# Linked Data Triples Enhance Document Relevance Classification
## 2 Related Work  
There is a substantial research related to enhancing document representation using external source of data. In Figure 1, we summarize the main techniques used for that goal, namely word embeddings and ontology- or knowledge-based approaches. Feature extraction plays an important role in representing documents as vectors in document classification. Initially, approaches used a bag-of-words model with term frequencies and inverse document frequencies as features. There have been numerous advances in recent years achieving more effective representations of textual documents. Word embeddings, such as Word2 vec [8], GloVe [9], and fastText [10], provide a fixed low-dimensional vector representation for every representative term within a document. Sentence embeddings, like InferSent [11] and Universal Sentence Encoder [12] (USE), provide fixed vector representations for every sentence in the document. The introduction of language-models, such as ELMo (Embeddings from Language Models) [13] and Bi-directional Encoder Representation of Transformers [14], has resulted in further advances, by enhancing the contextualized distributed of the representations of terms. Enriching document representations using semantic features, as in this work, is aimed at improving relevance classification performance. For instance, in Reference [15], enhanced document representations are formed by embedding background knowledge from a relevant domain ontology. The terms in documents are matched against the concepts in the ontology. If an exact/partial match is found, the terms are replaced by the associated concepts forming a richer semantic representation of the document. In Reference [16], document related concepts are fetched from the domain ontology forming a CF-IDF (Concept Frequency-Inverse Document Frequency) to classify news-feeds. In Reference [17], news documents are clustered by measuring similarity between named entities from a document and related matched information from DBPedia ([[https://www.dbpedia.org/](https://www.dbpedia.org/)]([https://www.dbpedia.org/](https://www.dbpedia.org/)), accessed on 10 May 2021.) A Semantic Enriched Deep Learning Architecture (SE-DLA) is proposed in Reference [18], this approach uses semantic vectors from WordNet to represent documents. WordNet [19] is a widely used resource containing taxonomy of words with their semantic relations. These semantic vectors are fused with general word-embeddings to form a vector space representation which is then fed into a neural network to obtain output relevance classes. In Reference [20], semantic enrichment is performed with external knowledge bases (KBs), for short text classification. Relevant concepts are retrieved from knowledge-bases, such as YAGO [21] and Freebase [22], and embedded into the model via concatenation. Documents belonging to a particular class are often linked together, hence providing better representation. Considering this assumption, a new document embedding method is proposed in Reference [23] using the hyperlinks and relations between documents for classification. Open domain knowledge bases are less useful in complex domains, like medical document classification [24]. Incorporating domain knowledge with relevant closed-domain ontologies are used in these situations to enrich document representations. However, the main weakness of these approaches is the reliance on external ontologies to improve the semantic richness of the document. The selection of ontology is domain-specific and needs to be relevant to the corpus. Open domain knowledge-bases may not be an ideal solution as they suffer incompleteness [20]. In contrast, our work utilizes linked-data triples (formed using natural language  
Figure 1: A taxonomy of document representation enhancement techniques. features--abstract topics and named entities) generated entirely from within the corpus itself, without recourse to external ontologies or knowledge sources.