eTourism Platform

Due to the increase of Linked Data published on the Web, it is more likely to find information related to real life concepts and increase the information associated with them in the form of User Generated Content (UGC) using Semantic Annotation techniques. In particular, we consider a tourism scenario, where more detailed information about attractions of a city and other POIs (like monuments, hotels, etc.) can be extracted from datasets in the Web of Data. For example, in DBpedia, we have information about many tourist attractions, such as the Colosseum, or St. Peter’s Basilica in Rome. Thus, when a mobile user is in a tourism situation like visiting a city, the processing of the information associated tourist attractions of a city or other POIs could enable more reliable recommendations for more interesting places. This use case is detailed in Subsection Use Case.

This is possible because any resource in Web of Data has a URI, i.e. it is uniquely identified. Hence, it is possible to access it to get the information about the object it represents. Nowadays it is also simpler to publish information on the Web. Any user can easily insert new content in textual or multimedia format (with the use of contemporary mobile devices, it is becoming more and more real time), e.g. probably it is possible that some news appears on Twitter before than on any news portal. For example, considering a car accident in a city, witnesses can post information or content about it even before the local press agency arrives. Last but not least, it is also easier to link information with already existing resources. In fact, much effort has been done by the Linked Data community to provide more ways to increase publication of Linked Data. There are plenty of tools to annotate raw data and link them, e.g. DBpedia Spotlight [88] or the FI-WARE Semantic Annotator GE (for which details are provided in Subsection Semantic Annotator).

Another element to consider is that the structure of Linked Data can be ex- ploited. In a tourism use case, it may be useful consider only hotels or only parks within a city. This can be done since Linked Data is structured: for example

9_{https://www.dropbox.com/sh/0q8d2mcbko9e2oj/AAASh-YHGz0MmG_} Z8hH6mfWOa?dl=0

considering DBpedia, any resource belongs to one or more categories and cate- gories of hotels and monuments exist. In addition, categories are organized in a tree hierarchy; thus it is possible to consider more general or more specific cate- gories. If we refer to Saint Petersburg, then we can also consider the hotels (asso- ciated to Category:Hotels_in_Saint_Petersburg), monuments (corresnding to Category:Monuments_and_memorials_in_Saint_Petersburg) and so on. The whole city may be mapped to Category:Buildings_and_structures_in_Saint _Petersburg. Finally, information in Linked Data and its structure can be used to increase the user experience.

In the following, we propose a platform to support tourism use cases, such as the one described in Subsection Use Case, which exploit ReDyAl to provide recommendations based on Linked Data. The overall system is shown in Figure5.4. Our platform communicates with mobile devices and with the Web of Data using web interactions, and it is made up of three main modules: a recommender system that implements ReDyAl, a UGC manager, and a semantic annotator. The first can provide information from Web of Data and UGC stored, while the second stores and retrieves UGC, which is also linked to resources belonging to the Web of Data using the annotator. In the following, we provide a description of each component of the platform (apart from the recommender system which is based on ReDyAl, which has already been presented in Section5.3).

Semantic Annotator

Thanks to the FI-WARE EU project,10it is possible to reuse existing components for creating modern applications and services. In this case, we have reused the Semantic Annotator GE, which is publicly available from the FI-WARE catalogue.11 The decision of reusing an existing component or creating a new one was taken after careful analysis, in which it was concluded that the component that provides the service offers greater advantages and reduces the time of integration on the platform. Besides, it encourages the standardization of the existing modules.

To extract semantic information, each plain text information associated with received content is analyzed by the Semantic Annotator GE (which architecture is shown in Figure5.5). First, the Text Processor module identifies the source language; then a morphological analysis is performed using FreeLing12 configured for the identified language. From this analysis, proper nouns lemmas are extracted while other part-of-speech are discarded. At this time, non-numeric proper nouns lemmas with a score of at least 0.2 are preserved and merged with plain text tags to compute a well-defined list of unique (multi) words. At this stage, the module uses term frequency to process the title further and to extract other potential relevant words.

Fig. 5.5 Semantic Annotator GE at a glance.

The next step involves the Semantic Broker, which is assisted by a set of resolvers that perform full-text or term-based analysis based on the previous output. Such resolvers are aimed at providing candidate semantic concepts referring to Linked Data as well as additional related information if it is available. Resolvers may be domain or language specific, or general purpose. For term-based analysis, each word of the previously computed list is individually processed to identify a list of

10_{https://www.fiware.org/}

11_{http://catalogue.fi-ware.org/enablers/semantic-annotation} 12_{http://nlp.lsi.upc.edu/freeling/}

candidate Linked Data resources to match with. A set of predefined services, such as DBpedia, Sindice13 and Evri14 are invoked in parallel.

The Semantic Filtering module processes candidate Linked Data resources re- ceived by the broker and performs a disambiguation based on the DBpedia score and the string similarity between each surface form and its corresponding list of candidates, which relies on the Jaro-Winkler distance [89]. This function aims at maximizing both values to identify the “preferred” candidate. In this process, after several empirical tests, candidates with distance lower than 0.8 are discarded at this stage, unless their DBpedia score is the maximum. Automatic annotation is performed using the “preferred” candidate identified during this step.

UGC Management

This layer of the platform is responsible for saving UGC and its semantically enriched version. Thus, for each given UGC entry, we have:

• UGC itself (any given multimedia file);

• Original associated plain information (stored in a SQL database);

• Associated semantic information represented in RDF (stored in a triple store). Additionally, this layer offers the means to retrieve specific content, either through the invocation of a REST API (to get the multimedia contents attached) or performing more complex SPARQL queries in the public endpoint.

Use Case

The previously described platform can be applied in a tourism scenario and can be exploited by an application that assists tourists by providing them suggestions about places to visit, accommodations, and other points of interest (POIs). Furthermore, it allows users to share their experience by providing content such as pictures, videos, reviews, and comments about places they have been. Then, this content is available

13_{http://sindice.com} 14_{http://www.evri.com}

to other users and can be exploited to enrich information about tourist attractions and other facilities.

For example, if a user is in Saint Petersburg, some tourist attractions are the Peter and Paul Fortress, the Saint Isaac’s Cathedral and the Hermitage Museum. All of these tourist attractions have a corresponding representation in DBpedia; thus they support user interactions, since users can decide if visit them or not by consulting the information provided by DBpedia. There are two possible interactions:

1. user device receives information about nearby tourist attractions;

2. user publishes information regarding the tourist attractions from his device (generates content).

The first enables users to be provided with recommendations about tourist attractions, while the latter allows users to share their experiences and increase the amount of available information to other tourists.

Fig. 5.6 Possible interactions in a eTourism use case.

Figure5.6illustrates the two different possible interactions for a user in Saint Pe- tersburg. He is at the Hermitage Museum, and his device receives recommendations about the Peter and Paul Fortress and the Saint Isaac’s Cathedral, which are nearby. He can access the DBpedia information related to each of them and also the related UGC shared by other users (1, 2). Then he decides to take a picture with his friends and make it available to other tourists; thus he adds information to the Hermitage museum by using the semantic annotator and UGC manager (3).

This use case has been developed together with Telecom Italia, the primary network provider in Italy, and it shows how information of real word objects stored in the Web of Data can be exploited in a tourism scenario. In this case, information about tourist attractions and POIs allows users to decide how to behave, e.g. if visiting or not a particular monument.