# Making Sense of Language Signals for Monitoring Radicalization
## 3 Semantic Modeling  
By leveraging the linked data principles, any operator or program can consult the published information, make sense of it, enrich it by combining it with other semantic sources, and even reason about it using the ontologies provided. The benefits of linked data have been extensively discussed, especially in the field of linguistics [40, 41]. Multiple projects have leveraged linked data for knowledge generation, interoperability, and publication of results [42, 43]. Discussing the full breadth of benefits of semantic modeling and linked data is beyond the scope of this work. For the purpose of this paper, two things should be noted. First, a semantic endpoint will be capable of responding to arbitrary but semantically meaningful queries. One such example might be: \"where was #example hashtag twitted from on January 1 st?\" In contrast with other technologies, which would be based on strict schemas (e.g., SQL) or limited query languages (e.g., ElasticSearch or MongoDB), the linked data endpoints: (1) represent information as knowledge graphs (the RDF model), and (2) enable information access and manipulation through a set of standard web technologies (HTTP and SPARQL). Second, domain knowledge (i.e., what information can be expressed, and how it is represented in the graph) is provided by semantic vocabularies or ontologies. This section focuses on the specific vocabularies that have been used in this work. The aim is twofold: (1) to allow other researchers to better understand and query the information published; and (2) to propose a comprehensive and cohesive model for representation for this specific domain, most of which could be later reused or improved upon by researchers working in the same field. In the spirit of the linked data principles, existing vocabularies have been used when possible. For instance, the fields in a tweet are mainly modeled using the Semantically-Interlinked Online Communities (SIOC) Core Ontology, supported by some properties from Schema.org and Dublin Core Metadata Initiative (DCMI). In particular, we have reused SIOC [44] to model social media content, DCMI [45] for metadata, DBpedia [46] for linking entities mentioned in tweets, NLP Interchange Format (NIF) [47] for text annotation, Onyx [41] for emotion annotation, and Marl [48] for sentiment annotation. We have also reviewed other vocabularies, such as the radicalization ontology [49]. This ontology includes a central class _Message_ that represents social media messages. This class is linked to the user of the social network, radicalization indicators (introversion, frustration and negative feelings, negative ideas about western society, etc. ), the focus (jihadism, discrimination, Islamism, West), mentions (i.e., weapons, tactics, and terrorist organizations), and intention towards a topic, which details positive and negative emotions and negative actions. In future work, this ontology could be combined with the ontologies proposed in this article to provide a fine-grained ontology of radicalization. In fact, the ontologies proposed in this article provide a first step in the semantic characterization of psycho-social indicators and moral values. In addition to that, there were several specific types of annotations that are not modeled in any of the publicly available vocabularies to the extent of our knowledge. Therefore, this work has required the creation of new vocabulary. Once again, instead of creating a single vocabulary with all the missing elements, these pieces have been separated into smaller individual vocabularies to foster reusability. The following vocabularies have been defined:  
* Semantic LIWC (SLIWC) vocabulary [50] is a semantic taxonomy and vocabulary for annotations using the LIWC dictionary. * MFT vocabulary [51], a vocabulary to model annotations in accordance with the MFT. * Narrative vocabulary [52] contains the concepts necessary to annotate NIF or SIOC elements with a narrative component. Each vocabulary will be explained in further detail in Sections 3.1-3.3, respectively.