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Section: New Results

Knowledge Engineering and Web of Data

Participants : Nicolas Jay, Florence Le Ber, Jean Lieber, Amedeo Napoli, Emmanuel Nauer, Justine Reynaud, Yannick Toussaint.


knowledge engineering, web of data, definition mining, classification-based reasoning, case-based reasoning, belief revision, semantic web

Current Trends in Case-Based Reasoning

Case-based reasoning (CBR) aims at solving a new problem, called the target problem, by exploiting past experiences (i.e. source cases) as well as other knowledge sources: domain knowledge, similarity knowledge and adaptation knowledge.

Two research works were carried out about how exploiting at the best the source cases. A first work addresses the exploitation of negative cases for adaptation knowledge discovery. Usually CBR exploits positive source cases consisting of a source problem and its solution that is known to be correct for the problem. However, negative cases, i.e. problem-solution pairs where the solution is an incorrect answer to the problem, which can be acquired when CBR process fails, are useful, especially for adaptation knowledge discovery. In [29], we propose an adaptation knowledge discovery approach exploiting both type of cases (positive and negatives cases), using closed itemsets built on variations between cases. Experiments show that exploiting negative cases in addition to positive ones improves the quality of the adaptation knowledge being extracted and, so, improves the results of the CBR system.

A second work addresses the issue of the selection of source cases used to solve a target problem. Three approaches have been studied to better exploit source cases: (1) approximation, which considers the use of one source case (the most similar to the target problem) to solve the target problem, (2) interpolation, which considers the use of two source cases (such as the target problem is between these two similar source problems), and (3) extrapolation, which considers the use of three source cases, linked to the target problem by an analogical proportion, where the analogical proportion handles both similarity and dissimilarity between cases. Experiments show that interpolation and extrapolation techniques are of interest for reusing cases, either in an independent or in a combined way [36], [47].

Using analogical proportion has also been used to find relevant pathology-gene pairs [28]. This first study to infer pathology-gene relation is based on the following hypothesis: if a target pathology is in analogy with three other pathologies for which associated genes are known, then it is plausible that the gene to be associated with the target pathology is in analogy with the genes associated to the three pathologies involved in the analogical proportion.

Another use of analogical proportion is its application to machine translation and is based on a similar principle: if four sentences form an analogical proportion in a language, then it is plausible that their translations in another language also form an analogical proportion. This was the idea developed by Yves Lepage (Waseda University), a few years ago. Now, a starting work on case-based machine translation aims at developing these ideas by incorporation other knowledge sources to the CBR system than the cases (domain knowledge, retrieval knowledge and adaptation knowledge) [35].

Another work on CBR is its application to medical coding. Cancer registries are important tools in the fight against cancer. At the heart of these registries is the data collection and coding process. Ruled by complex international standards and numerous best practices, operators are easily overwhelmed. In [54], [55], a system is presented to assist operators in the interpretation of best medical coding practices.

There has been another work on CBR related to an application in agronomy developed some time ago that has been synthesized in [60].

Exploring and Classifying the Web of Data

A part of the research work in Knowledge Engineering is oriented towards knowledge discovery in the web of data, following the increase of data published in RDF (Resource Description Framework) format and the interest in machine processable data. The quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud. In the team, we are particularly interested in the completeness of the data viewed as their their potential to provide concept definitions in terms of necessary and sufficient conditions [69]. We have proposed a novel technique based on Formal Concept Analysis which classifies subsets of RDF data into a concept lattice [83]. This allows data exploration as well as the discovery of implication rules which are used to automatically detect possible completions of RDF data and to provide definitions. Moreover, this is a way of reconciling syntax and semantics in the LOD cloud. Experiments on the DBpedia knowledge base shows that this kind of approach is well-founded and effective [44].

In the same way, FCA can be used to improve ontologies associated with the Web of data. Accordingly, we proposed a method to build a concept lattice from linked data and compare the structure of this lattice with an ontology used to type the considered data. The result of this comparison makes clear some alternative axioms to be proposed to ontology developers. We extended and reused this work in ontology alignment tasks [41].