Mehwish Alam

Framester: A Wide Coverage Linguistic Linked Data Hub

Semantic web applications leveraging NLP can benefit from easy access to expressive lexical resources such as FrameNet. However, the usefulness of FrameNet is affected by its limited coverage and non-standard semantics. The access to existing linguistic resources is also limited because of poor connectivity among them. We present some strategies based on Linguistic Linked Data to broaden FrameNet coverage and formal linkage of lexical and factual resources. We created a novel resource, Framester, which acts as a hub between FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, as well as other resources. Framester is not only a strongly connected knowledge graph, but also applies a rigorous formal treatment for Fillmores frame semantics, enabling full-fledged OWL querying and reasoning on a large frame-based knowledge graph. We also describe Word Frame Disambiguation, an application that reuses Framester data as a base in order to perform frame detection from text, with results comparable in precision to the state of the art, but with a much higher coverage.

Interactive exploration over RDF data using formal concept analysis

With an increased interest in machine processable data, many datasets are now published in RDF (Resource Description Framework) format in Linked Data Cloud. These data are distributed over independent resources which need to be centralized and explored for domain specific applications. This paper proposes a new approach based on interactive data exploration paradigm using Pattern Structures, an extension of Formal Concept Analysis, to provide exploration and navigation over Linked Data through concept lattices. It takes RDF triples and RDF Schema based on user requirements and provides one navigation space resulting from several RDF resources. This navigation space allows user to navigate and search only the part of data that is interesting for her.

A Proposal for Classifying the Content of the Web of Data Based on FCA and Pattern Structures

This paper focuses on a framework based on Formal Concept Analysis and the Pattern Structures for classifying sets of RDF triples. Firstly, this paper proposes a method to construct a pattern structure for the classification of RDF triples w.r.t. domain knowledge. More precisely, the poset of classes representing subjects and objects and the poset of predicates in RDF triples are taken into account. A similarity measure is also proposed based on these posets. Then, the paper discusses experimental details using a subset of DBpedia. It shows how the resulting pattern concept lattice is built and how it can be interpreted for discovering significant knowledge units from the obtained classes of RDF triples.

Event-based knowledge reconciliation using frame embeddings and frame similarity

Abstract This paper proposes an evolution over MERGILO, a tool for reconciling knowledge graphs extracted from text, using graph alignment and word similarity. The reconciled knowledge graphs are typically used for multi-document summarization, or to detect knowledge evolution across document series. The main point of improvement focuses on event reconciliation i.e., reconciling knowledge graphs generated by text about two similar events described differently. In order to gather a complete semantic representation of events, we use FRED semantic web machine reader, jointly with Framester, a linguistic linked data hub represented using a novel formal semantics for frames. Framester is used to enhance the extracted event knowledge with semantic frames. We extend MERGILO with similarities based on the graph structure of semantic frames and the subsumption hierarchy of semantic roles as defined in Framester. With an effective evaluation strategy similarly as used for MERGILO, we show the improvement of the new approach (MERGILO plus semantic frame/role similarities) over the baseline.