Faceted Semantic Search for Personalized Social Search
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Towards Ontology Based BPMN Implementation. Sophea Chhun, Néjib Moalla, Yacine Ouzrout
Towards ontology based BPMN Implementation. Sophea Chhun, Néjib Moalla, Yacine Ouzrout To cite this version: Sophea Chhun, Néjib Moalla, Yacine Ouzrout. Towards ontology based BPMN Implementation.. SKIMA, 6th Conference on Software Knowledge Information Management and Applications., Jan 2012, Chengdu, China. 8 p. hal-01551452 HAL Id: hal-01551452 https://hal.archives-ouvertes.fr/hal-01551452 Submitted on 6 Nov 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1 Towards ontology based BPMN implementation CHHUN Sophea, MOALLA Néjib and OUZROUT Yacine University of Lumiere Lyon2, laboratory DISP, France Natural language is understandable by human and not machine. None technical persons can only use natural language to specify their business requirements. However, the current version of Business process management and notation (BPMN) tools do not allow business analysts to implement their business processes without having technical skills. BPMN tool is a tool that allows users to design and implement the business processes by connecting different business tasks and rules together. The tools do not provide automatic implementation of business tasks from users’ specifications in natural language (NL). Therefore, this research aims to propose a framework to automatically implement the business processes that are expressed in NL requirements. -
EVERYTHING YOU NEED to KNOW ABOUT SEMANTIC SEO by GENNARO CUOFANO, ANDREA VOLPINI, and MARIA SILVIA SANNA the Semantic Web Is Here
WHITE PAPER EVERYTHING YOU NEED TO KNOW ABOUT SEMANTIC SEO BY GENNARO CUOFANO, ANDREA VOLPINI, AND MARIA SILVIA SANNA The Semantic Web is here. Those that are taking advantage of Semantic Technologies to build a Semantic SEO strategy are benefiting from staggering results. From a research paper put together with the team atWordLift , presented at SEMANTiCS 2017, we documented that structured data is compelling from the digital marketing standpoint. For instance, on the analysis of the design-focused website freeyork.org, after three months of using structured data in their WordPress website we saw the following improvements: • +12.13% new users • +18.47% increase in organic traffic • +2.4 times increase in page views • +13.75% of sessions duration In other words, many still think of Semantic Technologies belonging to the future, when in reality quite a few players in the digital marketing space are taking advantage of them already. Semantic SEO is a new and powerful way to make your content strategy more effective. In this article/guide I will explain from scratch what Semantic SEO is and why it’s important. Why Semantic SEO? In a nutshell, search engines need context to understand a query properly and to fetch relevant results for it. Contexts are built using words, expressions, and other combinations of words and links as they appear in bodies of knowledge such as encyclopedias and large corpora of text. Semantic SEO is a marketing technique that improves the traffic of a website byproviding meaningful data that can unambiguously answer a specific search intent. It is also a way to create clusters of content that are semantically grouped into topics rather than keywords. -
On Using Product-Specific Schema.Org from Web Data Commons: an Empirical Set of Best Practices
On using Product-Specific Schema.org from Web Data Commons: An Empirical Set of Best Practices Ravi Kiran Selvam Mayank Kejriwal [email protected] [email protected] USC Information Sciences Institute USC Information Sciences Institute Marina del Rey, California Marina del Rey, California ABSTRACT both to advertise and find products on the Web, in no small part Schema.org has experienced high growth in recent years. Struc- due to the ability of search engines to make good use of this data. tured descriptions of products embedded in HTML pages are now There is also limited evidence that structured data plays a key role not uncommon, especially on e-commerce websites. The Web Data in populating Web-scale knowledge graphs such as the Google Commons (WDC) project has extracted schema.org data at scale Knowledge Graph that are essential to modern semantic search from webpages in the Common Crawl and made it available as an [17]. RDF ‘knowledge graph’ at scale. The portion of this data that specif- However, even though most e-commerce platforms have their ically describes products offers a golden opportunity for researchers own proprietary datasets, some resources do exist for smaller com- and small companies to leverage it for analytics and downstream panies, researchers and organizations. A particularly important applications. Yet, because of the broad and expansive scope of this source of data is schema.org markup in webpages. Launched in data, it is not evident whether the data is usable in its raw form. In the early 2010s by major search engines such as Google and Bing, this paper, we do a detailed empirical study on the product-specific schema.org was designed to facilitate structured (and even knowl- schema.org data made available by WDC. -
Implementation of Intelligent Semantic Web Search Engine
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 4 Issue 04, April-2015 Implementation of Intelligent Semantic Web Search Engine Dinesh Jagtap*1, Nilesh Argade1, Shivaji Date1, Sainath Hole1, Mahendra Salunke2 1Department of Computer Engineering, 2Associate Professor, Department of Computer Engineering Sinhgad Institute of Technology and Science, Pune, Maharashtra, India 411041. II. BACKGROUND Abstract—Search engines are design for to search particular The idea of search engine and info retrieval from search information for a large database that is from World Wide engine is not a new concept. The interesting thing about Web. There are lots of search engines available. Google, traditional search engine is that different search engine will yahoo, Bing are the search engines which are most widely provide different result for the same query. While used search engines in today. The main objective of any search engines is to provide particular or required information was available in web, we have some fields of information with minimum time. The semantics web search problem in search engines. Information retrieval by engines are the next version of traditional search engines. The searching information on the web is not a fresh idea but has main problem of traditional search engines is that different challenges when it is compared to general information retrieval from the database is difficult or takes information retrieval. Different search engines return long time. Hence efficiency of search engines is reduced. To different search results due to the variation in indexing and overcome this intelligent semantic search engines are search process. Google, Yahoo, and Bing have been out introduced. -
Harnessing the Power of Folksonomies for Formal Ontology Matching On-The-Fly
Edinburgh Research Explorer Harnessing the power of folksonomies for formal ontology matching on the fly Citation for published version: Togia, T, McNeill, F & Bundy, A 2010, Harnessing the power of folksonomies for formal ontology matching on the fly. in Proceedings of the ISWC workshop on Ontology Matching. <http://ceur-ws.org/Vol- 689/om2010_poster4.pdf> Link: Link to publication record in Edinburgh Research Explorer Document Version: Early version, also known as pre-print Published In: Proceedings of the ISWC workshop on Ontology Matching General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 01. Oct. 2021 Harnessing the power of folksonomies for formal ontology matching on-the-y Theodosia Togia, Fiona McNeill and Alan Bundy School of Informatics, University of Edinburgh, EH8 9LE, Scotland Abstract. This paper is a short introduction to our work on build- ing and using folksonomies to facilitate communication between Seman- tic Web agents with disparate ontological representations. We briey present the Semantic Matcher, a system that measures the semantic proximity between terms in interacting agents' ontologies at run-time, fully automatically and minimally: that is, only for semantic mismatches that impede communication. -
Expert Knowledge Management Based on Ontology in a Digital Library
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by idUS. Depósito de Investigación Universidad de Sevilla EXPERT KNOWLEDGE MANAGEMENT BASED ON ONTOLOGY IN A DIGITAL LIBRARY Antonio Martín, Carlos León Departamento de Tecnología Electrónica, Seville University, Avda. Reina Mercedes S/N, Seville, Spain [email protected], [email protected] Keywords: Ontology, web services, Case-based reasoning, Digital Library, knowledge management, Semantic Web. Abstract: The architecture of the future Digital Libraries should be able to allow any users to access available knowledge resources from anywhere and at any time and efficient manner. Moreover to the individual user, there is a great deal of useless information in addition to the substantial amount of useful information. The goal is to investigate how to best combine Artificial Intelligent and Semantic Web technologies for semantic searching across largely distributed and heterogeneous digital libraries. The Artificial Intelligent and Semantic Web have provided both new possibilities and challenges to automatic information processing in search engine process. The major research tasks involved are to apply appropriate infrastructure for specific digital library system construction, to enrich metadata records with ontologies and enable semantic searching upon such intelligent system infrastructure. We study improving the efficiency of search methods to search a distributed data space like a Digital Library. This paper outlines the development of a Case- Based Reasoning prototype system based in an ontology for retrieval information of the Digital Library University of Seville. The results demonstrate that the used of expert system and the ontology into the retrieval process, the effectiveness of the information retrieval is enhanced. -
Folksannotation: a Semantic Metadata Tool for Annotating Learning Resources Using Folksonomies and Domain Ontologies
FolksAnnotation: A Semantic Metadata Tool for Annotating Learning Resources Using Folksonomies and Domain Ontologies Hend S. Al-Khalifa and Hugh C. Davis Learning Technology Research Group, ECS, The University of Southampton, Southampton, UK {hsak04r/hcd}@ecs.soton.ac.uk Abstract Furthermore, web resources stored in social bookmarking services have potential for educational There are many resources on the Web which are use. In order to realize this potential, we need to add suitable for educational purposes. Unfortunately the an extra layer of semantic metadata to the web task of identifying suitable resources for a particular resources stored in social bookmarking services; this educational purpose is difficult as they have not can be done via adding pedagogical values to the web typically been annotated with educational metadata. resources so that they can be used in an educational However, many resources have now been annotated in context. an unstructured manner within contemporary social In this paper, we will focus on how to benefit from 2 bookmaking services. This paper describes a novel tool social bookmarking services (in particular del.icio.us ), called ‘FolksAnnotation’ that creates annotations with as vehicles to share and add semantic metadata to educational semantics from the del.icio.us bookmarked resources. Thus, the research question we bookmarking service, guided by appropriate domain are tackling is: how folksonomies can support the ontologies. semantic annotation of web resources from an educational perspective ? 1. Introduction The organization of the paper is as follows: Section 2 introduces folksonomies and social bookmarking Metadata standards such as Dublin Core (DC) and services. In section 3, we review some recent research IEEE-LOM 1 have been widely used in e-learning on folksonomies and social bookmarking services. -
Learning to Match Ontologies on the Semantic Web
The VLDB Journal manuscript No. (will be inserted by the editor) Learning to Match Ontologies on the Semantic Web AnHai Doan1, Jayant Madhavan2, Robin Dhamankar1, Pedro Domingos2, Alon Halevy2 1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA fanhai,[email protected] 2 Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA fjayant,pedrod,[email protected] Received: date / Revised version: date Abstract On the Semantic Web, data will inevitably come and much of the potential of the Web has so far remained from many different ontologies, and information processing untapped. across ontologies is not possible without knowing the seman- In response, researchers have created the vision of the Se- tic mappings between them. Manually finding such mappings mantic Web [BLHL01], where data has structure and ontolo- is tedious, error-prone, and clearly not possible at the Web gies describe the semantics of the data. When data is marked scale. Hence, the development of tools to assist in the ontol- up using ontologies, softbots can better understand the se- ogy mapping process is crucial to the success of the Seman- mantics and therefore more intelligently locate and integrate tic Web. We describe GLUE, a system that employs machine data for a wide variety of tasks. The following example illus- learning techniques to find such mappings. Given two on- trates the vision of the Semantic Web. tologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well- founded probabilistic definitions to several practical similar- Example 1 Suppose you want to find out more about some- ity measures, and show that GLUE can work with all of them. -
Kgvec2go – Knowledge Graph Embeddings As a Service
Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 5641–5647 Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC KGvec2go – Knowledge Graph Embeddings as a Service Jan Portisch (1,2), Michael Hladik (2), Heiko Paulheim (1) (1) University of Mannheim - Data and Web Science Group, (2) SAP SE (1) B 6, 26 68159 Mannheim, Germany (2) Dietmar-Hopp Allee 16, 60190, Walldorf, Germany [email protected], [email protected], [email protected] Abstract In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model. Keywords: RDF2Vec, knowledge graph embeddings, knowledge graphs, background knowledge resources 1. Introduction The data set presented here allows to compare the perfor- A knowledge graph (KG) stores factual information in the mance of different knowledge graph embeddings on differ- form of triples. Today, many such graphs exist for various ent application tasks. It further allows to combine embed- domains, are publicly available, and are being interlinked. dings from different knowledge graphs in downstream ap- As of 2019, the linked open data cloud (Schmachtenberg plications. We evaluated the embeddings on three semantic et al., 2014) counts more than 1,000 data sets with multiple gold standards and also explored the combination of em- billions of unique triples.1 Knowledge graphs are typically beddings. -
Semantic Search on the Web
Semantic Web — Interoperability, Usability, Applicability 0 (0) 1 1 IOS Press Semantic Search on the Web Bettina Fazzinga a and Thomas Lukasiewicz b Semantic Web technology for interpreting Web search a Dipartimento di Elettronica, Informatica e queries and resources relative to one or more under- Sistemistica, Università della Calabria, Italy lying ontologies, describing some background domain E-mail: [email protected] knowledge, in particular, by connecting the Web re- b Computing Laboratory, University of Oxford, UK sources to semantic annotations, or by extracting se- E-mail: [email protected] mantic knowledge from Web resources. Such a search usually also aims at allowing for more complex Web search queries whose evaluation involves reasoning over the Web. Another common use of the notion of semantic search on the Web is the one as search in the Abstract. Web search is a key technology of the Web, since large datasets of the Semantic Web as a future substi- it is the primary way to access content on the Web. Cur- tute of the current Web. This second use is closely re- rent standard Web search is essentially based on a combi- nation of textual keyword search with an importance rank- lated to the first one, since the above semantic annota- ing of the documents depending on the link structure of the tion of Web resources, or alternatively the extraction of Web. For this reason, it has many limitations, and there are a semantic knowledge from Web resources, actually cor- plethora of research activities towards more intelligent forms responds to producing a knowledge base, which may of search on the Web, called semantic search on the Web, be encoded using Semantic Web technology. -
An Efficient Wikipedia Semantic Matching Approach to Text Document Classification
Information Sciences 393 (2017) 15–28 Contents lists available at ScienceDirect Information Sciences journal homepage: www.elsevier.com/locate/ins An efficient Wikipedia semantic matching approach to text document classification ∗ ∗ Zongda Wu a, , Hui Zhu b, , Guiling Li c, Zongmin Cui d, Hui Huang e, Jun Li e, Enhong Chen f, Guandong Xu g a Oujiang College, Wenzhou University, Wenzhou, Zhejiang, China b Wenzhou Vocational College of Science and Technology, Wenzhou, Zhejiang, China c School of Computer Science, China University of Geosciences, Wuhan, China d School of Information Science and Technology, Jiujiang University, Jiangxi, China e College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang, China f School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China g Faculty of Engineering and IT, University of Technology, Sydney, Australia a r t i c l e i n f o a b s t r a c t Article history: A traditional classification approach based on keyword matching represents each text doc- Received 28 July 2016 ument as a set of keywords, without considering the semantic information, thereby, re- Revised 6 January 2017 ducing the accuracy of classification. To solve this problem, a new classification approach Accepted 3 February 2017 based on Wikipedia matching was proposed, which represents each document as a con- Available online 7 February 2017 cept vector in the Wikipedia semantic space so as to understand the text semantics, and Keywords: has been demonstrated to improve the accuracy of classification. However, the immense Wikipedia matching Wikipedia semantic space greatly reduces the generation efficiency of a concept vector, re- Keyword matching sulting in a negative impact on the availability of the approach in an online environment. -
Schema Org Markup for Organization Logos
Schema Org Markup For Organization Logos Unspectacular Shelley bopping extempore while Devon always gurgled his xenon decongest sevenfold, he stupefied so coequally. stalagmometersRetroflexed and allodialpiecemeal. Michael never shog his celandine! Unreposeful and horned Darius pausing her cribbers ooze okay and Another example of information, or a local business markup for schema markup to get a huge source cmses, if your ecommerce store Schema markup is level of the strong potent forms of SEO however concern also happens to. Data said the off page showing the website social channels and logo. Why structured data and schema markup are no Flare. How both Add Schemaorg Markup to WordPress for Better SEO. SCHEMAORG Basic Markup SEO Agency Serpact. Healthcare Schema Markup and move It especially to Your SEO. What Google's Support of Schemaorg Logo Data iProspect. How people Add Schema Markup to WordPress and Kinsta. Schemaorg is a collaborative community activity with a mission to uphold maintain and. Schemaorg Tutorial w3resource. Use Schemaorg's organization markup to notice to Google the preferred logo Google has integrity they should honor that presumably in the absence of a Google. What and sending data types, seo plugin settings fields array output schema org markup for organization logos coming down on plugin settings page post type of, schema on your html tag. Org's Organization Markup in your SEO strategic plan Business Visibility on Google A squat of simple code on your website and you're logo will be. Wondering if you use html page. After all this search engines exist such as entities intertwine and it is a search on.