Sites Using Wrapper Induction, Named Entities, and Post-processing." In Web Mining: From Web to Semantic Web, 97–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30123-3_6.

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You, Yongjian, Shaohua Zhang, Jiong Lou, Xinsong Zhang, and Weijia Jia. "Neural Typing Entities in Chinese-Pedia." In Web and Big Data, 385–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96890-2_32.

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Christophides, Vassilis, Vasilis Efthymiou, and Kostas Stefanidis. "Matching and Resolving Entities." In Entity Resolution in the Web of Data, 17–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-79468-1_2.

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Nguyen, Tu Ngoc, Nattiya Kanhabua, and Wolfgang Nejdl. "Multiple Models for Recommending Temporal Aspects of Entities." In The Semantic Web, 462–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93417-4_30.

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Kozareva, Zornitsa, Joaquim Silva, Pablo Gamallo, and Gabriel Lopes. "Cluster Analysis of Named Entities." In Intelligent Information Processing and Web Mining, 429–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39985-8_47.

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Barrière, Caroline. "Searching for Named Entities." In Natural Language Understanding in a Semantic Web Context, 23–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41337-2_3.

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Thalhammer, Andreas, and Achim Rettinger. "PageRank on Wikipedia: Towards General Importance Scores for Entities." In The Semantic Web, 227–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47602-5_41.

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Wang, Chengyu, Rong Zhang, Xiaofeng He, Guomin Zhou, and Aoying Zhou. "NERank: Bringing Order to Named Entities from Texts." In Web Technologies and Applications, 15–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45814-4_2.

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Barrière, Caroline. "Entities, Labels, and Surface Forms." In Natural Language Understanding in a Semantic Web Context, 9–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41337-2_2.

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Conference papers on the topic "Entities on the Web"

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Demartini, Gianluca, Claudiu S. Firan, Mihai Georgescu, Tereza Iofciu, Ralf Krestel, and Wolfgang Nejdl. "An Architecture for Finding Entities on the Web." In 2009 Latin American Web Congress (LA-WEB). IEEE, 2009. http://dx.doi.org/10.1109/la-web.2009.14.

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Spaniol, Marc, and Gerhard Weikum. "Tracking entities in web archives." In the 21st international conference companion. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2187980.2188030.

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Thompson, Henry S., Jonathan Rees, and Jeni Tennison. "URIs in data: for entities, or for descriptions of entities—A critical analysis." In the 5th Annual ACM Web Science Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2464464.2532514.

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Demartini, Gianluca, Claudiu S. Firan, Tereza Iofciu, Ralf Krestel, and Wolfgang Nejdl. "A Model for Ranking Entities and Its Application to Wikipedia." In 2008 Latin American Web Conference (LA-WEB). IEEE, 2008. http://dx.doi.org/10.1109/la-web.2008.8.

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Gamon, Michael, Tae Yano, Xinying Song, Johnson Apacible, and Patrick Pantel. "Identifying salient entities in web pages." In the 22nd ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2505515.2505602.

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Jain, Alpa, and Patrick Pantel. "Identifying comparable entities on the web." In Proceeding of the 18th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646198.

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Yu, Wei, Junpeng Chen, and Guoying Yu. "Discovering Entities Relationships on the Web." In 2008 Second IEEE International Conference on Semantic Computing (ICSC). IEEE, 2008. http://dx.doi.org/10.1109/icsc.2008.16.

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Ideh, Azari, and Koohpeyma Fateme. "Disambiguating named entities by semantic web." In 3rd International Conference on Computer Science and Service System. Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/csss-14.2014.173.

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Ho, Vinh Thinh, Koninika Pal, Niko Kleer, Klaus Berberich, and Gerhard Weikum. "Entities with Quantities." In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3336191.3371860.

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Heist, Nicolas, and Heiko Paulheim. "Information Extraction From Co-Occurring Similar Entities." In WWW '21: The Web Conference 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3442381.3449836.

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Reports on the topic "Entities on the Web"

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Lewis, Dustin, ed. International Counterterrorism Efforts: An Initial Mapping. Harvard Law School Program on International Law and Armed Conflict, February 2015. http://dx.doi.org/10.54813/ktkl6017.

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The number, range, and scope of intergovernmental entities and initiatives with a counterterrorism component have grown significantly in recent years. Today, a web of counterterrorism laws, policies, and enforcement approaches is developed and overseen by over 70 international institutions, bodies, and networks around the world. These efforts focus on everything from promulgating international legal rules to developing global policy standards, from drafting model criminal laws to promoting intelligence- and information-sharing. To date, the full scope of these efforts has not, to our knowledge, been captured in one place. We set out to identify and summarize these efforts in a single online resource.
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Lutz, Carsten. Adding Numbers to the SHIQ Description Logic - First Results. Aachen University of Technology, 2001. http://dx.doi.org/10.25368/2022.117.

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Recently, the Description Logic (DL) SHIQ has found a large number of applications. This success is due to the fact that SHIQ combines a rich expressivity with efficient reasoning, as is demonstrated by its implementation in DL systems such as FaCT and RACER. One weakness of SHIQ, however, limits its usability in several application areas: numerical knowledge such as knowledge about the age, weight, or temperature of real-world entities cannot be adequately represented. In this paper, we propose an extension of SHIQ that aims at closing this gap. The new Description Logic Q-SHIQ, which augments SHIQ by additional, 'concrete domain' style concept constructors, allows to refer to rational numbers in concept descriptions, and also to define concepts based on the comparison of numbers via predicates such as < or =. We argue that this kind of expressivity is needed in many application areas such as reasoning about the semantic web. We prove reasoning with Q-SHIQ to be EXPTIME-complete (thus not harder than reasoning with SHIQ) by devising an automata-based decision procedure.
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Dahl, Deborah A. Determiners, Entities, and Contexts. Fort Belvoir, VA: Defense Technical Information Center, October 1986. http://dx.doi.org/10.21236/ada458702.

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Decleir, Cyril, Mohand-Saïd Hacid, and Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.

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Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].
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McCall, Jamie, Nora Anzawi, Miles Zeller, and James Onorevole. Growth, Equity, and Individual Welfare: A Theoretical Framework for “Moving the Needle” on CDFI Impact Evaluation. Carolina Small Business Development Fund and AltCap, January 2023. http://dx.doi.org/10.46712/evaluation.frameworks.

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Community development financial institutions (CDFI) are grassroots entities that guide sustainable economic growth by prioritizing the needs of marginalized populations. Traditionally, CDFIs have been evaluated by the extent to which their activities foster local development or lessen economic inequities. Our analysis suggests this phenomenon is the result of deeply entrenched alliances between the public sector and the interests of CDFI stakeholders. These institutions are major resource providers and thus have set the narrative around CDFI impact measurement and evaluation. We propose an alternative framework that prioritizes changes in individual welfare which foster economic autonomy and improve relationships with community institutions. Our framework is admittedly theoretical, and further iteration will be required to operationalize it into a workable concept. Ultimately though, the existing framework around these issues is broken – and we find scant evidence that it can be salvaged.
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Qin, Hua, Yanu Prasetyo, Christine Sanders, Elizabeth Prentice, and Muh Syukron. Perceptions and behaviors in response to the novel coronavirus disease 2019 (COVID-19) : reports on major survey findings. University of Missouri, Division of Applied Social Sciences, 2020. http://dx.doi.org/10.32469/10355/79261.

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The United States has been affected by an extensive novel coronavirus (COVID-19) outbreak since March 2020. On March 9, 2020 we started an online survey of people’s perceptions and behaviors related to this issue in Missouri and adjacent states (Kansas, Iowa, Illinois, and Arkansas). The survey was ended on June 9, 2020 and in total 7,392 surveys were completed. In order to assess how attitudes and behaviors related to COVID-19 may change over time, two follow-up surveys were conducted with those respondents who indicated interest in the re-surveys and provided an email address. These two working reports summarize major results of the initial survey and three survey waves, including respondents’ perceived severity of the COVID-19 outbreak, sources of information, knowledge about COVID-19, perceptions of COVID-19 risk, satisfaction with management entities, and preventive actions.
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Salz, R. Entities Involved in the IETF Standards Process. RFC Editor, June 2022. http://dx.doi.org/10.17487/rfc9281.

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Aldrich, Susan. Web Services Backplane: Infrastructure for Web Services. Boston, MA: Patricia Seybold Group, January 2003. http://dx.doi.org/10.1571/la1-2-03cc.

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Nottingham, M. Web Linking. RFC Editor, October 2010. http://dx.doi.org/10.17487/rfc5988.

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Kramer, Mitch. Nina Web. Boston, MA: Patricia Seybold Group, January 2014. http://dx.doi.org/10.1571/pr01-30-14cc.

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