The introduction of the schema.org vocabulary was a big step towards making websites machine read- and understandable. Due to schema.org's RDF-like nature storing annotations in a graph database is easy and efficient. In this paper the authors show how they gather touristic data in the Austrian region of Tirol and provide this data publicly in a knowledge graph. The definition of subsets of the vocabulary is followed by providing means to map data sources efficiently to schema.org and then store the annotated content into the graph. To showcase the consumption of the touristic data four scenarios are described which use the knowledge graph for real life applications and data analysis.
The Tyrolean Tourism Knowledge Graph contains static (e.g. phone number, address) and dynamic (e.g. accommodation offers) data based on schema.org annotations collected from different sources such as Destination Management Organizations (DMO) and Geographical Information Systems (GIS). In our previous work @cite_5 , we explained how we annotated the relevant data in the region with schema.org from different sources. Our knowledge graph consolidates these annotations and enables intelligent applications like chatbots to contribute the digitalization of tourism in Tyrol. Additionally, since we store the historical data, the knowledge graph allows data analytics to provide insights from the region.
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The tourism industry has a significant impact on the world’s economy, contributes 10.2 of the world’s gross domestic product in 2016. It becomes a very competitive industry, where having a strong online presence is an essential aspect for business success. To achieve this goal, the proper usage of latest Web technologies, particularly schema.org annotations is crucial. In this paper, we present our effort to improve the online visibility of touristic service providers in the region of Tyrol, Austria, by creating and deploying a substantial amount of semantic annotations according to schema.org, a widely used vocabulary for structured data on the Web. We started our work from Tourismusverband (TVB) Mayrhofen-Hippach and all touristic service providers in the Mayrhofen-Hippach region and applied the same approach to other TVBs and regions, as well as other use cases. The rationale for doing this is straightforward. Having schema.org annotations enables search engines to understand the content better, and provide better results for end users, as well as enables various intelligent applications to utilize them. As a direct consequence, the region of Tyrol and its touristic service increase their online visibility and decrease the dependency on intermediaries, i.e. Online Travel Agency (OTA). The past decade has seen the emergence of web-scale structured and linked semantic knowledge resources (e.g., Freebase, DBPedia). These semantic knowledge graphs provide a scalable “schema for the web”, representing a significant opportunity for the spoken language understanding (SLU) research community. This paper leverages these resources to bootstrap a web-scale semantic parser with no requirement for semantic schema design, no data collection, and no manual annotations. Our approach is based on an iterative graph crawl algorithm. From an initial seed node (entity-type), the method learns the related entity-types from the graph structure, and automatically annotates documents that can be linked to the node (e.g., Wikipedia articles, web search documents). Following the branches, the graph is crawled and the procedure is repeated. The resulting collection of annotated documents is used to bootstrap webscale conditional random field (CRF) semantic parsers. Finally, we use a maximum-a-posteriori (MAP) unsupervised adaptation technique on sample data from a specific domain to refine the parsers. The scale of the unsupervised parsers is on the order of thousands of domains and entity-types, millions of entities, and hundreds of millions of relations. The precision-recall of the semantic parsers trained with our unsupervised method approaches those trained with supervised annotations. Concerned about the Turing test's ability to correctly evaluate if a system exhibits human-like intelligence, the Winograd Schema Challenge (WSC) has been proposed as an alternative. A Winograd Schema consists of a sentence and a question. The answers to the questions are intuitive for humans but are designed to be difficult for machines, as they require various forms of commonsense knowledge about the sentence. In this paper we demonstrate our progress towards addressing the WSC. We present an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with them to come up with the answer. In the process we develop a semantic parser (www.kparser.org). We show that our approach works well with respect to a subset of Winograd schemas. Anafora is a newly-developed open source web-based text annotation tool built to be lightweight, flexible, easy to use and capable of annotating with a variety of schemas, simple and complex. Anafora allows secure web-based annotation of any plaintext file with both spanned (e.g. named entity or markable) and relation annotations, as well as adjudication for both types of annotation. Anafora offers automatic set assignment and progress-tracking, centralized and humaneditable XML annotation schemas, and filebased storage and organization of data in a human-readable single-file XML format. Performance measurement of tourism websites is becoming a critical issue for effective online marketing. The aim of this article is to analyse the effectiveness of entries (visit behaviour and length of sessions) depending on their traffic source: direct visit, in-link entries (for instance, en.wikipedia.org), and search engine visits (for example, Google). For this purpose, time series analysis of Google Analytics data is made use of. This method could be interesting for any tourism website optimizer. Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair. In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, and although the introduction of regions serves to consolidate the sparse data, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality. The success of the Semantic Web hinges on being able to produce semantic markups on Web pages and their components, in a way that is cost-effective and consistent with adopted schemas and ontologies. Since images are an essential component of the Web, this work focuses on an intelligent approach to semantic annotation of images. We propose a three-layer architecture, in which the bottom layer organizes visual information extracted from the raw image contents, which are mapped to semantically meaningful keywords in the middle layer, which are then connected to schemas and ontologies on the top layer. Our key contribution is the use of machine learning algorithms for user-assisted, semi-automatic image annotation, in such a way that the knowledge of previously annotated images - both at metadata and visual levels - is used to speed up the annotation of subsequent images within the same domain (ontology) as well as to improve future query and retrieval of annotated images. Structured data is picking up on the Web, particularly in the search world. Schema.org, jointly initiated by Google, Microsoft, and Yahoo! provides a hierarchical set of vocabularies to embed metadata in HTML pages for an enhanced search and browsing experience. RDFa-Lite, Microdata and JSON-LD as lower semantic techniques have gained more attention by Web users to markup Web pages and even emails based on Schema.org. However, from the user interface point of view, we still lack user-friendly tools that facilitate the process of structured content authoring. The majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way how humans interact with information. In this paper we present RDFaCE as an implementation of WYSIWYM (What-You-See-Is-What-You-Mean) concept for direct manipulation of semantically structured content in conventional modalities. RDFaCE utilizes on-the-fly form generation based on Schema.org vocabulary for embedding metadata within Web documents. Furthermore, it employs external NLP services to enable automatic annotation of entities and to suggest URIs for entities. RDFaCE is written as a plugin for TinyMCE WYSIWYG editor thereby can be easily integrated into existing content management systems. The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, for example, to explain search results, to explore knowledge spaces, to semantically enrich textual documents, or to feed knowledge-intensive applications such as recommender systems. In this work, we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia, which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets: a dataset of sounds composed of tags, textual descriptions, and user’s download information gathered from Freesound.org and a dataset of songs that mixes song textual descriptions with tags and user’s listening habits extracted from Songfacts.com and Last.fm, respectively. Results show significant improvements with respect to state-of-the-art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty. A general-purpose text annotation tool called Knowtator is introduced. Knowtator facilitates the manual creation of annotated corpora that can be used for evaluating or training a variety of natural language processing systems. Building on the strengths of the widely used Protege knowledge representation system, Knowtator has been developed as a Protege plug-in that leverages Protege's knowledge representation capabilities to specify annotation schemas. Knowtator's unique advantage over other annotation tools is the ease with which complex annotation schemas (e.g. schemas which have constrained relationships between annotation types) can be defined and incorporated into use. Knowtator is available under the Mozilla Public License 1.1 at http: bionlp.sourceforge.net Knowtator. Annotated corpora of the size needed for modern computational linguistics research cannot be created by small groups of hand annotators. One solution is to exploit collaborative work on the Web and one way to do this is through games like the ESP game. Applying this methodology however requires developing methods for teaching subjects the rules of the game and evaluating their contribution while maintaining the game entertainment. In addition, applying this method to linguistic annotation tasks like anaphoric annotation requires developing methods for presenting text and identifying the components of the text that need to be annotated. In this paper we present the first version of Phrase Detectives (http: www.phrasedetectives.org), to our knowledge the first game designed for collaborative linguistic annotation on the Web. This paper provides a comprehensive study on annotations by defining their contours and complexity. This work adds a new complementary approach to the usual case and user studies, and also investigates history in order to benefit from previous knowledge and our cultural heritage. This study emphasizes an aspect which has never previously been taken into account: the temporal dimension involved in annotations. Moreover, it discusses both the notion of hypertext between documents and annotations and the idea of annotations as context for documents. The study gives the necessary historical and cultural background to derive a set of key features of annotations that must be taken into account when designing systems that have to support the management of digital annotations on digital contents. Motivation: In the Life Sciences, guidelines, checklists and ontologies describing what metadata is required for the interpretation and reuse of experimental data are emerging. Data producers, however, may have little experience in the use of such standards and require tools to support this form of data annotation. Results: RightField is an open source application that provides a mechanism for embedding ontology annotation support for Life Science data in Excel spreadsheets. Individual cells, columns or rows can be restricted to particular ranges of allowed classes or instances from chosen ontologies. The RightField-enabled spreadsheet presents selected ontology terms to the users as a simple drop-down list, enabling scientists to consistently annotate their data. The result is ‘semantic annotation by stealth’, with an annotation process that is less error-prone, more efficient, and more consistent with community standards. Availability and implementation: RightField is open source under a BSD license and freely available from http: www.rightfield.org.uk Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from raw text. In most previous applications of universal schema, each textual pattern is represented as a single embedding, preventing generalization to unseen patterns. Recent work employs a neural network to capture patterns' compositional semantics, providing generalization to all possible input text. In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation. We evaluate our model through extensive experiments on the English and Spanish TAC KBP benchmark, outperforming the top system from TAC 2013 slot-filling using no handwritten patterns or additional annotation. We also consider a multilingual setting in which English training data entities overlap with the seed KB, but Spanish text does not. Despite having no annotation for Spanish data, we train an accurate predictor, with additional improvements obtained by tying word embeddings across languages. Furthermore, we find that multilingual training improves English relation extraction accuracy. Our approach is thus suited to broad-coverage automated knowledge base construction in a variety of languages and domains. Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models took off in the eighties and early nineties alongside object-oriented models. Their influence gradually died out with the emergence of other database models, in particular geographical, spatial, semistructured, and XML. Recently, the need to manage information with graph-like nature has reestablished the relevance of this area. The main objective of this survey is to present the work that has been conducted in the area of graph database modeling, concentrating on data structures, query languages, and integrity constraints. The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs ( KGs ) are a prominent example. KGs maintain both numerical and non-numerical facts, with the support of an underlying schema. These facts are usually accompanied by a confidence score that witnesses how likely is for them to hold. Despite their popularity, most of existing KGs focus on static data thus impeding the availabilityof timewise knowledge. What is missing is a comprehensive solution for the management of uncertain and temporal data in KGs . The goal of this paper is to fill this gap. We rely on two main ingredients. The first is a numerical extension of Markov Logic Networks (MLNs) that provide the necessary underpinning to formalize the syntax and semantics of uncertain temporal KGs . The second is a set of Datalog constraints with inequalities that extend the underlying schema of the KGs and help to detect inconsistencies. From a theoretical point of view, we discuss the complexity of two important classes of queries for uncertain temporal KGs: maximuma-posteriori and conditional probability inference. Due to the hardness of these problems and the fact that MLN solvers do not scale well, we also explore the usage of Probabilistic Soft Logics (PSL) as a practical tool to support our reasoning tasks. We report on an experimental evaluation comparing the MLN and PSL approaches. The goal of data integration is to provide a uniform access to a set of heterogeneous data sources, freeing the user from the knowledge about where the data are, how they are stored, and how they can be accessed. The problem of designing effective data integration solutions has been addressed by several research and development projects in the last years. One of the outcomes of this research work is a clear conceptual architecture for data integration1. According to this architecture [9], the main components of a data integration system are the global schema, the sources, and the mapping. Thus, a data integration system is seen as a triple 〈G,S,M〉, where: Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. In this work, we propose DoSeR (Disambiguation of Semantic Resources), a (named) entity disambiguation framework that is knowledge-base-agnostic in terms of RDF (e.g. DBpedia) and entity-annotated document knowledge bases (e.g. Wikipedia). Initially, our framework automatically generates semantic entity embeddings given one or multiple knowledge bases. In the following, DoSeR accepts documents with a given set of surface forms as input and collectively links them to an entity in a knowledge base with a graph-based approach. We evaluate DoSeR on seven different data sets against publicly available, state-of-the-art (named) entity disambiguation frameworks. Our approach outperforms the state-of-the-art approaches that make use of RDF knowledge bases and or entity-annotated document knowledge bases by up to 10 F1 measure. In order to create distributed automation systems, it is required to ensure their interoperability; however, ensuring interoperability between heterogeneous systems (using different communication protocols, data formats, and semantics) is a challenging task. Among many interoperability challenges, this paper addresses issues concerning semantic and data interoperability, namely, it provides a contribution to support the semantic compatibility verification and the generation of translators for XML messages. Translators are generated based on XML-schemas that are annotated with a reference ontology. We base our annotations on an extension of an existing declarative annotation method. In particular, our extension explicitly addresses ambiguities of annotations, schema instance mismatches, and coverage mismatches that frequently occur on Internet-of-things message schemas. We have evaluated our approach based on a set of interaction scenarios from the domain of the Arrowhead Project. A tool prototype that supports the semantic compatibility verification and the generation of XML translators is available at http: gres.uninova.pt tag . Large collaborative datasets offer the challenging opportunity of creating systems capable of extracting knowledge in the presence of noisy data. In this work we explore the ability to automatically learn tag semantics by mining a global georeferenced image collection crawled from Flickr with the aim of improving an automatic annotation system. We are able to categorize sets of tags as places, landmarks, and visual descriptors. By organizing our dataset of more than 1.69 million images using a quadtree we can efficiently find geographic areas with sufficient density to provide useful results for place and landmark extraction. Precision-recall curves for our techniques compared with previous existing work used to identify place tags and manual groundtruth landmark annotation show the merit of our methods applied on a world scale.