Question/Answering has become a hot top in recent years as it can be applied in a variety of domain contexts for
data mining for knowledge discovery and as an application of
natural language processing. However, one of the core underpinnings has always been about matching a question to an answer and a reformulation. In simple terms, one could apply a decision tree style approach or formalize a keyphrase matching over a set of rules. In recent years, there has been much growth towards applications of probabilistic techniques over rules-based systems. A hybrid approach in artificial intelligence has proven to be an optimal solution in many contexts. And, including semantic constructs through
ontologies allows an agent to understand and reason over domain knowledge through inference and deduction. Furthermore, one can take such an intelligent metaphor of understanding a step further into the
BDI context of
multi-agent systems and mediations for
argumentation and
game theory.
Deep Learning has also provided some robust alternatives. The below is a listing of some proposed ideas on how potentially effective question/answering strategies could be achieved for open/closed-domain understanding. In every case, a semantic ontological understanding becomes important for a somewhat guided way of reasoning about the open world. One can view
question/answering as almost like a data funnel or pipeline of question to answer matching through a series of filtration steps in form of
Sentiment Analysis,
Sentence Comprehension as form of thought chains or tokens,
Machine Learning for Classification and Clustering, as well as aspects of semantic domain concepts. In such respects, one can formulate a respective knowledge graph from a generalized view of the open world and gradually apply layers on top of specialized curated domain ontologies to provide for a
Commonsense Reasoning, analogous to a human.
DBPedia is a starting point to the open world and the entire web is another. A separate lexical store could also be used such as
wordnet,
sentiwordnet, and
wiktionary. Alternative examples to further build on the knowledgebase include:
Yago-Sumo, UMBEL,
SenticNet,
OMCS, and
ConceptNet. One could even build a graph of the various curated FAQ sites for a connected knowledge source. However, one day the
Web of Data would itself provide a gigantic linked data graph of queryable knowledge via metadata. Today such options are in form of Schema.org and others. In future as research evolves, cognitive agents will be more self-aware of their world with granular and more efficient ways of understanding without much guidance. Another aspect of practical note here is the desirability for a feedback loop between short-term and long-term retention of knowledge cues to avoid excessive repeated backtracking for inference on similar question patterns in context.
| Description Steps | Agent Belief |
| QA Semantic Domain Ontologies/NLP + BDI Multiagent Ensemble Classifiers (potential for Deep Learning) | Multiple BDI |
| QA Semantic Domain Ontologies/NLP + BDI Multiagent Belief Networks using Radial Basis Functions (Autoencoders vs Argumentation) | Multiple BDI |
| QA Semantic Domain Ontologies/NLP + BDI Multiagent Reinforcement Learning/Q Learning | Multiple BDI |
| QA Semantic Domain Ontologies/NLP + Predicate Calculus for Deductive Inference | Single |
| QA Semantic Domain Ontologies/NLP + Basic Commonsense Reasoning | Single |
| QA Semantic Domain Ontologies/NLP + Deep Learning (DBN/Autoencoders) | Single |
| QA Semantic Domain Ontologies/NLP + LDA/LSA/Search Driven | Single |
| QA Semantic Domain Ontologies/NLP + Predicate Calculus for Deductive Inference + Commonsense Reasoning | Single |
| QA Semantic Domain Ontologies/NLP + Groovy/Prolog Rules | Single |
| QA Semantic Domain Ontologies/NLP + Bayesian Networks | Single |
| QA TopicMap/NLP + DeepLearning (Recursive Neural Tensor Network) | Single |
| QA Semantic Domain Ontologies/NLP + QATopicMap + Self-Organizing Map | Single |
| QA Semantic Domain Ontologies/NLP + Connected Memory/Neuroscience (Associative Memory/HebianLearning) | Single |
| QA Semantic Domain Ontologies/NLP + Machine Learning/Clustering in a DataGrid like GridGain | Single |