Knowledge Gathering from Social Media to Improve Marketing in Agri-food Sector

none5noNowadays many small and medium companies are interested in entering into foreign markets to establish a brand presence, sell their products and beat the competitors. Before making such a marketing decision, marketing experts can be guided by the traditional analysis of reports but also by the Web, through the analysis of social networks, blogs, forums, etc. These sources can provide real-time information about the perception that users have of specific brands and products. As a result, there are several tools that can extract interesting information from these unstructured data. In this paper, we propose an innovative knowledge extraction architecture realized through the integration of some existing tools. The aim is to retrieve the more frequent concepts from unstructured sources, suggest other links of articles and images, with multi-language feature so that the research is language independent. The architecture provides a knowledge base of a specific domain, which is used to suggest concepts related to the research, and to filter the results obtained from the elaboration of the unstructured sources. We present a case of study related to marketing in agri-food sector, in order to illustrate how the software works, the results obtained, their interpretation and the managerial implications.Caione, Adriana; Paiano, Roberto; Guido, Anna Lisa; Fait, Monica; Scorrano, PaolaCaione, Adriana; Paiano, Roberto; Guido, ANNA LISA; Fait, MONICA MARIA ELENA; Scorrano, Paol

Rivière or Fleuve? Modelling Multilinguality in the Hydrographical

The need for interoperability among geospatial resources in different natural languages evidences the difficulties to cope with domain representations highly dependent of the culture in which they have been conceived. In this paper we characterize the problem of representing cultural discrepancies in ontologies. We argue that such differences can be accounted for at the ontology terminological layer by means of external elaborated models of linguistic information associated to ontologies. With the aim of showing how external models can cater for cultural discrepancies, we compare two versions of an ontology of the hydrographical domain: hydrOntology. The first version makes use of the labeling system supported by RDF(S) and OWL to include multilingual linguistic information in the ontology. The second version relies on the Linguistic Information Repository model (LIR) to associate structured multilingual information to ontology concepts. In this paper we propose an extension to the LIR to better capture linguistic and cultural specificities within and across language

Statistical Extraction of Multilingual Natural Language Patterns for RDF Predicates: Algorithms and Applications

The Data Web has undergone a tremendous growth period. It currently consists of more then 3300 publicly available knowledge bases describing millions of resources from various domains, such as life sciences, government or geography, with over 89 billion facts. In the same way, the Document Web grew to the state where approximately 4.55 billion websites exist, 300 million photos are uploaded on Facebook as well as 3.5 billion Google searches are performed on average every day. However, there is a gap between the Document Web and the Data Web, since for example knowledge bases available on the Data Web are most commonly extracted from structured or semi-structured sources, but the majority of information available on the Web is contained in unstructured sources such as news articles, blog post, photos, forum discussions, etc. As a result, data on the Data Web not only misses a significant fragment of information but also suffers from a lack of actuality since typical extraction methods are time-consuming and can only be carried out periodically. Furthermore, provenance information is rarely taken into consideration and therefore gets lost in the transformation process. In addition, users are accustomed to entering keyword queries to satisfy their information needs. With the availability of machine-readable knowledge bases, lay users could be empowered to issue more specific questions and get more precise answers. In this thesis, we address the problem of Relation Extraction, one of the key challenges pertaining to closing the gap between the Document Web and the Data Web by four means. First, we present a distant supervision approach that allows finding multilingual natural language representations of formal relations already contained in the Data Web. We use these natural language representations to find sentences on the Document Web that contain unseen instances of this relation between two entities. Second, we address the problem of data actuality by presenting a real-time data stream RDF extraction framework and utilize this framework to extract RDF from RSS news feeds. Third, we present a novel fact validation algorithm, based on natural language representations, able to not only verify or falsify a given triple, but also to find trustworthy sources for it on the Web and estimating a time scope in which the triple holds true. The features used by this algorithm to determine if a website is indeed trustworthy are used as provenance information and therewith help to create metadata for facts in the Data Web. Finally, we present a question answering system that uses the natural language representations to map natural language question to formal SPARQL queries, allowing lay users to make use of the large amounts of data available on the Data Web to satisfy their information need

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

A decade of Semantic Web research through the lenses of a mixed methods approach

The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up approaches have been proposed that offer a quantitative analysis of the evolution of a research domain. In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows for the combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (Rexplore, Saffron, PoolParty), on a corpus of Semantic Web papers published between 2006 and 2015. In this process, we both use the latter for “fact-checking” on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom up approaches to research topic identification. Although we provide a detailed study on the past decade of Semantic Web research, the findings and the methodology are relevant not only for our community but beyond the area of the Semantic Web to other research fields as well

Linked Data Meets Big Data: A Knowledge Organization Systems Perspective

The objective of this paper is a) to provide a conceptualanalysis of the term big data and b) to introduce linked dataapplications such as SKOS-based knowledge organizationsystems as new tools for the analysis, organization, representation, visualization and access to big data