In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking has been performed either as dependent sequential tasks or independent parallel tasks. In this paper, we propose a framework, calledEARL, which performs entity linking and relation linking as a joint single task. EARL is modeled on an optimised variation of GeneralisedTravelling Salesperson Problem. The system determines the best semantic connection between all keywords of the question by referring to the knowledge graph. This is achieved by exploiting the connection density between entity candidates and relation candidates. We have empirically evaluated the framework on a dataset with 3000 complex questions. Our system surpasses state-of-the-art scores for entity linking task by reporting an accuracy of 0.67against 0.40 from the next best entity linker
@cite_19: This paper presents PATTY: a large resource for textual patterns that denote binary relations between entities. The patterns are semantically typed and organized into a subsumption taxonomy. The PATTY system is based on efficient algorithms for frequent itemset mining and can process Web-scale corpora. It harnesses the rich type system and entity population of large knowledge bases. The PATTY taxonomy comprises 350,569 pattern synsets. Random-sampling-based evaluation shows a pattern accuracy of 84.7 . PATTY has 8,162 subsumptions, with a random-sampling-based precision of 75 . The PATTY resource is freely available for interactive access and download.
@cite_18: 
@cite_16: Transforming natural language questions into formal queries is an integral task in Question Answering (QA) systems. QA systems built on knowledge graphs like DBpedia, require a step after natural language processing for linking words, specifically including named entities and relations, to their corresponding entities in a knowledge graph. To achieve this task, several approaches rely on background knowledge bases containing semantically-typed relations, e.g., PATTY, for an extra disambiguation step. Two major factors may affect the performance of relation linking approaches whenever background knowledge bases are accessed: a) limited availability of such semantic knowledge sources, and b) lack of a systematic approach on how to maximize the benefits of the collected knowledge. We tackle this problem and devise SIBKB, a semantic-based index able to capture knowledge encoded on background knowledge bases like PATTY. SIBKB represents a background knowledge base as a bi-partite and a dynamic index over the relation patterns included in the knowledge base. Moreover, we develop a relation linking component able to exploit SIBKB features. The benefits of SIBKB are empirically studied on existing QA benchmarks and observed results suggest that SIBKB is able to enhance the accuracy of relation linking by up to three times.
@cite_6: Research has seen considerable achievements concerning translation of natural language patterns into formal queries for Question Answering (QA) based on Knowledge Graphs (KG). One of the main challenges in this research area is about how to identify which property within a Knowledge Graph matches the predicate found in a Natural Language (NL) relation. Current approaches for formal query generation attempt to resolve this problem mainly by first retrieving the named entity from the KG together with a list of its predicates, then filtering out one from all the predicates of the entity. We attempt an approach to directly match an NL predicate to KG properties that can be employed within QA pipelines. In this paper, we specify a systematic approach as well as providing a tool that can be employed to solve this task. Our approach models KB relations with their underlying parts of speech, we then enhance this with extra attributes obtained from Wordnet and Dependency parsing characteristics. From a question, we model a similar representation of query relations. We then define distance measurements between the query relation and the properties representations from the KG to identify which property is referred to by the relation within the query. We report substantive recall values and considerable precision from our evaluation.
As relation linking is generally considered to be a problem-specific task, only a few general purpose relation linking systems are in use. Iterative bootstrapping strategies for extracting RDF resources from unstructured text have been explored in BOA @cite_18 and PATTY @cite_19 . It consists of natural language patterns corresponding to relations present in the knowledge graph. Word embedding models are also frequently used to overcome the linguistic gap for relation linking. RelMatch @cite_16 improves the accuracy of the PATTY dataset for relation linking. There are tools such as ReMatch @cite_6 which uses wordnet similarity for relation linking.